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100 Great Computer Science Research Topics Ideas for 2023

Computer science research paper topics

Being a computer student in 2023 is not easy. Besides studying a constantly evolving subject, you have to come up with great computer science research topics at some point in your academic life. If you’re reading this article, you’re among many other students that have also come to this realization.

Interesting Computer Science Topics

Awesome research topics in computer science, hot topics in computer science, topics to publish a journal on computer science.

  • Controversial Topics in Computer Science

Fun AP Computer Science Topics

Exciting computer science ph.d. topics, remarkable computer science research topics for undergraduates, incredible final year computer science project topics, advanced computer science topics, unique seminars topics for computer science, exceptional computer science masters thesis topics, outstanding computer science presentation topics.

  • Key Computer Science Essay Topics

Main Project Topics for Computer Science

  • We Can Help You with Computer Science Topics

Whether you’re earnestly searching for a topic or stumbled onto this article by accident, there is no doubt that every student needs excellent computer science-related topics for their paper. A good topic will not only give your essay or research a good direction but will also make it easy to come up with supporting points. Your topic should show all your strengths as well.

Fortunately, this article is for every student that finds it hard to generate a suitable computer science topic. The following 100+ topics will help give you some inspiration when creating your topics. Let’s get into it.

One of the best ways of making your research paper interesting is by coming up with relevant topics in computer science . Here are some topics that will make your paper immersive:

  • Evolution of virtual reality
  • What is green cloud computing
  • Ways of creating a Hopefield neural network in C++
  • Developments in graphic systems in computers
  • The five principal fields in robotics
  • Developments and applications of nanotechnology
  • Differences between computer science and applied computing

Your next research topic in computer science shouldn’t be tough to find once you’ve read this section. If you’re looking for simple final year project topics in computer science, you can find some below.

  • Applications of the blockchain technology in the banking industry
  • Computational thinking and how it influences science
  • Ways of terminating phishing
  • Uses of artificial intelligence in cyber security
  • Define the concepts of a smart city
  • Applications of the Internet of Things
  • Discuss the applications of the face detection application

Whenever a topic is described as “hot,” it means that it is a trendy topic in computer science. If computer science project topics for your final years are what you’re looking for, have a look at some below:

  • Applications of the Metaverse in the world today
  • Discuss the challenges of machine learning
  • Advantages of artificial intelligence
  • Applications of nanotechnology in the paints industry
  • What is quantum computing?
  • Discuss the languages of parallel computing
  • What are the applications of computer-assisted studies?

Perhaps you’d like to write a paper that will get published in a journal. If you’re searching for the best project topics for computer science students that will stand out in a journal, check below:

  • Developments in human-computer interaction
  • Applications of computer science in medicine
  • Developments in artificial intelligence in image processing
  • Discuss cryptography and its applications
  • Discuss methods of ransomware prevention
  • Applications of Big Data in the banking industry
  • Challenges of cloud storage services in 2023

 Controversial Topics in Computer Science

Some of the best computer science final year project topics are those that elicit debates or require you to take a stand. You can find such topics listed below for your inspiration:

  • Can robots be too intelligent?
  • Should the dark web be shut down?
  • Should your data be sold to corporations?
  • Will robots completely replace the human workforce one day?
  • How safe is the Metaverse for children?
  • Will artificial intelligence replace actors in Hollywood?
  • Are social media platforms safe anymore?

Are you a computer science student looking for AP topics? You’re in luck because the following final year project topics for computer science are suitable for you.

  • Standard browser core with CSS support
  • Applications of the Gaussian method in C++ development in integrating functions
  • Vital conditions of reducing risk through the Newton method
  • How to reinforce machine learning algorithms.
  • How do artificial neural networks function?
  • Discuss the advancements in computer languages in machine learning
  • Use of artificial intelligence in automated cars

When studying to get your doctorate in computer science, you need clear and relevant topics that generate the reader’s interest. Here are some Ph.D. topics in computer science you might consider:

  • Developments in information technology
  • Is machine learning detrimental to the human workforce?
  • How to write an algorithm for deep learning
  • What is the future of 5G in wireless networks
  • Statistical data in Maths modules in Python
  • Data retention automation from a website using API
  • Application of modern programming languages

Looking for computer science topics for research is not easy for an undergraduate. Fortunately, these computer science project topics should make your research paper easy:

  • Ways of using artificial intelligence in real estate
  • Discuss reinforcement learning and its applications
  • Uses of Big Data in science and medicine
  • How to sort algorithms using Haskell
  • How to create 3D configurations for a website
  • Using inverse interpolation to solve non-linear equations
  • Explain the similarities between the Internet of Things and artificial intelligence

Your dissertation paper is one of the most crucial papers you’ll ever do in your final year. That’s why selecting the best ethics in computer science topics is a crucial part of your paper. Here are some project topics for the computer science final year.

  • How to incorporate numerical methods in programming
  • Applications of blockchain technology in cloud storage
  • How to come up with an automated attendance system
  • Using dynamic libraries for site development
  • How to create cubic splines
  • Applications of artificial intelligence in the stock market
  • Uses of quantum computing in financial modeling

Your instructor may want you to challenge yourself with an advanced science project. Thus, you may require computer science topics to learn and research. Here are some that may inspire you:

  • Discuss the best cryptographic protocols
  • Advancement of artificial intelligence used in smartphones
  • Briefly discuss the types of security software available
  • Application of liquid robots in 2023
  • How to use quantum computers to solve decoherence problem
  • macOS vs. Windows; discuss their similarities and differences
  • Explain the steps taken in a cyber security audit

When searching for computer science topics for a seminar, make sure they are based on current research or events. Below are some of the latest research topics in computer science:

  • How to reduce cyber-attacks in 2023
  • Steps followed in creating a network
  • Discuss the uses of data science
  • Discuss ways in which social robots improve human interactions
  • Differentiate between supervised and unsupervised machine learning
  • Applications of robotics in space exploration
  • The contrast between cyber-physical and sensor network systems

Are you looking for computer science thesis topics for your upcoming projects? The topics below are meant to help you write your best paper yet:

  • Applications of computer science in sports
  • Uses of computer technology in the electoral process
  • Using Fibonacci to solve the functions maximum and their implementations
  • Discuss the advantages of using open-source software
  • Expound on the advancement of computer graphics
  • Briefly discuss the uses of mesh generation in computational domains
  • How much data is generated from the internet of things?

A computer science presentation requires a topic relevant to current events. Whether your paper is an assignment or a dissertation, you can find your final year computer science project topics below:

  • Uses of adaptive learning in the financial industry
  • Applications of transitive closure on graph
  • Using RAD technology in developing software
  • Discuss how to create maximum flow in the network
  • How to design and implement functional mapping
  • Using artificial intelligence in courier tracking and deliveries
  • How to make an e-authentication system

 Key Computer Science Essay Topics

You may be pressed for time and require computer science master thesis topics that are easy. Below are some topics that fit this description:

  • What are the uses of cloud computing in 2023
  • Discuss the server-side web technologies
  • Compare and contrast android and iOS
  • How to come up with a face detection algorithm
  • What is the future of NFTs
  • How to create an artificial intelligence shopping system
  • How to make a software piracy prevention algorithm

One major mistake students make when writing their papers is selecting topics unrelated to the study at hand. This, however, will not be an issue if you get topics related to computer science, such as the ones below:

  • Using blockchain to create a supply chain management system
  • How to protect a web app from malicious attacks
  • Uses of distributed information processing systems
  • Advancement of crowd communication software since COVID-19
  • Uses of artificial intelligence in online casinos
  • Discuss the pillars of math computations
  • Discuss the ethical concerns arising from data mining

We Can Help You with Computer Science Topics, Essays, Thesis, and Research Papers

We hope that this list of computer science topics helps you out of your sticky situation. We do offer other topics in different subjects. Additionally, we also offer professional writing services tailor-made for you.

We understand what students go through when searching the internet for computer science research paper topics, and we know that many students don’t know how to write a research paper to perfection. However, you shouldn’t have to go through all this when we’re here to help.

Don’t waste any more time; get in touch with us today and get your paper done excellently.

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Latest Computer Science Research Topics for 2023

Home Blog Programming Latest Computer Science Research Topics for 2023

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Everybody sees a dream—aspiring to become a doctor, astronaut, or anything that fits your imagination. If you were someone who had a keen interest in looking for answers and knowing the “why” behind things, you might be a good fit for research. Further, if this interest revolved around computers and tech, you would be an excellent computer researcher!

As a tech enthusiast, you must know how technology is making our life easy and comfortable. With a single click, Google can get you answers to your silliest query or let you know the best restaurants around you. Do you know what generates that answer? Want to learn about the science going on behind these gadgets and the internet?

For this, you will have to do a bit of research. Here we will learn about top computer science thesis topics and computer science thesis ideas.

Why is Research in Computer Science Important?

Computers and technology are becoming an integral part of our lives. We are dependent on them for most of our work. With the changing lifestyle and needs of the people, continuous research in this sector is required to ease human work. However, you need to be a certified researcher to contribute to the field of computers. You can check out Advance Computer Programming certification to learn and advance in the versatile language and get hands-on experience with all the topics of C# application development.

1. Innovation in Technology

Research in computer science contributes to technological advancement and innovations. We end up discovering new things and introducing them to the world. Through research, scientists and engineers can create new hardware, software, and algorithms that improve the functionality, performance, and usability of computers and other digital devices.

2. Problem-Solving Capabilities

From disease outbreaks to climate change, solving complex problems requires the use of advanced computer models and algorithms. Computer science research enables scholars to create methods and tools that can help in resolving these challenging issues in a blink of an eye.

3. Enhancing Human Life

Computer science research has the potential to significantly enhance human life in a variety of ways. For instance, researchers can produce educational software that enhances student learning or new healthcare technology that improves clinical results. If you wish to do Ph.D., these can become interesting computer science research topics for a PhD.

4. Security Assurance

As more sensitive data is being transmitted and kept online, security is our main concern. Computer science research is crucial for creating new security systems and tactics that defend against online threats.

Top Computer Science Research Topics

Before starting with the research, knowing the trendy research paper ideas for computer science exploration is important. It is not so easy to get your hands on the best research topics for computer science; spend some time and read about the following mind-boggling ideas before selecting one.

1. Integrated Blockchain and Edge Computing Systems: A Survey, Some Research Issues, and Challenges

Welcome to the era of seamless connectivity and unparalleled efficiency! Blockchain and edge computing are two cutting-edge technologies that have the potential to revolutionize numerous sectors. Blockchain is a distributed ledger technology that is decentralized and offers a safe and transparent method of storing and transferring data.

As a young researcher, you can pave the way for a more secure, efficient, and scalable architecture that integrates blockchain and edge computing systems. So, let's roll up our sleeves and get ready to push the boundaries of technology with this exciting innovation!

Blockchain helps to reduce latency and boost speed. Edge computing, on the other hand, entails processing data close to the generation source, such as sensors and IoT devices. Integrating edge computing with blockchain technologies can help to achieve safer, more effective, and scalable architecture.

Moreover, this research title for computer science might open doors of opportunities for you in the financial sector.

2. A Survey on Edge Computing Systems and Tools

With the rise in population, the data is multiplying by manifolds each day. It's high time we find efficient technology to store it. However, more research is required for the same.

Say hello to the future of computing with edge computing! The edge computing system can store vast amounts of data to retrieve in the future. It also provides fast access to information in need. It maintains computing resources from the cloud and data centers while processing.

Edge computing systems bring processing power closer to the data source, resulting in faster and more efficient computing. But what tools are available to help us harness the power of edge computing?

As a part of this research, you will look at the newest edge computing tools and technologies to see how they can improve your computing experience. Here are some of the tools you might get familiar with upon completion of this research:

  • Apache NiFi:  A framework for data processing that enables users to gather, transform, and transfer data from edge devices to cloud computing infrastructure.
  • Microsoft Azure IoT Edge: A platform in the cloud that enables the creation and deployment of cutting-edge intelligent applications.
  • OpenFog Consortium:  An organization that supports the advancement of fog computing technologies and architectures is the OpenFog Consortium.

3. Machine Learning: Algorithms, Real-world Applications, and Research Directions

Machine learning is the superset of Artificial Intelligence; a ground-breaking technology used to train machines to mimic human action and work. ML is used in everything from virtual assistants to self-driving cars and is revolutionizing the way we interact with computers. But what is machine learning exactly, and what are some of its practical uses and future research directions?

To find answers to such questions, it can be a wonderful choice to pick from the pool of various computer science dissertation ideas.

You will discover how computers learn several actions without explicit programming and see how they perform beyond their current capabilities. However, to understand better, having some basic programming knowledge always helps. KnowledgeHut’s Programming course for beginners will help you learn the most in-demand programming languages and technologies with hands-on projects.

During the research, you will work on and study

  • Algorithm: Machine learning includes many algorithms, from decision trees to neural networks.
  • Applications in the Real-world: You can see the usage of ML in many places. It can early detect and diagnose diseases like cancer. It can detect fraud when you are making payments. You can also use it for personalized advertising.
  • Research Trend:  The most recent developments in machine learning research, include explainable AI, reinforcement learning, and federated learning.

While a single research paper is not enough to bring the light on an entire domain as vast as machine learning; it can help you witness how applicable it is in numerous fields, like engineering, data science & analysis, business intelligence, and many more.

Whether you are a data scientist with years of experience or a curious tech enthusiast, machine learning is an intriguing and vital field that's influencing the direction of technology. So why not dig deeper?

4. Evolutionary Algorithms and their Applications to Engineering Problems

Imagine a system that can solve most of your complex queries. Are you interested to know how these systems work? It is because of some algorithms. But what are they, and how do they work? Evolutionary algorithms use genetic operators like mutation and crossover to build new generations of solutions rather than starting from scratch.

This research topic can be a choice of interest for someone who wants to learn more about algorithms and their vitality in engineering.

Evolutionary algorithms are transforming the way we approach engineering challenges by allowing us to explore enormous solution areas and optimize complex systems.

The possibilities are infinite as long as this technology is developed further. Get ready to explore the fascinating world of evolutionary algorithms and their applications in addressing engineering issues.

5. The Role of Big Data Analytics in the Industrial Internet of Things

Datasets can have answers to most of your questions. With good research and approach, analyzing this data can bring magical results. Welcome to the world of data-driven insights! Big Data Analytics is the transformative process of extracting valuable knowledge and patterns from vast and complex datasets, boosting innovation and informed decision-making.

This field allows you to transform the enormous amounts of data produced by IoT devices into insightful knowledge that has the potential to change how large-scale industries work. It's like having a crystal ball that can foretell.

Big data analytics is being utilized to address some of the most critical issues, from supply chain optimization to predictive maintenance. Using it, you can find patterns, spot abnormalities, and make data-driven decisions that increase effectiveness and lower costs for several industrial operations by analyzing data from sensors and other IoT devices.

The area is so vast that you'll need proper research to use and interpret all this information. Choose this as your computer research topic to discover big data analytics' most compelling applications and benefits. You will see that a significant portion of industrial IoT technology demands the study of interconnected systems, and there's nothing more suitable than extensive data analysis.

6. An Efficient Lightweight Integrated Blockchain (ELIB) Model for IoT Security and Privacy

Are you concerned about the security and privacy of your Internet of Things (IoT) devices? As more and more devices become connected, it is more important than ever to protect the security and privacy of data. If you are interested in cyber security and want to find new ways of strengthening it, this is the field for you.

ELIB is a cutting-edge solution that offers private and secure communication between IoT devices by fusing the strength of blockchain with lightweight cryptography. This architecture stores encrypted data on a distributed ledger so only parties with permission can access it.

But why is ELIB so practical and portable? ELIB uses lightweight cryptography to provide quick and effective communication between devices, unlike conventional blockchain models that need complicated and resource-intensive computations.

Due to its increasing vitality, it is gaining popularity as a research topic as someone aware that this framework works and helps reinstate data security is highly demanded in financial and banking.

7. Natural Language Processing Techniques to Reveal Human-Computer Interaction for Development Research Topics

Welcome to the world where machines decode the beauty of the human language. With natural language processing (NLP) techniques, we can analyze the interactions between humans and computers to reveal valuable insights for development research topics. It is also one of the most crucial PhD topics in computer science as NLP-based applications are gaining more and more traction.

Etymologically, natural language processing (NLP) is a potential technique that enables us to examine and comprehend natural language data, such as discussions between people and machines. Insights on user behaviour, preferences, and pain areas can be gleaned from these encounters utilizing NLP approaches.

But which specific areas should we leverage on using NLP methods? This is precisely what you’ll discover while doing this computer science research.

Gear up to learn more about the fascinating field of NLP and how it can change how we design and interact with technology, whether you are a UX designer, a data scientist, or just a curious tech lover and linguist.

8. All One Needs to Know About Fog Computing and Related Edge Computing Paradigms: A Complete Survey

If you are an IoT expert or a keen lover of the Internet of Things, you should leap and move forward to discovering Fog Computing. With the rise of connected devices and the Internet of Things (IoT), traditional cloud computing models are no longer enough. That's where fog computing and related edge computing paradigms come in.

Fog computing is a distributed approach that brings processing and data storage closer to the devices that generate and consume data by extending cloud computing to the network's edge.

As computing technologies are significantly used today, the area has become a hub for researchers to delve deeper into the underlying concepts and devise more and more fog computing frameworks. You can also contribute to and master this architecture by opting for this stand-out topic for your research.

Tips and Tricks to Write Computer Research Topics

Before starting to explore these hot research topics in computer science you may have to know about some tips and tricks that can easily help you.

  • Know your interest.
  • Choose the topic wisely.
  • Make proper research about the demand of the topic.
  • Get proper references.
  • Discuss with experts.

By following these tips and tricks, you can write a compelling and impactful computer research topic that contributes to the field's advancement and addresses important research gaps.

From machine learning and artificial intelligence to blockchain, edge computing, and big data analytics, numerous trending computer research topics exist to explore.

One of the most important trends is using cutting-edge technology to address current issues. For instance, new IIoT security and privacy opportunities are emerging by integrating blockchain and edge computing. Similarly, the application of natural language processing methods is assisting in revealing human-computer interaction and guiding the creation of new technologies.

Another trend is the growing emphasis on sustainability and moral considerations in technological development. Researchers are looking into how computer science might help in innovation.

With the latest developments and leveraging cutting-edge tools and techniques, researchers can make meaningful contributions to the field and help shape the future of technology. Going for Full-stack Developer online training will help you master the latest tools and technologies. 

Frequently Asked Questions (FAQs)

Research in computer science is mainly focused on different niches. It can be theoretical or technical as well. It completely depends upon the candidate and his focused area. They may do research for inventing new algorithms or many more to get advanced responses in that field.  

Yes, moreover it would be a very good opportunity for the candidate. Because computer science students may have a piece of knowledge about the topic previously. They may find Easy thesis topics for computer science to fulfill their research through KnowledgeHut. 

 There are several scopes available for computer science. A candidate can choose different subjects such as AI, database management, software design, graphics, and many more. 


Zeshan Naz holds 6 years of work experience in Content Marketing. EdTech is her field of expertise and she looks forward to helping more professionals get ahead in their careers. Zeshan is an avid reader and in her leisure time, loves traveling around and exploring places.

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Research Topics & Ideas: CompSci & IT

50+ Computer Science Research Topic Ideas To Fast-Track Your Project

IT & Computer Science Research Topics

Finding and choosing a strong research topic is the critical first step when it comes to crafting a high-quality dissertation, thesis or research project. If you’ve landed on this post, chances are you’re looking for a computer science-related research topic , but aren’t sure where to start. Here, we’ll explore a variety of CompSci & IT-related research ideas and topic thought-starters, including algorithms, AI, networking, database systems, UX, information security and software engineering.

NB – This is just the start…

The topic ideation and evaluation process has multiple steps . In this post, we’ll kickstart the process by sharing some research topic ideas within the CompSci domain. This is the starting point, but to develop a well-defined research topic, you’ll need to identify a clear and convincing research gap , along with a well-justified plan of action to fill that gap.

If you’re new to the oftentimes perplexing world of research, or if this is your first time undertaking a formal academic research project, be sure to check out our free dissertation mini-course. In it, we cover the process of writing a dissertation or thesis from start to end. Be sure to also sign up for our free webinar that explores how to find a high-quality research topic. 

Overview: CompSci Research Topics

  • Algorithms & data structures
  • Artificial intelligence ( AI )
  • Computer networking
  • Database systems
  • Human-computer interaction
  • Information security (IS)
  • Software engineering
  • Examples of CompSci dissertation & theses

Topics/Ideas: Algorithms & Data Structures

  • An analysis of neural network algorithms’ accuracy for processing consumer purchase patterns
  • A systematic review of the impact of graph algorithms on data analysis and discovery in social media network analysis
  • An evaluation of machine learning algorithms used for recommender systems in streaming services
  • A review of approximation algorithm approaches for solving NP-hard problems
  • An analysis of parallel algorithms for high-performance computing of genomic data
  • The influence of data structures on optimal algorithm design and performance in Fintech
  • A Survey of algorithms applied in internet of things (IoT) systems in supply-chain management
  • A comparison of streaming algorithm performance for the detection of elephant flows
  • A systematic review and evaluation of machine learning algorithms used in facial pattern recognition
  • Exploring the performance of a decision tree-based approach for optimizing stock purchase decisions
  • Assessing the importance of complete and representative training datasets in Agricultural machine learning based decision making.
  • A Comparison of Deep learning algorithms performance for structured and unstructured datasets with “rare cases”
  • A systematic review of noise reduction best practices for machine learning algorithms in geoinformatics.
  • Exploring the feasibility of applying information theory to feature extraction in retail datasets.
  • Assessing the use case of neural network algorithms for image analysis in biodiversity assessment

Topics & Ideas: Artificial Intelligence (AI)

  • Applying deep learning algorithms for speech recognition in speech-impaired children
  • A review of the impact of artificial intelligence on decision-making processes in stock valuation
  • An evaluation of reinforcement learning algorithms used in the production of video games
  • An exploration of key developments in natural language processing and how they impacted the evolution of Chabots.
  • An analysis of the ethical and social implications of artificial intelligence-based automated marking
  • The influence of large-scale GIS datasets on artificial intelligence and machine learning developments
  • An examination of the use of artificial intelligence in orthopaedic surgery
  • The impact of explainable artificial intelligence (XAI) on transparency and trust in supply chain management
  • An evaluation of the role of artificial intelligence in financial forecasting and risk management in cryptocurrency
  • A meta-analysis of deep learning algorithm performance in predicting and cyber attacks in schools

Research topic evaluator

Topics & Ideas: Networking

  • An analysis of the impact of 5G technology on internet penetration in rural Tanzania
  • Assessing the role of software-defined networking (SDN) in modern cloud-based computing
  • A critical analysis of network security and privacy concerns associated with Industry 4.0 investment in healthcare.
  • Exploring the influence of cloud computing on security risks in fintech.
  • An examination of the use of network function virtualization (NFV) in telecom networks in Southern America
  • Assessing the impact of edge computing on network architecture and design in IoT-based manufacturing
  • An evaluation of the challenges and opportunities in 6G wireless network adoption
  • The role of network congestion control algorithms in improving network performance on streaming platforms
  • An analysis of network coding-based approaches for data security
  • Assessing the impact of network topology on network performance and reliability in IoT-based workspaces

Webinar - How to find a research topic

Topics & Ideas: Database Systems

  • An analysis of big data management systems and technologies used in B2B marketing
  • The impact of NoSQL databases on data management and analysis in smart cities
  • An evaluation of the security and privacy concerns of cloud-based databases in financial organisations
  • Exploring the role of data warehousing and business intelligence in global consultancies
  • An analysis of the use of graph databases for data modelling and analysis in recommendation systems
  • The influence of the Internet of Things (IoT) on database design and management in the retail grocery industry
  • An examination of the challenges and opportunities of distributed databases in supply chain management
  • Assessing the impact of data compression algorithms on database performance and scalability in cloud computing
  • An evaluation of the use of in-memory databases for real-time data processing in patient monitoring
  • Comparing the effects of database tuning and optimization approaches in improving database performance and efficiency in omnichannel retailing

Topics & Ideas: Human-Computer Interaction

  • An analysis of the impact of mobile technology on human-computer interaction prevalence in adolescent men
  • An exploration of how artificial intelligence is changing human-computer interaction patterns in children
  • An evaluation of the usability and accessibility of web-based systems for CRM in the fast fashion retail sector
  • Assessing the influence of virtual and augmented reality on consumer purchasing patterns
  • An examination of the use of gesture-based interfaces in architecture
  • Exploring the impact of ease of use in wearable technology on geriatric user
  • Evaluating the ramifications of gamification in the Metaverse
  • A systematic review of user experience (UX) design advances associated with Augmented Reality
  • A comparison of natural language processing algorithms automation of customer response Comparing end-user perceptions of natural language processing algorithms for automated customer response
  • Analysing the impact of voice-based interfaces on purchase practices in the fast food industry

Research Topic Kickstarter - Need Help Finding A Research Topic?

Topics & Ideas: Information Security

  • A bibliometric review of current trends in cryptography for secure communication
  • An analysis of secure multi-party computation protocols and their applications in cloud-based computing
  • An investigation of the security of blockchain technology in patient health record tracking
  • A comparative study of symmetric and asymmetric encryption algorithms for instant text messaging
  • A systematic review of secure data storage solutions used for cloud computing in the fintech industry
  • An analysis of intrusion detection and prevention systems used in the healthcare sector
  • Assessing security best practices for IoT devices in political offices
  • An investigation into the role social media played in shifting regulations related to privacy and the protection of personal data
  • A comparative study of digital signature schemes adoption in property transfers
  • An assessment of the security of secure wireless communication systems used in tertiary institutions

Topics & Ideas: Software Engineering

  • A study of agile software development methodologies and their impact on project success in pharmacology
  • Investigating the impacts of software refactoring techniques and tools in blockchain-based developments
  • A study of the impact of DevOps practices on software development and delivery in the healthcare sector
  • An analysis of software architecture patterns and their impact on the maintainability and scalability of cloud-based offerings
  • A study of the impact of artificial intelligence and machine learning on software engineering practices in the education sector
  • An investigation of software testing techniques and methodologies for subscription-based offerings
  • A review of software security practices and techniques for protecting against phishing attacks from social media
  • An analysis of the impact of cloud computing on the rate of software development and deployment in the manufacturing sector
  • Exploring the impact of software development outsourcing on project success in multinational contexts
  • An investigation into the effect of poor software documentation on app success in the retail sector

CompSci & IT Dissertations/Theses

While the ideas we’ve presented above are a decent starting point for finding a CompSci-related research topic, they are fairly generic and non-specific. So, it helps to look at actual dissertations and theses to see how this all comes together.

Below, we’ve included a selection of research projects from various CompSci-related degree programs to help refine your thinking. These are actual dissertations and theses, written as part of Master’s and PhD-level programs, so they can provide some useful insight as to what a research topic looks like in practice.

  • An array-based optimization framework for query processing and data analytics (Chen, 2021)
  • Dynamic Object Partitioning and replication for cooperative cache (Asad, 2021)
  • Embedding constructural documentation in unit tests (Nassif, 2019)
  • PLASA | Programming Language for Synchronous Agents (Kilaru, 2019)
  • Healthcare Data Authentication using Deep Neural Network (Sekar, 2020)
  • Virtual Reality System for Planetary Surface Visualization and Analysis (Quach, 2019)
  • Artificial neural networks to predict share prices on the Johannesburg stock exchange (Pyon, 2021)
  • Predicting household poverty with machine learning methods: the case of Malawi (Chinyama, 2022)
  • Investigating user experience and bias mitigation of the multi-modal retrieval of historical data (Singh, 2021)
  • Detection of HTTPS malware traffic without decryption (Nyathi, 2022)
  • Redefining privacy: case study of smart health applications (Al-Zyoud, 2019)
  • A state-based approach to context modeling and computing (Yue, 2019)
  • A Novel Cooperative Intrusion Detection System for Mobile Ad Hoc Networks (Solomon, 2019)
  • HRSB-Tree for Spatio-Temporal Aggregates over Moving Regions (Paduri, 2019)

Looking at these titles, you can probably pick up that the research topics here are quite specific and narrowly-focused , compared to the generic ones presented earlier. This is an important thing to keep in mind as you develop your own research topic. That is to say, to create a top-notch research topic, you must be precise and target a specific context with specific variables of interest . In other words, you need to identify a clear, well-justified research gap.

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Investigating the impacts of software refactoring techniques and tools in blockchain-based developments.

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Home » 500+ Computer Science Research Topics

500+ Computer Science Research Topics

Computer Science Research Topics

Computer Science is a constantly evolving field that has transformed the world we live in today. With new technologies emerging every day, there are countless research opportunities in this field. Whether you are interested in artificial intelligence, machine learning, cybersecurity, data analytics, or computer networks, there are endless possibilities to explore. In this post, we will delve into some of the most interesting and important research topics in Computer Science. From the latest advancements in programming languages to the development of cutting-edge algorithms, we will explore the latest trends and innovations that are shaping the future of Computer Science. So, whether you are a student or a professional, read on to discover some of the most exciting research topics in this dynamic and rapidly expanding field.

Computer Science Research Topics

Computer Science Research Topics are as follows:

  • Using machine learning to detect and prevent cyber attacks
  • Developing algorithms for optimized resource allocation in cloud computing
  • Investigating the use of blockchain technology for secure and decentralized data storage
  • Developing intelligent chatbots for customer service
  • Investigating the effectiveness of deep learning for natural language processing
  • Developing algorithms for detecting and removing fake news from social media
  • Investigating the impact of social media on mental health
  • Developing algorithms for efficient image and video compression
  • Investigating the use of big data analytics for predictive maintenance in manufacturing
  • Developing algorithms for identifying and mitigating bias in machine learning models
  • Investigating the ethical implications of autonomous vehicles
  • Developing algorithms for detecting and preventing cyberbullying
  • Investigating the use of machine learning for personalized medicine
  • Developing algorithms for efficient and accurate speech recognition
  • Investigating the impact of social media on political polarization
  • Developing algorithms for sentiment analysis in social media data
  • Investigating the use of virtual reality in education
  • Developing algorithms for efficient data encryption and decryption
  • Investigating the impact of technology on workplace productivity
  • Developing algorithms for detecting and mitigating deepfakes
  • Investigating the use of artificial intelligence in financial trading
  • Developing algorithms for efficient database management
  • Investigating the effectiveness of online learning platforms
  • Developing algorithms for efficient and accurate facial recognition
  • Investigating the use of machine learning for predicting weather patterns
  • Developing algorithms for efficient and secure data transfer
  • Investigating the impact of technology on social skills and communication
  • Developing algorithms for efficient and accurate object recognition
  • Investigating the use of machine learning for fraud detection in finance
  • Developing algorithms for efficient and secure authentication systems
  • Investigating the impact of technology on privacy and surveillance
  • Developing algorithms for efficient and accurate handwriting recognition
  • Investigating the use of machine learning for predicting stock prices
  • Developing algorithms for efficient and secure biometric identification
  • Investigating the impact of technology on mental health and well-being
  • Developing algorithms for efficient and accurate language translation
  • Investigating the use of machine learning for personalized advertising
  • Developing algorithms for efficient and secure payment systems
  • Investigating the impact of technology on the job market and automation
  • Developing algorithms for efficient and accurate object tracking
  • Investigating the use of machine learning for predicting disease outbreaks
  • Developing algorithms for efficient and secure access control
  • Investigating the impact of technology on human behavior and decision making
  • Developing algorithms for efficient and accurate sound recognition
  • Investigating the use of machine learning for predicting customer behavior
  • Developing algorithms for efficient and secure data backup and recovery
  • Investigating the impact of technology on education and learning outcomes
  • Developing algorithms for efficient and accurate emotion recognition
  • Investigating the use of machine learning for improving healthcare outcomes
  • Developing algorithms for efficient and secure supply chain management
  • Investigating the impact of technology on cultural and societal norms
  • Developing algorithms for efficient and accurate gesture recognition
  • Investigating the use of machine learning for predicting consumer demand
  • Developing algorithms for efficient and secure cloud storage
  • Investigating the impact of technology on environmental sustainability
  • Developing algorithms for efficient and accurate voice recognition
  • Investigating the use of machine learning for improving transportation systems
  • Developing algorithms for efficient and secure mobile device management
  • Investigating the impact of technology on social inequality and access to resources
  • Machine learning for healthcare diagnosis and treatment
  • Machine Learning for Cybersecurity
  • Machine learning for personalized medicine
  • Cybersecurity threats and defense strategies
  • Big data analytics for business intelligence
  • Blockchain technology and its applications
  • Human-computer interaction in virtual reality environments
  • Artificial intelligence for autonomous vehicles
  • Natural language processing for chatbots
  • Cloud computing and its impact on the IT industry
  • Internet of Things (IoT) and smart homes
  • Robotics and automation in manufacturing
  • Augmented reality and its potential in education
  • Data mining techniques for customer relationship management
  • Computer vision for object recognition and tracking
  • Quantum computing and its applications in cryptography
  • Social media analytics and sentiment analysis
  • Recommender systems for personalized content delivery
  • Mobile computing and its impact on society
  • Bioinformatics and genomic data analysis
  • Deep learning for image and speech recognition
  • Digital signal processing and audio processing algorithms
  • Cloud storage and data security in the cloud
  • Wearable technology and its impact on healthcare
  • Computational linguistics for natural language understanding
  • Cognitive computing for decision support systems
  • Cyber-physical systems and their applications
  • Edge computing and its impact on IoT
  • Machine learning for fraud detection
  • Cryptography and its role in secure communication
  • Cybersecurity risks in the era of the Internet of Things
  • Natural language generation for automated report writing
  • 3D printing and its impact on manufacturing
  • Virtual assistants and their applications in daily life
  • Cloud-based gaming and its impact on the gaming industry
  • Computer networks and their security issues
  • Cyber forensics and its role in criminal investigations
  • Machine learning for predictive maintenance in industrial settings
  • Augmented reality for cultural heritage preservation
  • Human-robot interaction and its applications
  • Data visualization and its impact on decision-making
  • Cybersecurity in financial systems and blockchain
  • Computer graphics and animation techniques
  • Biometrics and its role in secure authentication
  • Cloud-based e-learning platforms and their impact on education
  • Natural language processing for machine translation
  • Machine learning for predictive maintenance in healthcare
  • Cybersecurity and privacy issues in social media
  • Computer vision for medical image analysis
  • Natural language generation for content creation
  • Cybersecurity challenges in cloud computing
  • Human-robot collaboration in manufacturing
  • Data mining for predicting customer churn
  • Artificial intelligence for autonomous drones
  • Cybersecurity risks in the healthcare industry
  • Machine learning for speech synthesis
  • Edge computing for low-latency applications
  • Virtual reality for mental health therapy
  • Quantum computing and its applications in finance
  • Biomedical engineering and its applications
  • Cybersecurity in autonomous systems
  • Machine learning for predictive maintenance in transportation
  • Computer vision for object detection in autonomous driving
  • Augmented reality for industrial training and simulations
  • Cloud-based cybersecurity solutions for small businesses
  • Natural language processing for knowledge management
  • Machine learning for personalized advertising
  • Cybersecurity in the supply chain management
  • Cybersecurity risks in the energy sector
  • Computer vision for facial recognition
  • Natural language processing for social media analysis
  • Machine learning for sentiment analysis in customer reviews
  • Explainable Artificial Intelligence
  • Quantum Computing
  • Blockchain Technology
  • Human-Computer Interaction
  • Natural Language Processing
  • Cloud Computing
  • Robotics and Automation
  • Augmented Reality and Virtual Reality
  • Cyber-Physical Systems
  • Computational Neuroscience
  • Big Data Analytics
  • Computer Vision
  • Cryptography and Network Security
  • Internet of Things
  • Computer Graphics and Visualization
  • Artificial Intelligence for Game Design
  • Computational Biology
  • Social Network Analysis
  • Bioinformatics
  • Distributed Systems and Middleware
  • Information Retrieval and Data Mining
  • Computer Networks
  • Mobile Computing and Wireless Networks
  • Software Engineering
  • Database Systems
  • Parallel and Distributed Computing
  • Human-Robot Interaction
  • Intelligent Transportation Systems
  • High-Performance Computing
  • Cyber-Physical Security
  • Deep Learning
  • Sensor Networks
  • Multi-Agent Systems
  • Human-Centered Computing
  • Wearable Computing
  • Knowledge Representation and Reasoning
  • Adaptive Systems
  • Brain-Computer Interface
  • Health Informatics
  • Cognitive Computing
  • Cybersecurity and Privacy
  • Internet Security
  • Cybercrime and Digital Forensics
  • Cloud Security
  • Cryptocurrencies and Digital Payments
  • Machine Learning for Natural Language Generation
  • Cognitive Robotics
  • Neural Networks
  • Semantic Web
  • Image Processing
  • Cyber Threat Intelligence
  • Secure Mobile Computing
  • Cybersecurity Education and Training
  • Privacy Preserving Techniques
  • Cyber-Physical Systems Security
  • Virtualization and Containerization
  • Machine Learning for Computer Vision
  • Network Function Virtualization
  • Cybersecurity Risk Management
  • Information Security Governance
  • Intrusion Detection and Prevention
  • Biometric Authentication
  • Machine Learning for Predictive Maintenance
  • Security in Cloud-based Environments
  • Cybersecurity for Industrial Control Systems
  • Smart Grid Security
  • Software Defined Networking
  • Quantum Cryptography
  • Security in the Internet of Things
  • Natural language processing for sentiment analysis
  • Blockchain technology for secure data sharing
  • Developing efficient algorithms for big data analysis
  • Cybersecurity for internet of things (IoT) devices
  • Human-robot interaction for industrial automation
  • Image recognition for autonomous vehicles
  • Social media analytics for marketing strategy
  • Quantum computing for solving complex problems
  • Biometric authentication for secure access control
  • Augmented reality for education and training
  • Intelligent transportation systems for traffic management
  • Predictive modeling for financial markets
  • Cloud computing for scalable data storage and processing
  • Virtual reality for therapy and mental health treatment
  • Data visualization for business intelligence
  • Recommender systems for personalized product recommendations
  • Speech recognition for voice-controlled devices
  • Mobile computing for real-time location-based services
  • Neural networks for predicting user behavior
  • Genetic algorithms for optimization problems
  • Distributed computing for parallel processing
  • Internet of things (IoT) for smart cities
  • Wireless sensor networks for environmental monitoring
  • Cloud-based gaming for high-performance gaming
  • Social network analysis for identifying influencers
  • Autonomous systems for agriculture
  • Robotics for disaster response
  • Data mining for customer segmentation
  • Computer graphics for visual effects in movies and video games
  • Virtual assistants for personalized customer service
  • Natural language understanding for chatbots
  • 3D printing for manufacturing prototypes
  • Artificial intelligence for stock trading
  • Machine learning for weather forecasting
  • Biomedical engineering for prosthetics and implants
  • Cybersecurity for financial institutions
  • Machine learning for energy consumption optimization
  • Computer vision for object tracking
  • Natural language processing for document summarization
  • Wearable technology for health and fitness monitoring
  • Internet of things (IoT) for home automation
  • Reinforcement learning for robotics control
  • Big data analytics for customer insights
  • Machine learning for supply chain optimization
  • Natural language processing for legal document analysis
  • Artificial intelligence for drug discovery
  • Computer vision for object recognition in robotics
  • Data mining for customer churn prediction
  • Autonomous systems for space exploration
  • Robotics for agriculture automation
  • Machine learning for predicting earthquakes
  • Natural language processing for sentiment analysis in customer reviews
  • Big data analytics for predicting natural disasters
  • Internet of things (IoT) for remote patient monitoring
  • Blockchain technology for digital identity management
  • Machine learning for predicting wildfire spread
  • Computer vision for gesture recognition
  • Natural language processing for automated translation
  • Big data analytics for fraud detection in banking
  • Internet of things (IoT) for smart homes
  • Robotics for warehouse automation
  • Machine learning for predicting air pollution
  • Natural language processing for medical record analysis
  • Augmented reality for architectural design
  • Big data analytics for predicting traffic congestion
  • Machine learning for predicting customer lifetime value
  • Developing algorithms for efficient and accurate text recognition
  • Natural Language Processing for Virtual Assistants
  • Natural Language Processing for Sentiment Analysis in Social Media
  • Explainable Artificial Intelligence (XAI) for Trust and Transparency
  • Deep Learning for Image and Video Retrieval
  • Edge Computing for Internet of Things (IoT) Applications
  • Data Science for Social Media Analytics
  • Cybersecurity for Critical Infrastructure Protection
  • Natural Language Processing for Text Classification
  • Quantum Computing for Optimization Problems
  • Machine Learning for Personalized Health Monitoring
  • Computer Vision for Autonomous Driving
  • Blockchain Technology for Supply Chain Management
  • Augmented Reality for Education and Training
  • Natural Language Processing for Sentiment Analysis
  • Machine Learning for Personalized Marketing
  • Big Data Analytics for Financial Fraud Detection
  • Cybersecurity for Cloud Security Assessment
  • Artificial Intelligence for Natural Language Understanding
  • Blockchain Technology for Decentralized Applications
  • Virtual Reality for Cultural Heritage Preservation
  • Natural Language Processing for Named Entity Recognition
  • Machine Learning for Customer Churn Prediction
  • Big Data Analytics for Social Network Analysis
  • Cybersecurity for Intrusion Detection and Prevention
  • Artificial Intelligence for Robotics and Automation
  • Blockchain Technology for Digital Identity Management
  • Virtual Reality for Rehabilitation and Therapy
  • Natural Language Processing for Text Summarization
  • Machine Learning for Credit Risk Assessment
  • Big Data Analytics for Fraud Detection in Healthcare
  • Cybersecurity for Internet Privacy Protection
  • Artificial Intelligence for Game Design and Development
  • Blockchain Technology for Decentralized Social Networks
  • Virtual Reality for Marketing and Advertising
  • Natural Language Processing for Opinion Mining
  • Machine Learning for Anomaly Detection
  • Big Data Analytics for Predictive Maintenance in Transportation
  • Cybersecurity for Network Security Management
  • Artificial Intelligence for Personalized News and Content Delivery
  • Blockchain Technology for Cryptocurrency Mining
  • Virtual Reality for Architectural Design and Visualization
  • Natural Language Processing for Machine Translation
  • Machine Learning for Automated Image Captioning
  • Big Data Analytics for Stock Market Prediction
  • Cybersecurity for Biometric Authentication Systems
  • Artificial Intelligence for Human-Robot Interaction
  • Blockchain Technology for Smart Grids
  • Virtual Reality for Sports Training and Simulation
  • Natural Language Processing for Question Answering Systems
  • Machine Learning for Sentiment Analysis in Customer Feedback
  • Big Data Analytics for Predictive Maintenance in Manufacturing
  • Cybersecurity for Cloud-Based Systems
  • Artificial Intelligence for Automated Journalism
  • Blockchain Technology for Intellectual Property Management
  • Virtual Reality for Therapy and Rehabilitation
  • Natural Language Processing for Language Generation
  • Machine Learning for Customer Lifetime Value Prediction
  • Big Data Analytics for Predictive Maintenance in Energy Systems
  • Cybersecurity for Secure Mobile Communication
  • Artificial Intelligence for Emotion Recognition
  • Blockchain Technology for Digital Asset Trading
  • Virtual Reality for Automotive Design and Visualization
  • Natural Language Processing for Semantic Web
  • Machine Learning for Fraud Detection in Financial Transactions
  • Big Data Analytics for Social Media Monitoring
  • Cybersecurity for Cloud Storage and Sharing
  • Artificial Intelligence for Personalized Education
  • Blockchain Technology for Secure Online Voting Systems
  • Virtual Reality for Cultural Tourism
  • Natural Language Processing for Chatbot Communication
  • Machine Learning for Medical Diagnosis and Treatment
  • Big Data Analytics for Environmental Monitoring and Management.
  • Cybersecurity for Cloud Computing Environments
  • Virtual Reality for Training and Simulation
  • Big Data Analytics for Sports Performance Analysis
  • Cybersecurity for Internet of Things (IoT) Devices
  • Artificial Intelligence for Traffic Management and Control
  • Blockchain Technology for Smart Contracts
  • Natural Language Processing for Document Summarization
  • Machine Learning for Image and Video Recognition
  • Blockchain Technology for Digital Asset Management
  • Virtual Reality for Entertainment and Gaming
  • Natural Language Processing for Opinion Mining in Online Reviews
  • Machine Learning for Customer Relationship Management
  • Big Data Analytics for Environmental Monitoring and Management
  • Cybersecurity for Network Traffic Analysis and Monitoring
  • Artificial Intelligence for Natural Language Generation
  • Blockchain Technology for Supply Chain Transparency and Traceability
  • Virtual Reality for Design and Visualization
  • Natural Language Processing for Speech Recognition
  • Machine Learning for Recommendation Systems
  • Big Data Analytics for Customer Segmentation and Targeting
  • Cybersecurity for Biometric Authentication
  • Artificial Intelligence for Human-Computer Interaction
  • Blockchain Technology for Decentralized Finance (DeFi)
  • Virtual Reality for Tourism and Cultural Heritage
  • Machine Learning for Cybersecurity Threat Detection and Prevention
  • Big Data Analytics for Healthcare Cost Reduction
  • Cybersecurity for Data Privacy and Protection
  • Artificial Intelligence for Autonomous Vehicles
  • Blockchain Technology for Cryptocurrency and Blockchain Security
  • Virtual Reality for Real Estate Visualization
  • Natural Language Processing for Question Answering
  • Big Data Analytics for Financial Markets Prediction
  • Cybersecurity for Cloud-Based Machine Learning Systems
  • Artificial Intelligence for Personalized Advertising
  • Blockchain Technology for Digital Identity Verification
  • Virtual Reality for Cultural and Language Learning
  • Natural Language Processing for Semantic Analysis
  • Machine Learning for Business Forecasting
  • Big Data Analytics for Social Media Marketing
  • Artificial Intelligence for Content Generation
  • Blockchain Technology for Smart Cities
  • Virtual Reality for Historical Reconstruction
  • Natural Language Processing for Knowledge Graph Construction
  • Machine Learning for Speech Synthesis
  • Big Data Analytics for Traffic Optimization
  • Artificial Intelligence for Social Robotics
  • Blockchain Technology for Healthcare Data Management
  • Virtual Reality for Disaster Preparedness and Response
  • Natural Language Processing for Multilingual Communication
  • Machine Learning for Emotion Recognition
  • Big Data Analytics for Human Resources Management
  • Cybersecurity for Mobile App Security
  • Artificial Intelligence for Financial Planning and Investment
  • Blockchain Technology for Energy Management
  • Virtual Reality for Cultural Preservation and Heritage.
  • Big Data Analytics for Healthcare Management
  • Cybersecurity in the Internet of Things (IoT)
  • Artificial Intelligence for Predictive Maintenance
  • Computational Biology for Drug Discovery
  • Virtual Reality for Mental Health Treatment
  • Machine Learning for Sentiment Analysis in Social Media
  • Human-Computer Interaction for User Experience Design
  • Cloud Computing for Disaster Recovery
  • Quantum Computing for Cryptography
  • Intelligent Transportation Systems for Smart Cities
  • Cybersecurity for Autonomous Vehicles
  • Artificial Intelligence for Fraud Detection in Financial Systems
  • Social Network Analysis for Marketing Campaigns
  • Cloud Computing for Video Game Streaming
  • Machine Learning for Speech Recognition
  • Augmented Reality for Architecture and Design
  • Natural Language Processing for Customer Service Chatbots
  • Machine Learning for Climate Change Prediction
  • Big Data Analytics for Social Sciences
  • Artificial Intelligence for Energy Management
  • Virtual Reality for Tourism and Travel
  • Cybersecurity for Smart Grids
  • Machine Learning for Image Recognition
  • Augmented Reality for Sports Training
  • Natural Language Processing for Content Creation
  • Cloud Computing for High-Performance Computing
  • Artificial Intelligence for Personalized Medicine
  • Virtual Reality for Architecture and Design
  • Augmented Reality for Product Visualization
  • Natural Language Processing for Language Translation
  • Cybersecurity for Cloud Computing
  • Artificial Intelligence for Supply Chain Optimization
  • Blockchain Technology for Digital Voting Systems
  • Virtual Reality for Job Training
  • Augmented Reality for Retail Shopping
  • Natural Language Processing for Sentiment Analysis in Customer Feedback
  • Cloud Computing for Mobile Application Development
  • Artificial Intelligence for Cybersecurity Threat Detection
  • Blockchain Technology for Intellectual Property Protection
  • Virtual Reality for Music Education
  • Machine Learning for Financial Forecasting
  • Augmented Reality for Medical Education
  • Natural Language Processing for News Summarization
  • Cybersecurity for Healthcare Data Protection
  • Artificial Intelligence for Autonomous Robots
  • Virtual Reality for Fitness and Health
  • Machine Learning for Natural Language Understanding
  • Augmented Reality for Museum Exhibits
  • Natural Language Processing for Chatbot Personality Development
  • Cloud Computing for Website Performance Optimization
  • Artificial Intelligence for E-commerce Recommendation Systems
  • Blockchain Technology for Supply Chain Traceability
  • Virtual Reality for Military Training
  • Augmented Reality for Advertising
  • Natural Language Processing for Chatbot Conversation Management
  • Cybersecurity for Cloud-Based Services
  • Artificial Intelligence for Agricultural Management
  • Blockchain Technology for Food Safety Assurance
  • Virtual Reality for Historical Reenactments
  • Machine Learning for Cybersecurity Incident Response.
  • Secure Multiparty Computation
  • Federated Learning
  • Internet of Things Security
  • Blockchain Scalability
  • Quantum Computing Algorithms
  • Explainable AI
  • Data Privacy in the Age of Big Data
  • Adversarial Machine Learning
  • Deep Reinforcement Learning
  • Online Learning and Streaming Algorithms
  • Graph Neural Networks
  • Automated Debugging and Fault Localization
  • Mobile Application Development
  • Software Engineering for Cloud Computing
  • Cryptocurrency Security
  • Edge Computing for Real-Time Applications
  • Natural Language Generation
  • Virtual and Augmented Reality
  • Computational Biology and Bioinformatics
  • Internet of Things Applications
  • Robotics and Autonomous Systems
  • Explainable Robotics
  • 3D Printing and Additive Manufacturing
  • Distributed Systems
  • Parallel Computing
  • Data Center Networking
  • Data Mining and Knowledge Discovery
  • Information Retrieval and Search Engines
  • Network Security and Privacy
  • Cloud Computing Security
  • Data Analytics for Business Intelligence
  • Neural Networks and Deep Learning
  • Reinforcement Learning for Robotics
  • Automated Planning and Scheduling
  • Evolutionary Computation and Genetic Algorithms
  • Formal Methods for Software Engineering
  • Computational Complexity Theory
  • Bio-inspired Computing
  • Computer Vision for Object Recognition
  • Automated Reasoning and Theorem Proving
  • Natural Language Understanding
  • Machine Learning for Healthcare
  • Scalable Distributed Systems
  • Sensor Networks and Internet of Things
  • Smart Grids and Energy Systems
  • Software Testing and Verification
  • Web Application Security
  • Wireless and Mobile Networks
  • Computer Architecture and Hardware Design
  • Digital Signal Processing
  • Game Theory and Mechanism Design
  • Multi-agent Systems
  • Evolutionary Robotics
  • Quantum Machine Learning
  • Computational Social Science
  • Explainable Recommender Systems.
  • Artificial Intelligence and its applications
  • Cloud computing and its benefits
  • Cybersecurity threats and solutions
  • Internet of Things and its impact on society
  • Virtual and Augmented Reality and its uses
  • Blockchain Technology and its potential in various industries
  • Web Development and Design
  • Digital Marketing and its effectiveness
  • Big Data and Analytics
  • Software Development Life Cycle
  • Gaming Development and its growth
  • Network Administration and Maintenance
  • Machine Learning and its uses
  • Data Warehousing and Mining
  • Computer Architecture and Design
  • Computer Graphics and Animation
  • Quantum Computing and its potential
  • Data Structures and Algorithms
  • Computer Vision and Image Processing
  • Robotics and its applications
  • Operating Systems and its functions
  • Information Theory and Coding
  • Compiler Design and Optimization
  • Computer Forensics and Cyber Crime Investigation
  • Distributed Computing and its significance
  • Artificial Neural Networks and Deep Learning
  • Cloud Storage and Backup
  • Programming Languages and their significance
  • Computer Simulation and Modeling
  • Computer Networks and its types
  • Information Security and its types
  • Computer-based Training and eLearning
  • Medical Imaging and its uses
  • Social Media Analysis and its applications
  • Human Resource Information Systems
  • Computer-Aided Design and Manufacturing
  • Multimedia Systems and Applications
  • Geographic Information Systems and its uses
  • Computer-Assisted Language Learning
  • Mobile Device Management and Security
  • Data Compression and its types
  • Knowledge Management Systems
  • Text Mining and its uses
  • Cyber Warfare and its consequences
  • Wireless Networks and its advantages
  • Computer Ethics and its importance
  • Computational Linguistics and its applications
  • Autonomous Systems and Robotics
  • Information Visualization and its importance
  • Geographic Information Retrieval and Mapping
  • Business Intelligence and its benefits
  • Digital Libraries and their significance
  • Artificial Life and Evolutionary Computation
  • Computer Music and its types
  • Virtual Teams and Collaboration
  • Computer Games and Learning
  • Semantic Web and its applications
  • Electronic Commerce and its advantages
  • Multimedia Databases and their significance
  • Computer Science Education and its importance
  • Computer-Assisted Translation and Interpretation
  • Ambient Intelligence and Smart Homes
  • Autonomous Agents and Multi-Agent Systems.

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How to Contact Faculty for IW/Thesis Advising

Send the professor an e-mail. When you write a professor, be clear that you want a meeting regarding a senior thesis or one-on-one IW project, and briefly describe the topic or idea that you want to work on. Check the faculty listing for email addresses.

Parastoo Abtahi, Room 419

Available for single-semester IW and senior thesis advising, 2023-2024

  • Research Areas: Human-Computer Interaction (HCI), Augmented Reality (AR), and Spatial Computing
  • Input techniques for on-the-go interaction (e.g., eye-gaze, microgestures, voice) with a focus on uncertainty, disambiguation, and privacy.
  • Minimal and timely multisensory output (e.g., spatial audio, haptics) that enables users to attend to their physical environment and the people around them, instead of a 2D screen.
  • Interaction with intelligent systems (e.g., IoT, robots) situated in physical spaces with a focus on updating users’ mental model despite the complexity and dynamicity of these systems.

Ryan Adams, Room 411

Research areas:

  • Machine learning driven design
  • Generative models for structured discrete objects
  • Approximate inference in probabilistic models
  • Accelerating solutions to partial differential equations
  • Innovative uses of automatic differentiation
  • Modeling and optimizing 3d printing and CNC machining

Andrew Appel, Room 209

  • Research Areas: Formal methods, programming languages, compilers, computer security.
  • Software verification (for which taking COS 326 / COS 510 is helpful preparation)
  • Game theory of poker or other games (for which COS 217 / 226 are helpful)
  • Computer game-playing programs (for which COS 217 / 226)
  •  Risk-limiting audits of elections (for which ORF 245 or other knowledge of probability is useful)

Sanjeev Arora, Room 407

  • Theoretical machine learning, deep learning and its analysis, natural language processing. My advisees would typically have taken a course in algorithms (COS423 or COS 521 or equivalent) and a course in machine learning.
  • Show that finding approximate solutions to NP-complete problems is also NP-complete (i.e., come up with NP-completeness reductions a la COS 487). 
  • Experimental Algorithms: Implementing and Evaluating Algorithms using existing software packages. 
  • Studying/designing provable algorithms for machine learning and implementions using packages like scipy and MATLAB, including applications in Natural language processing and deep learning.
  • Any topic in theoretical computer science.

David August, Room 221

  • Research Areas: Computer Architecture, Compilers, Parallelism
  • Containment-based approaches to security:  We have designed and tested a simple hardware+software containment mechanism that stops incorrect communication resulting from faults, bugs, or exploits from leaving the system.   Let's explore ways to use containment to solve real problems.  Expect to work with corporate security and technology decision-makers.
  • Parallelism: Studies show much more parallelism than is currently realized in compilers and architectures.  Let's find ways to realize this parallelism.
  • Any other interesting topic in computer architecture or compilers. 

Mark Braverman, 194 Nassau St., Room 231

Available for Spring 2024 single-semester IW, only

  • Research Areas: computational complexity, algorithms, applied probability, computability over the real numbers, game theory and mechanism design, information theory.
  • Topics in computational and communication complexity.
  • Applications of information theory in complexity theory.
  • Algorithms for problems under real-life assumptions.
  • Game theory, network effects
  • Mechanism design (could be on a problem proposed by the student)

Sebastian Caldas, 221 Nassau Street, Room 105

  • Research Areas: collaborative learning, machine learning for healthcare. Typically, I will work with students that have taken COS324.
  • Methods for collaborative and continual learning.
  • Machine learning for healthcare applications.

Bernard Chazelle, 194 Nassau St., Room 301

  • Research Areas: Natural Algorithms, Computational Geometry, Sublinear Algorithms. 
  • Natural algorithms (flocking, swarming, social networks, etc).
  • Sublinear algorithms
  • Self-improving algorithms
  • Markov data structures

Danqi Chen, Room 412

Not available for IW or thesis advising, 2023-2024

  • My advisees would be expected to have taken a course in machine learning and ideally have taken COS484 or an NLP graduate seminar.
  • Representation learning for text and knowledge bases
  • Pre-training and transfer learning
  • Question answering and reading comprehension
  • Information extraction
  • Text summarization
  • Any other interesting topics related to natural language understanding/generation

Marcel Dall'Agnol, Corwin 034

Available for single-semester and senior thesis advising, 2023-2024

  • Research Areas: Theoretical computer science. (Specifically, quantum computation, sublinear algorithms, complexity theory, interactive proofs and cryptography)

Jia Deng, Room 423

Available for Fall 2023 single-semester IW, only

  •  Research Areas: Computer Vision, Machine Learning.
  • Object recognition and action recognition
  • Deep Learning, autoML, meta-learning
  • Geometric reasoning, logical reasoning

Adji Bousso Dieng, Room 406

  • Research areas: Vertaix is a research lab at Princeton University led by Professor Adji Bousso Dieng. We work at the intersection of artificial intelligence (AI) and the natural sciences. The models and algorithms we develop are motivated by problems in those domains and contribute to advancing methodological research in AI. We leverage tools in statistical machine learning and deep learning in developing methods for learning with the data, of various modalities, arising from the natural sciences.

Robert Dondero, Corwin Hall, Room 038

  • Research Areas:  Software engineering; software engineering education.
  • Develop or evaluate tools to facilitate student learning in undergraduate computer science courses at Princeton, and beyond.
  • In particular, can code critiquing tools help students learn about software quality?

Zeev Dvir, 194 Nassau St., Room 250

Not available for IW or thesis advising, 2023-2024.

  • Research Areas: computational complexity, pseudo-randomness, coding theory and discrete mathematics.
  • Independent Research: I have various research problems related to Pseudorandomness, Coding theory, Complexity and Discrete mathematics - all of which require strong mathematical background. A project could also be based on writing a survey paper describing results from a few theory papers revolving around some particular subject.

Benjamin Eysenbach, Room 416

  • Research areas: reinforcement learning, machine learning. My advisees would typically have taken COS324.
  • Using RL algorithms to applications in science and engineering.
  • Emergent behavior of RL algorithms on high-fidelity robotic simulators.
  • Studying how architectures and representations can facilitate generalization.

Christiane Fellbaum, 1-S-14 Green

No longer available for single-term IW and senior thesis advising, 2023-2024

  • Research Areas: theoretical and computational linguistics, word sense disambiguation, lexical resource construction, English and multilingual WordNet(s), ontology
  • Anything having to do with natural language--come and see me with/for ideas suitable to your background and interests. Some topics students have worked on in the past:
  • Developing parsers, part-of-speech taggers, morphological analyzers for underrepresented languages (you don't have to know the language to develop such tools!)
  • Quantitative approaches to theoretical linguistics questions
  • Extensions and interfaces for WordNet (English and WN in other languages),
  • Applications of WordNet(s), including:
  • Foreign language tutoring systems,
  • Spelling correction software,
  • Word-finding/suggestion software for ordinary users and people with memory problems,
  • Machine Translation 
  • Sentiment and Opinion detection
  • Automatic reasoning and inferencing
  • Collaboration with professors in the social sciences and humanities ("Digital Humanities")

Adam Finkelstein, Room 424 

  • Research Areas: computer graphics, audio.

Robert S. Fish, Corwin Hall, Room 037

No longer available for single-semester IW and senior thesis advising, 2023-2024

  • Networking and telecommunications
  • Learning, perception, and intelligence, artificial and otherwise;
  • Human-computer interaction and computer-supported cooperative work
  • Online education, especially in Computer Science Education
  • Topics in research and development innovation methodologies including standards, open-source, and entrepreneurship
  • Distributed autonomous organizations and related blockchain technologies

Michael Freedman, Room 308 

  • Research Areas: Distributed systems, security, networking
  • Projects related to streaming data analysis, datacenter systems and networks, untrusted cloud storage and applications. Please see my group website at http://sns.cs.princeton.edu/ for current research projects.

Ruth Fong, Room 032

  • Research Areas: computer vision, machine learning, deep learning, interpretability, explainable AI, fairness and bias in AI
  • Develop a technique for understanding AI models
  • Design a AI model that is interpretable by design
  • Build a paradigm for detecting and/or correcting failure points in an AI model
  • Analyze an existing AI model and/or dataset to better understand its failure points
  • Build a computer vision system for another domain (e.g., medical imaging, satellite data, etc.)
  • Develop a software package for explainable AI
  • Adapt explainable AI research to a consumer-facing problem

Note: I am happy to advise any project if there's a sufficient overlap in interest and/or expertise; please reach out via email to chat about project ideas.

Tom Griffiths, Room 405

Research areas: computational cognitive science, computational social science, machine learning and artificial intelligence

Note: I am open to projects that apply ideas from computer science to understanding aspects of human cognition in a wide range of areas, from decision-making to cultural evolution and everything in between. For example, we have current projects analyzing chess game data and magic tricks, both of which give us clues about how human minds work. Students who have expertise or access to data related to games, magic, strategic sports like fencing, or other quantifiable domains of human behavior feel free to get in touch.

Aarti Gupta, Room 220

  • Research Areas: Formal methods, program analysis, logic decision procedures
  • Finding bugs in open source software using automatic verification tools
  • Software verification (program analysis, model checking, test generation)
  • Decision procedures for logical reasoning (SAT solvers, SMT solvers)

Elad Hazan, Room 409  

  • Research interests: machine learning methods and algorithms, efficient methods for mathematical optimization, regret minimization in games, reinforcement learning, control theory and practice
  • Machine learning, efficient methods for mathematical optimization, statistical and computational learning theory, regret minimization in games.
  • Implementation and algorithm engineering for control, reinforcement learning and robotics
  • Implementation and algorithm engineering for time series prediction

Felix Heide, Room 410

  • Research Areas: Computational Imaging, Computer Vision, Machine Learning (focus on Optimization and Approximate Inference).
  • Optical Neural Networks
  • Hardware-in-the-loop Holography
  • Zero-shot and Simulation-only Learning
  • Object recognition in extreme conditions
  • 3D Scene Representations for View Generation and Inverse Problems
  • Long-range Imaging in Scattering Media
  • Hardware-in-the-loop Illumination and Sensor Optimization
  • Inverse Lidar Design
  • Phase Retrieval Algorithms
  • Proximal Algorithms for Learning and Inference
  • Domain-Specific Language for Optics Design

Kyle Jamieson, Room 306

  • Research areas: Wireless and mobile networking; indoor radar and indoor localization; Internet of Things
  • See other topics on my independent work  ideas page  (campus IP and CS dept. login req'd)

Alan Kaplan, 221 Nassau Street, Room 105

Research Areas:

  • Random apps of kindness - mobile application/technology frameworks used to help individuals or communities; topic areas include, but are not limited to: first response, accessibility, environment, sustainability, social activism, civic computing, tele-health, remote learning, crowdsourcing, etc.
  • Tools automating programming language interoperability - Java/C++, React Native/Java, etc.
  • Software visualization tools for education
  • Connected consumer devices, applications and protocols

Brian Kernighan, Room 311

  • Research Areas: application-specific languages, document preparation, user interfaces, software tools, programming methodology
  • Application-oriented languages, scripting languages.
  • Tools; user interfaces
  • Digital humanities

Zachary Kincaid, Room 219

  • Research areas: programming languages, program analysis, program verification, automated reasoning
  • Independent Research Topics:
  • Develop a practical algorithm for an intractable problem (e.g., by developing practical search heuristics, or by reducing to, or by identifying a tractable sub-problem, ...).
  • Design a domain-specific programming language, or prototype a new feature for an existing language.
  • Any interesting project related to programming languages or logic.

Gillat Kol, Room 316

Aleksandra korolova, 309 sherrerd hall.

Available for single-term IW and senior thesis advising, 2023-2024

  • Research areas: Societal impacts of algorithms and AI; privacy; fair and privacy-preserving machine learning; algorithm auditing.

Advisees typically have taken one or more of COS 226, COS 324, COS 423, COS 424 or COS 445.

Amit Levy, Room 307

  • Research Areas: Operating Systems, Distributed Systems, Embedded Systems, Internet of Things
  • Distributed hardware testing infrastructure
  • Second factor security tokens
  • Low-power wireless network protocol implementation
  • USB device driver implementation

Kai Li, Room 321

  • Research Areas: Distributed systems; storage systems; content-based search and data analysis of large datasets.
  • Fast communication mechanisms for heterogeneous clusters.
  • Approximate nearest-neighbor search for high dimensional data.
  • Data analysis and prediction of in-patient medical data.
  • Optimized implementation of classification algorithms on manycore processors.

Xiaoyan Li, 221 Nassau Street, Room 104

  • Research areas: Information retrieval, novelty detection, question answering, AI, machine learning and data analysis.
  • Explore new statistical retrieval models for document retrieval and question answering.
  • Apply AI in various fields.
  • Apply supervised or unsupervised learning in health, education, finance, and social networks, etc.
  • Any interesting project related to AI, machine learning, and data analysis.

Wyatt Lloyd, Room 323

  • Research areas: Distributed Systems
  • Caching algorithms and implementations
  • Storage systems
  • Distributed transaction algorithms and implementations

Margaret Martonosi, Room 208

  • Quantum Computing research, particularly related to architecture and compiler issues for QC.
  • Computer architectures specialized for modern workloads (e.g., graph analytics, machine learning algorithms, mobile applications
  • Investigating security and privacy vulnerabilities in computer systems, particularly IoT devices.
  • Other topics in computer architecture or mobile / IoT systems also possible.

Jonathan Mayer, Sherrerd Hall, Room 307 

  • Research areas: Technology law and policy, with emphasis on national security, criminal procedure, consumer privacy, network management, and online speech.
  • Assessing the effects of government policies, both in the public and private sectors.
  • Collecting new data that relates to government decision making, including surveying current business practices and studying user behavior.
  • Developing new tools to improve government processes and offer policy alternatives.

Andrés Monroy-Hernåndez, Room 405

  • Research Areas: Human-Computer Interaction, Social Computing, Public-Interest Technology, Augmented Reality, Urban Computing
  • Research interests:developing public-interest socio-technical systems.  We are currently creating alternatives to gig work platforms that are more equitable for all stakeholders. For instance, we are investigating the socio-technical affordances necessary to support a co-op food delivery network owned and managed by workers and restaurants. We are exploring novel system designs that support self-governance, decentralized/federated models, community-centered data ownership, and portable reputation systems.  We have opportunities for students interested in human-centered computing, UI/UX design, full-stack software development, and qualitative/quantitative user research.
  • Beyond our core projects, we are open to working on research projects that explore the use of emerging technologies, such as AR, wearables, NFTs, and DAOs, for creative and out-of-the-box applications.

Christopher Moretti, Corwin Hall, Room 036

  • Research areas: Distributed systems, high-throughput computing, computer science/engineering education
  • Expansion, improvement, and evaluation of open-source distributed computing software.
  • Applications of distributed computing for "big science" (e.g. biometrics, data mining, bioinformatics)
  • Software and best practices for computer science education and study, especially Princeton's 126/217/226 sequence or MOOCs development
  • Sports analytics and/or crowd-sourced computing

Radhika Nagpal, F316 Engineering Quadrangle

  • Research areas: control, robotics and dynamical systems

Karthik Narasimhan, Room 422

  • Research areas: Natural Language Processing, Reinforcement Learning
  • Autonomous agents for text-based games ( https://www.microsoft.com/en-us/research/project/textworld/ )
  • Transfer learning/generalization in NLP
  • Techniques for generating natural language
  • Model-based reinforcement learning

Arvind Narayanan, 308 Sherrerd Hall 

Research Areas: fair machine learning (and AI ethics more broadly), the social impact of algorithmic systems, tech policy

Pedro Paredes, Corwin Hall, Room 041

My primary research work is in Theoretical Computer Science.

 * Research Interest: Spectral Graph theory, Pseudorandomness, Complexity theory, Coding Theory, Quantum Information Theory, Combinatorics.

The IW projects I am interested in advising can be divided into three categories:

 1. Theoretical research

I am open to advise work on research projects in any topic in one of my research areas of interest. A project could also be based on writing a survey given results from a few papers. Students should have a solid background in math (e.g., elementary combinatorics, graph theory, discrete probability, basic algebra/calculus) and theoretical computer science (226 and 240 material, like big-O/Omega/Theta, basic complexity theory, basic fundamental algorithms). Mathematical maturity is a must.

A (non exhaustive) list of topics of projects I'm interested in:   * Explicit constructions of better vertex expanders and/or unique neighbor expanders.   * Construction deterministic or random high dimensional expanders.   * Pseudorandom generators for different problems.   * Topics around the quantum PCP conjecture.   * Topics around quantum error correcting codes and locally testable codes, including constructions, encoding and decoding algorithms.

 2. Theory informed practical implementations of algorithms   Very often the great advances in theoretical research are either not tested in practice or not even feasible to be implemented in practice. Thus, I am interested in any project that consists in trying to make theoretical ideas applicable in practice. This includes coming up with new algorithms that trade some theoretical guarantees for feasible implementation yet trying to retain the soul of the original idea; implementing new algorithms in a suitable programming language; and empirically testing practical implementations and comparing them with benchmarks / theoretical expectations. A project in this area doesn't have to be in my main areas of research, any theoretical result could be suitable for such a project.

Some examples of areas of interest:   * Streaming algorithms.   * Numeric linear algebra.   * Property testing.   * Parallel / Distributed algorithms.   * Online algorithms.    3. Machine learning with a theoretical foundation

I am interested in projects in machine learning that have some mathematical/theoretical, even if most of the project is applied. This includes topics like mathematical optimization, statistical learning, fairness and privacy.

One particular area I have been recently interested in is in the area of rating systems (e.g., Chess elo) and applications of this to experts problems.

Final Note: I am also willing to advise any project with any mathematical/theoretical component, even if it's not the main one; please reach out via email to chat about project ideas.

Iasonas Petras, Corwin Hall, Room 033

  • Research Areas: Information Based Complexity, Numerical Analysis, Quantum Computation.
  • Prerequisites: Reasonable mathematical maturity. In case of a project related to Quantum Computation a certain familiarity with quantum mechanics is required (related courses: ELE 396/PHY 208).
  • Possible research topics include:

1.   Quantum algorithms and circuits:

  • i. Design or simulation quantum circuits implementing quantum algorithms.
  • ii. Design of quantum algorithms solving/approximating continuous problems (such as Eigenvalue problems for Partial Differential Equations).

2.   Information Based Complexity:

  • i. Necessary and sufficient conditions for tractability of Linear and Linear Tensor Product Problems in various settings (for example worst case or average case). 
  • ii. Necessary and sufficient conditions for tractability of Linear and Linear Tensor Product Problems under new tractability and error criteria.
  • iii. Necessary and sufficient conditions for tractability of Weighted problems.
  • iv. Necessary and sufficient conditions for tractability of Weighted Problems under new tractability and error criteria.

3. Topics in Scientific Computation:

  • i. Randomness, Pseudorandomness, MC and QMC methods and their applications (Finance, etc)

Yuri Pritykin, 245 Carl Icahn Lab

  • Research interests: Computational biology; Cancer immunology; Regulation of gene expression; Functional genomics; Single-cell technologies.
  • Potential research projects: Development, implementation, assessment and/or application of algorithms for analysis, integration, interpretation and visualization of multi-dimensional data in molecular biology, particularly single-cell and spatial genomics data.

Benjamin Raphael, Room 309  

  • Research interests: Computational biology and bioinformatics; Cancer genomics; Algorithms and machine learning approaches for analysis of large-scale datasets
  • Implementation and application of algorithms to infer evolutionary processes in cancer
  • Identifying correlations between combinations of genomic mutations in human and cancer genomes
  • Design and implementation of algorithms for genome sequencing from new DNA sequencing technologies
  • Graph clustering and network anomaly detection, particularly using diffusion processes and methods from spectral graph theory

Vikram Ramaswamy, 035 Corwin Hall

  • Research areas: Interpretability of AI systems, Fairness in AI systems, Computer vision.
  • Constructing a new method to explain a model / create an interpretable by design model
  • Analyzing a current model / dataset to understand bias within the model/dataset
  • Proposing new fairness evaluations
  • Proposing new methods to train to improve fairness
  • Developing synthetic datasets for fairness / interpretability benchmarks
  • Understanding robustness of models

Ran Raz, Room 240

  • Research Area: Computational Complexity
  • Independent Research Topics: Computational Complexity, Information Theory, Quantum Computation, Theoretical Computer Science

Szymon Rusinkiewicz, Room 406

  • Research Areas: computer graphics; computer vision; 3D scanning; 3D printing; robotics; documentation and visualization of cultural heritage artifacts
  • Research ways of incorporating rotation invariance into computer visiontasks such as feature matching and classification
  • Investigate approaches to robust 3D scan matching
  • Model and compensate for imperfections in 3D printing
  • Given a collection of small mobile robots, apply control policies learned in simulation to the real robots.

Olga Russakovsky, Room 408

  • Research Areas: computer vision, machine learning, deep learning, crowdsourcing, fairness&bias in AI
  • Design a semantic segmentation deep learning model that can operate in a zero-shot setting (i.e., recognize and segment objects not seen during training)
  • Develop a deep learning classifier that is impervious to protected attributes (such as gender or race) that may be erroneously correlated with target classes
  • Build a computer vision system for the novel task of inferring what object (or part of an object) a human is referring to when pointing to a single pixel in the image. This includes both collecting an appropriate dataset using crowdsourcing on Amazon Mechanical Turk, creating a new deep learning formulation for this task, and running extensive analysis of both the data and the model

Sebastian Seung, Princeton Neuroscience Institute, Room 153

  • Research Areas: computational neuroscience, connectomics, "deep learning" neural networks, social computing, crowdsourcing, citizen science
  • Gamification of neuroscience (EyeWire  2.0)
  • Semantic segmentation and object detection in brain images from microscopy
  • Computational analysis of brain structure and function
  • Neural network theories of brain function

Jaswinder Pal Singh, Room 324

  • Research Areas: Boundary of technology and business/applications; building and scaling technology companies with special focus at that boundary; parallel computing systems and applications: parallel and distributed applications and their implications for software and architectural design; system software and programming environments for multiprocessors.
  • Develop a startup company idea, and build a plan/prototype for it.
  • Explore tradeoffs at the boundary of technology/product and business/applications in a chosen area.
  • Study and develop methods to infer insights from data in different application areas, from science to search to finance to others. 
  • Design and implement a parallel application. Possible areas include graphics, compression, biology, among many others. Analyze performance bottlenecks using existing tools, and compare programming models/languages.
  • Design and implement a scalable distributed algorithm.

Mona Singh, Room 420

  • Research Areas: computational molecular biology, as well as its interface with machine learning and algorithms.
  • Whole and cross-genome methods for predicting protein function and protein-protein interactions.
  • Analysis and prediction of biological networks.
  • Computational methods for inferring specific aspects of protein structure from protein sequence data.
  • Any other interesting project in computational molecular biology.

Robert Tarjan, 194 Nassau St., Room 308

Available for single-semester IW and senior thesis advising, 2022-2023

  • Research Areas: Data structures; graph algorithms; combinatorial optimization; computational complexity; computational geometry; parallel algorithms.
  • Implement one or more data structures or combinatorial algorithms to provide insight into their empirical behavior.
  • Design and/or analyze various data structures and combinatorial algorithms.

Olga Troyanskaya, Room 320

  • Research Areas: Bioinformatics; analysis of large-scale biological data sets (genomics, gene expression, proteomics, biological networks); algorithms for integration of data from multiple data sources; visualization of biological data; machine learning methods in bioinformatics.
  • Implement and evaluate one or more gene expression analysis algorithm.
  • Develop algorithms for assessment of performance of genomic analysis methods.
  • Develop, implement, and evaluate visualization tools for heterogeneous biological data.

David Walker, Room 211

  • Research Areas: Programming languages, type systems, compilers, domain-specific languages, software-defined networking and security
  • Independent Research Topics:  Any other interesting project that involves humanitarian hacking, functional programming, domain-specific programming languages, type systems, compilers, software-defined networking, fault tolerance, language-based security, theorem proving, logic or logical frameworks.

Kevin Wayne, Corwin Hall, Room 040

  • Research Areas: design, analysis, and implementation of algorithms; data structures; combinatorial optimization; graphs and networks.
  • Design and implement computer visualizations of algorithms or data structures.
  • Develop pedagogical tools or programming assignments for the computer science curriculum at Princeton and beyond.
  • Develop assessment infrastructure and assessments for MOOCs.

Matt Weinberg, 194 Nassau St., Room 222

  • Research Areas: algorithms, algorithmic game theory, mechanism design, game theoretical problems in {Bitcoin, networking, healthcare}.
  • Theoretical questions related to COS 445 topics such as matching theory, voting theory, auction design, etc. 
  • Theoretical questions related to incentives in applications like Bitcoin, the Internet, health care, etc. In a little bit more detail: protocols for these systems are often designed assuming that users will follow them. But often, users will actually be strictly happier to deviate from the intended protocol. How should we reason about user behavior in these protocols? How should we design protocols in these settings?

Huacheng Yu, Room 310

  • data structures
  • streaming algorithms
  • design and analyze data structures / streaming algorithms
  • prove impossibility results (lower bounds)
  • implement and evaluate data structures / streaming algorithms

Ellen Zhong, Room 314

No longer available for single-term IW  and senior thesis advising, 2023-2024

Opportunities outside the department

We encourage students to look in to doing interdisciplinary computer science research and to work with professors in departments other than computer science.  However, every CS independent work project must have a strong computer science element (even if it has other scientific or artistic elements as well.)  To do a project with an adviser outside of computer science you must have permission of the department.  This can be accomplished by having a second co-adviser within the computer science department or by contacting the independent work supervisor about the project and having he or she sign the independent work proposal form.

Here is a list of professors outside the computer science department who are eager to work with computer science undergraduates.

Maria Apostolaki, Engineering Quadrangle, C330

  • Research areas: Computing & Networking, Data & Information Science, Security & Privacy

Branko Glisic, Engineering Quadrangle, Room E330

  • Documentation of historic structures
  • Cyber physical systems for structural health monitoring
  • Developing virtual and augmented reality applications for documenting structures
  • Applying machine learning techniques to generate 3D models from 2D plans of buildings
  •  Contact : Rebecca Napolitano, rkn2 (@princeton.edu)

Mihir Kshirsagar, Sherrerd Hall, Room 315

Center for Information Technology Policy.

  • Consumer protection
  • Content regulation
  • Competition law
  • Economic development
  • Surveillance and discrimination

Sharad Malik, Engineering Quadrangle, Room B224

Select a Senior Thesis Adviser for the 2020-21 Academic Year.

  • Design of reliable hardware systems
  • Verifying complex software and hardware systems

Prateek Mittal, Engineering Quadrangle, Room B236

  • Internet security and privacy 
  • Social Networks
  • Privacy technologies, anonymous communication
  • Network Science
  • Internet security and privacy: The insecurity of Internet protocols and services threatens the safety of our critical network infrastructure and billions of end users. How can we defend end users as well as our critical network infrastructure from attacks?
  • Trustworthy social systems: Online social networks (OSNs) such as Facebook, Google+, and Twitter have revolutionized the way our society communicates. How can we leverage social connections between users to design the next generation of communication systems?
  • Privacy Technologies: Privacy on the Internet is eroding rapidly, with businesses and governments mining sensitive user information. How can we protect the privacy of our online communications? The Tor project (https://www.torproject.org/) is a potential application of interest.

Ken Norman,  Psychology Dept, PNI 137

  • Research Areas: Memory, the brain and computation 
  • Lab:  Princeton Computational Memory Lab

Potential research topics

  • Methods for decoding cognitive state information from neuroimaging data (fMRI and EEG) 
  • Neural network simulations of learning and memory

Caroline Savage

Office of Sustainability, Phone:(609)258-7513, Email: cs35 (@princeton.edu)

The  Campus as Lab  program supports students using the Princeton campus as a living laboratory to solve sustainability challenges. The Office of Sustainability has created a list of campus as lab research questions, filterable by discipline and topic, on its  website .

An example from Computer Science could include using  TigerEnergy , a platform which provides real-time data on campus energy generation and consumption, to study one of the many energy systems or buildings on campus. Three CS students used TigerEnergy to create a  live energy heatmap of campus .

Other potential projects include:

  • Apply game theory to sustainability challenges
  • Develop a tool to help visualize interactions between complex campus systems, e.g. energy and water use, transportation and storm water runoff, purchasing and waste, etc.
  • How can we learn (in aggregate) about individuals’ waste, energy, transportation, and other behaviors without impinging on privacy?

Janet Vertesi, Sociology Dept, Wallace Hall, Room 122

  • Research areas: Sociology of technology; Human-computer interaction; Ubiquitous computing.
  • Possible projects: At the intersection of computer science and social science, my students have built mixed reality games, produced artistic and interactive installations, and studied mixed human-robot teams, among other projects.

David Wentzlaff, Engineering Quadrangle, Room 228

Computing, Operating Systems, Sustainable Computing.

  • Instrument Princeton's Green (HPCRC) data center
  • Investigate power utilization on an processor core implemented in an FPGA
  • Dismantle and document all of the components in modern electronics. Invent new ways to build computers that can be recycled easier.
  • Other topics in parallel computer architecture or operating systems


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Graphics and Vision

Human-computer interaction.

  • Information Science and Systems
  • Integrated Circuits and Systems
  • Nanoscale Materials, Devices, and Systems
  • Natural Language and Speech Processing
  • Optics + Photonics
  • Optimization and Game Theory

Programming Languages and Software Engineering

Quantum computing, communication, and sensing, security and cryptography.

  • Signal Processing

Systems and Networking

  • Systems Theory, Control, and Autonomy

Theory of Computation

  • Departmental History
  • Departmental Organization
  • Visiting Committee
  • Explore all research areas

topics for research computer science

Computer science deals with the theory and practice of algorithms, from idealized mathematical procedures to the computer systems deployed by major tech companies to answer billions of user requests per day.

Primary subareas of this field include: theory, which uses rigorous math to test algorithms’ applicability to certain problems; systems, which develops the underlying hardware and software upon which applications can be implemented; and human-computer interaction, which studies how to make computer systems more effectively meet the needs of real people. The products of all three subareas are applied across science, engineering, medicine, and the social sciences. Computer science drives interdisciplinary collaboration both across MIT and beyond, helping users address the critical societal problems of our era, including opportunity access, climate change, disease, inequality and polarization.

Research areas

Our goal is to develop AI technologies that will change the landscape of healthcare. This includes early diagnostics, drug discovery, care personalization and management. Building on MIT’s pioneering history in artificial intelligence and life sciences, we are working on algorithms suitable for modeling biological and clinical data across a range of modalities including imaging, text and genomics.

Our research covers a wide range of topics of this fast-evolving field, advancing how machines learn, predict, and control, while also making them secure, robust and trustworthy. Research covers both the theory and applications of ML. This broad area studies ML theory (algorithms, optimization, 
), statistical learning (inference, graphical models, causal analysis, 
), deep learning, reinforcement learning, symbolic reasoning ML systems, as well as diverse hardware implementations of ML.

We develop the next generation of wired and wireless communications systems, from new physical principles (e.g., light, terahertz waves) to coding and information theory, and everything in between.

We bring some of the most powerful tools in computation to bear on design problems, including modeling, simulation, processing and fabrication.

We design the next generation of computer systems. Working at the intersection of hardware and software, our research studies how to best implement computation in the physical world. We design processors that are faster, more efficient, easier to program, and secure. Our research covers systems of all scales, from tiny Internet-of-Things devices with ultra-low-power consumption to high-performance servers and datacenters that power planet-scale online services. We design both general-purpose processors and accelerators that are specialized to particular application domains, like machine learning and storage. We also design Electronic Design Automation (EDA) tools to facilitate the development of such systems.

Educational technology combines both hardware and software to enact global change, making education accessible in unprecedented ways to new audiences. We develop the technology that makes better understanding possible.

The shared mission of Visual Computing is to connect images and computation, spanning topics such as image and video generation and analysis, photography, human perception, touch, applied geometry, and more.

The focus of our research in Human-Computer Interaction (HCI) is inventing new systems and technology that lie at the interface between people and computation, and understanding their design, implementation, and societal impact.

We develop new approaches to programming, whether that takes the form of programming languages, tools, or methodologies to improve many aspects of applications and systems infrastructure.

Our work focuses on developing the next substrate of computing, communication and sensing. We work all the way from new materials to superconducting devices to quantum computers to theory.

Our research focuses on robotic hardware and algorithms, from sensing to control to perception to manipulation.

Our research is focused on making future computer systems more secure. We bring together a broad spectrum of cross-cutting techniques for security, from theoretical cryptography and programming-language ideas, to low-level hardware and operating-systems security, to overall system designs and empirical bug-finding. We apply these techniques to a wide range of application domains, such as blockchains, cloud systems, Internet privacy, machine learning, and IoT devices, reflecting the growing importance of security in many contexts.

From distributed systems and databases to wireless, the research conducted by the systems and networking group aims to improve the performance, robustness, and ease of management of networks and computing systems.

Theory of Computation (TOC) studies the fundamental strengths and limits of computation, how these strengths and limits interact with computer science and mathematics, and how they manifest themselves in society, biology, and the physical world.

topics for research computer science

Latest news

Computational imaging researcher attended a lecture, found her career.

A pivotal talk led postdoc Kristina Monakhova to develop smart, computational cameras and microscopes for intelligent systems.

Student Spotlight: Isabella Pedraza Piñeros

Our first subject, Isabella Pedraza Piñeros, is a first-year MEng student in the Department of EECS; she graduated with her degree in Computer Science and Engineering in the spring of 2023.

Accelerating AI tasks while preserving data security

The SecureLoop search tool efficiently identifies secure designs for hardware that can boost the performance of complex AI tasks, while requiring less energy.

Forging climate connections across the Institute

Inaugural Fast Forward Faculty Fund grants aim to spur new work on climate change and deepen collaboration at MIT.

Using language to give robots a better grasp of an open-ended world

By blending 2D images with foundation models to build 3D feature fields, a new MIT method helps robots understand and manipulate nearby objects with open-ended language prompts.

Upcoming events

Doctoral thesis: a simulated annealing approach to designing optimal decision trees for classification, prescriptive, and survival analysis.

Research Opportunities

Undergraduate research in computer science.

For specific information on undergraduate research opportunities in Computer Science visit  https://csadvising.seas.harvard.edu/research/ .

General Information about Undergraduate Research

Opportunities for undergraduates to conduct research in engineering, the applied sciences, and in related fields abound at Harvard. As part of your coursework, or perhaps as part of individual research opportunities working with professors, you will have the chance to  take part in or participate in  some extraordinary projects covering topics ranging from bioengineering to cryptography to environmental engineering.

Our dedicated undergraduate research facilities and Active Learning Labs also provide opportunities for students to engage in hands-on learning. We encourage undergraduates from all relevant concentrations to tackle projects during the academic year and/or over the summer.

Keep in mind, many students also pursue summer research at private companies and labs as well as at government institutions like the National Institutes of Health.

If you have any questions, please contact or stop by the Office of Academic Programs, located in the Science and Engineering Complex, Room 1.101, in Allston.

Research FAQs

The SEAS website has a wealth of information on the variety of cross-disciplinary research taking place at SEAS. You can view the concentrations available at SEAS here , as well as the research areas that faculty in these concentrations participate in. Note that many research areas span multiple disciplines; participating in undergraduate research is an excellent way to expand what you learn beyond the content of the courses in your concentration! 

To view which specific faculty conduct research in each area, check out the All Research Areas section of the website. You can also find a helpful visualization tool to show you the research interests of all the faculty at SEAS, or you can filter the faculty directory by specific research interests. Many faculty’s directory entry will have a link to their lab’s website, where you can explore the various research projects going on in their lab.

The Centers & Initiatives page shows the many Harvard research centers that SEAS faculty are members of (some based at SEAS, some based in other departments at Harvard). 

Beyond the website, there are plenty of research seminars and colloquia happening all year long that you can attend to help you figure out what exactly you are interested in. Keep an eye on the calendar at https://events.seas.harvard.edu ! 

There are several events that are designed specifically for helping undergraduate students get involved with research at SEAS, such as the Undergraduate Research Open House and Research Lightning Talks . This event runs every fall in early November and is a great opportunity to talk to representatives from research labs all over SEAS. You can find recordings from last year’s Open House on the SEAS Undergraduate Research Canvas site .

Most of our faculty have indicated that curiosity, professionalism, commitment and an open mind are paramount. Good communication skills, in particular those that align with being professional are critical. These skills include communicating early with your mentor if you are going to be late to or miss a meeting, or reaching out for help if you are struggling to figure something out. Good writing skills and math (calculus in particular) are usually helpful, and if you have programming experience that may be a plus for many groups. So try to take your math and programming courses early (first year) including at least one introductory concentration class, as those would also add to your repertoire of useful skills.

Adapted from the Life Sciences Research FAQs

Start by introducing yourself and the purpose of your inquiry (e.g. you’d like to speak about summer research opportunities in their lab). Next, mention specific aspects of their research and state why they interest you (this requires some background research on your part). Your introduction will be stronger if you convey not only some knowledge of the lab’s scientific goals, but also a genuine interest in their research area and technical approaches.

In the next paragraph tell them about yourself, what your goals are and why you want to do research with their group. Describe previous research experience (if you have any). Previous experience is, of course, not required for joining many research groups, but it can be helpful. Many undergraduates have not had much if any previous experience; professors are looking for students who are highly motivated to learn, curious and dependable.

Finally, give a timeline of your expected start date, how many hours per week you can devote during the academic term, as well as your summer plans.

Most faculty will respond to your email if it is clear that you are genuinely interested in their research and have not simply sent out a generic email. If you don’t receive a response within 7-10 days, don’t be afraid to follow up with another email. Faculty are often busy and receive a lot of emails, so be patient.

There are several ways that undergraduate research can be funded at SEAS. The Program for Research in Science and Engineering ( PRISE ) is a 10-week summer program that provides housing in addition to a stipend for summer research. The Harvard College Research Program ( HCRP ) is available during the academic year as well as the summer.  The Harvard University Center for the Environment ( HUCE ) has a summer undergraduate research program. The Harvard College Office of Undergraduate Research and Fellowships ( URAF ) has more information on these, as well as many other programs.

Students that were granted Federal Work Study as part of their financial aid package can use their Work Study award to conduct undergraduate research as well (research positions should note that they are work-study eligible to utilize this funding source).  

Research labs may have funding available to pay students directly, though we encourage you to seek out one of the many funding options available above first.

Yes! Some students choose to do research for course credit instead of for a stipend. To do so for a SEAS concentrations, students must enroll in one of the courses below and submit the relevant Project Application Form on the Course’s Canvas Page:

  • Applied Mathematics 91r (Supervised Reading and Research)
  • Computer Science 91r (Supervised Reading and Research)
  • Engineering Sciences 91r (Supervised Reading and Research)

In general, you should expect to spend a minimum of one semester or one summer working on a project. There are many benefits to spending a longer period of time dedicated to a project. It’s important to have a conversation early with your research PI (“Principal Investigator”, the faculty who runs your research lab or program) to discuss the intended timeline of the first phase of your project, and there will be many additional opportunities to discuss how it could be extended beyond that.

For students who are satisfied with their research experience, remaining in one lab for the duration of their undergraduate careers can have significant benefits. Students who spend two or three years in the same lab often find that they have become fully integrated members of the research group. In addition, the continuity of spending several years in one lab group often allows students to develop a high level of technical expertise that permits them to work on more sophisticated projects and perhaps produce more significant results, which can also lead to a very successful senior thesis or capstone design project. 

However, there is not an obligation to commit to a single lab over your time at Harvard, and there are many reasons you may consider a change:

  • your academic interests or concentration may have changed and thus the lab project is no longer appropriate
  • you would like to study abroad (note that there is no additional cost in tuition for the term-time study abroad and Harvard has many fellowships for summer study abroad programs)
  • your mentor may have moved on and there is no one in the lab to direct your project (it is not unusual for a postdoctoral fellow who is co-mentoring student to move as they secure a faculty position elsewhere)
  • the project may not be working and the lab hasn’t offered an alternative
  • or there may be personal reasons for leaving.  It is acceptable to move on

If you do encounter difficulties, but you strongly prefer to remain in the lab, get help.  Talk to your PI or research mentor, your faculty advisor or concentration advisor, or reach out to [email protected] for advice. The PI may not be aware of the problem and bringing it to their attention may be all that is necessary to resolve it.

Accepting an undergraduate into a research group and providing training for them is a very resource-intensive proposition for a lab, both in terms of the time commitment required from the lab mentors as well as the cost of laboratory supplies, reagents, computational time, etc. It is incumbent upon students to recognize and respect this investment.

  • One way for you to acknowledge the lab’s investment is to show that you appreciate the time that your mentors set aside from their own experiments to teach you. For example, try to be meticulous about letting your mentor know well in advance when you are unable to come to the lab as scheduled, or if you are having a hard time making progress. 
  • On the other hand, showing up in the lab at a time that is not on your regular schedule and expecting that your mentor will be available to work with you is unrealistic because they may be in the middle of an experiment that cannot be interrupted for several hours. 
  • In addition to adhering to your lab schedule, show you respect the time that your mentor is devoting to you by putting forth a sincere effort when you are in the lab.  This includes turning off your phone, ignoring text messages, avoiding surfing the web and chatting with your friends in the lab etc. You will derive more benefit from a good relationship with your lab both in terms of your achievements in research and future interactions with the PI if you demonstrate a sincere commitment to them.
  • There will be “crunch” times, maybe even whole weeks, when you will be unable to work in the lab as many hours as you normally would because of midterms, finals, paper deadlines, illness or school vacations. This is fine and not unusual for students, but remember to let your mentor know in advance when you anticipate absences. Disappearing from the lab for days without communicating with your mentor is not acceptable. Your lab mentor and PI are much more likely to be understanding about schedule changes if you keep the lines of communication open but they may be less charitable if you simply disappear for days or weeks at a time. From our conversations with students, we have learned that maintaining good communication and a strong relationship with the lab mentor and/or PI correlates well with an undergraduate’s satisfaction and success in the laboratory.
  • Perhaps the best way for you to demonstrate your appreciation of the lab’s commitment is to approach your project with genuine interest and intellectual curiosity. Regardless of how limited your time in the lab may be, especially for first-years and sophomores, it is crucial to convey a sincere sense of engagement with your project and the lab’s research goals. You want to avoid giving the impression that you are there merely to fulfill a degree requirement or as a prerequisite for a post-graduate program.

There are lots of ways to open a conversation around how to get involved with research.

  • For pre-concentrators: Talk to a student who has done research. The Peer Concentration Advisor (PCA) teams for Applied Math , Computer Science and Engineering mention research in their bios and would love to talk about their experience. Each PCA team has a link to Find My PCA which allows you to be matched with a PCA based on an interest area such as research. 
  • For SEAS concentrators: Start a conversation with your ADUS, DUS, or faculty advisor about faculty that you are interested in working with. If you don’t have a list already, start with faculty whose courses you have taken or faculty in your concentration area. You may also find it helpful to talk with graduate student TFs in your courses about the work they are doing, as well as folks in the Active Learning Labs, as they have supported many students working on research and final thesis projects.
  • For all students: Attend a SEAS Research Open House event to be connected with lab representatives that are either graduate students, postdocs, researchers or the PI for the labs. If you can’t attend the event, contact information is also listed on the Undergraduate Research Canvas page for follow-up in the month after the event is hosted. 

For any student who feels like they need more support to start the process, please reach out to [email protected] so someone from the SEAS Taskforce for Undergraduate Research can help you explore existing resources on the Undergraduate Research Canvas page . We especially encourage first-generation and students from underrepresented backgrounds to reach out if you have any questions.

In Computer Science

  • First-Year Exploration
  • Concentration Information
  • Secondary Field
  • Senior Thesis
  • AB/SM Information
  • Student Organizations
  • How to Apply
  • PhD Timeline
  • PhD Course Requirements
  • Qualifying Exam
  • Committee Meetings (Review Days)
  • Committee on Higher Degrees
  • Research Interest Comparison
  • Collaborations
  • Cross-Harvard Engagement
  • Lecture Series
  • Clubs & Organizations
  • Centers & Initiatives
  • Alumni Stories

Put a stop to deadline pressure, and have your homework done by an expert.

201 Computer Science Topics (Updated for 2023)

computer science topics

If you are reading this, you are surely in need of some excellent computer science topics. The good news is that you have arrived at the right place. We have 201 brand new computer science topics that should work great in 2023. The even better news is that each and every one of these research topics in computer science is 100% free to use. You can reword our topics or use them as they are; it’s up to you.

Wondering why you need computer science research paper topics? The truth is that by using the best research paper topics in computer science, you get the chance to win some bonus points from your professor.

After all, who wouldn’t want to read papers on interesting topics in computer science or on some awesome computer science ethics topics? Your professor is bored of reading essays on the same old topics over and over again. Bring something fresh to the table and you’ll immediately stand out from your classmates. If the scope of your work allows, you can also check our technology topics . Without further ado, here is our list of research topics in computer science.

Best Computer Science Research Topics

Writing a research paper can be tough if you don’t pick the right topic. Here are some of the best computer science research topics you can find in 2023:

  • How important is machine learning?
  • The latest advancements in quantum computing
  • The next level of the Internet
  • Virtualization technologies
  • Real-life applications of bioinformatics
  • Introducing computer science in high school
  • Define deep learning
  • Describe MIMO OFDM wireless communication

Easy Topics in Computer Science

If you don’t want to spend too much time working on your paper, we suggest you pick one of our easy topics in computer science:

  • What is a computer virus?
  • Explain the TCP/IP protocol
  • Explain how the microprocessor works
  • The role of Random Access Memory
  • Protecting endangered species using computers
  • Describe a virtual reality device
  • How does machine learning work?
  • Explain the term “Neural Network”

Computer Security Topics

There are literally thousands of topics to discuss when it comes to computer security. We managed to narrow down the list of computer security topics to only the most interesting of them:

  • Top 3 most dangerous viruses
  • What is a Trojan Horse?
  • The role of antivirus/firewall solutions
  • In-depth analysis of ransomware
  • Everything about the Mydoom virus
  • Virus propagation methods
  • The most secure passwords
  • In-depth analysis of mobile protection

Hot Topics in Computer Science

Are you looking for the newest and most interesting topics? If you are, you should check out our hot topics in computer science:

  • What is the Internet of Things?
  • Semantic Web
  • Bioinformatics
  • Latest image processing algorithms
  • Machine Learning
  • Latest in Cloud Computing
  • Artificial Intelligence breakthroughs
  • Quantum Computing

Computer Science Research Topics for Undergraduates

Undergrads should avoid wasting time searching for topics and simply pick one of these free computer science research topics for undergraduates:

  • Data Warehousing for the financial sector
  • IoT applications in healthcare
  • Data Warehousing in the retail services sector
  • IoT applications in manufacturing
  • Big data applications/algorithms
  • SaaS vs PaaS
  • Mobile ad hoc networks for vehicles
  • Data mining: The Genetic algorithm

AP Computer Science Topics

Mastering the AP Computer Science A programming class is not easy. Fortunately, we have some AP computer science topics that can help you write a great paper in no time:

  • Discuss computer-assisted education
  • Novel emerging technologies in computer science
  • Open-source vs. proprietary software
  • Natural language processing real life applications
  • Evaluating 3D models effectively
  • Internet of Things applications in the medical field
  • Discuss data security in a pharmacy
  • What is mesh generation and what are its applications?

Computer Ethics Topics

Yes, there is such a thing as computer ethics. In fact, the subject is pretty vast, so you have plenty of computer ethics topics to choose from:

  • Ethic problems with data mining
  • Machine learning issues
  • Internet of Things controversies
  • AI security concerns
  • Image processing and privacy concerns
  • Quantum computing ethic problems
  • Controversies surrounding robots
  • Internet censorship

Computer Science and Robotics Topics

Computer science can be tightly linked to advances in robotics, so why don’t you write about one of our computer science and robotics topics:

  • Social robots
  • Machine learning in robotics
  • Robot vision: AI applications
  • Autonomous cars
  • Are computers taking our jobs?
  • Robots in the healthcare sector
  • AI in the Mars Rovers
  • Programming an industrial robot

Best Project Topics for Computer Science Student

If you are looking for the best project topics for computer science student, you are in luck. We have exactly what you need:

  • Write a program in Java
  • Create a Website in PHP and MySQL
  • Write an Android app
  • Explore Microsoft Mobile app ideas
  • Graphical Interfaces in Java
  • Java Server Faces applications
  • Create a relational database
  • Create a Web app

Controversial Topics in Computer Science

There are many controversial topics in computer science, but we managed to pick the best ones. Use any of them for free:

  • Robots are stealing our jobs
  • Privacy concerns on the Internet
  • How safe are social networking platforms?
  • Policing the Internet
  • The role of the dark net
  • Corporations using personal data
  • Targeted ads
  • Tracking cookies

Evolution of Computers Topics

If you are interested in writing about how things evolved since the first computers appeared on the market, we have some interesting evolution of computers topics for you:

  • Describe the Fiber Distributed Data Interface (FDDI)
  • What is a firewall and how does it work?
  • What is an ExpressCard?
  • How does an adapter card work?
  • From the first computer to quantum computing
  • The history of the Cloud
  • The evolution of Denial of Service attacks
  • Quantum computers

Computer Architecture Research Topics

Interested in discussing the functionality, organization and implementation of computer systems? You need our computer architecture research topics:

  • What are reduced instruction set computers?
  • Describe synchronous design
  • Parallel hardware systems
  • The Sun SPARK architecture
  • Analyze data-driven nets
  • Discuss functional programming methods
  • Discuss micropipelines
  • The Von Neumann architecture

Computer Science Thesis Topics

If you need to write a thesis in computer science, our writers have some excellent computer science thesis topics for you. Choose one:

  • Quantum computing advancements
  • The role of big data in the banking sector
  • Artificial intelligence and computer security
  • An in-depth analysis of an antivirus tool
  • Image processing algorithms
  • Discuss model-based reflex agents (AI)
  • Discuss fuzzy logic systems
  • Data mining in governmental agencies

Internet of Things Ideas

You’ve probably heard about the IoT, but didn’t really bother to investigate. Check out these Internet of Things ideas and impress your professor:

  • The concept of a smart home
  • What is the IoT?
  • Internet of Things applications
  • Internet of Things in manufacturing
  • Product flow monitoring
  • IoT in Quality Control
  • The Ring doorbell camera
  • Video streaming with IoT

Quantum Computing Ideas

Truth be told, quantum computing is one of the hottest ideas and works great for 2023. Pick one of our quantum computing ideas for free:

  • Discuss a quantum algorithm
  • What is quantum computing?
  • Discuss adiabatic optimization
  • Discuss quantum annealing
  • Cryptography in quantum computing
  • 5 requirements for quantum computing
  • Quantum computing and financial modeling
  • Implications for Artificial Intelligence

Computer Science Project Topics

So, you are interested in starting a computer science project. Pick one of these computer science project topics for free right now:

  • Securing a workstation
  • Face detection application
  • An Android battery safer system
  • Create your very own search engine
  • Write a group chat app in Java
  • Selenium browser automation applications
  • Mitigate a DDoS attack
  • Load balancing applications

Computer Engineering Research Topics

Researching good computer engineering topics can take hours. Why waste your time when we have some computer engineering research topics right here:

  • Clustering in data mining
  • The advantages of data mining
  • The disadvantages of using big data
  • Artificial intelligence in security applications
  • Strong AI vs. Weak AI
  • Pattern measurement in image processing
  • Computer-aided image restoration methods
  • DNA/RNA simulations using bioinformatics

Interesting Computer Science Topics

Want to make sure your professor notices your paper? No problem! Simply pick one of these interesting computer science topics:

  • Green cloud computing
  • Spectral clustering in data mining
  • Fraud detection using big data
  • AI uses of computer vision
  • CNN Advanced Machine Learning
  • Augmented reality vs. virtual reality

Computer Networks Topics

Writing about networks and networking never gets old. We have some highly interesting computer networks topics just for you:

  • Create a network (practical project)
  • Network security best practices
  • The IPv6 protocol
  • The TCP/IP protocol
  • How does the Internet work?
  • Banking computer networks

Current Topics in Computer Science

You are probably interested in writing about the newest and hottest topics, so here are some current topics in computer science:

  • Define data science
  • The 5G network
  • What are swarm robots?
  • NoSQL databases
  • Programs creating programs
  • Using computer science in biology

Cool Computer Security Research Topics

Do you want to impress your professor and secure a top grade? Pick one of our cool computer security research topics:

  • Humans: the weak link in network security
  • Analyzing the top 3 online scams
  • Discuss endpoint security best practices
  • IoT security
  • What is a cyber security audit?
  • Best algorithm for data encryption
  • Ransomware and ways to prevent it
  • Discuss unified user profiles

Computer Science Presentation Topics

Our team of ENL writers managed to put together an excellent list of computer science presentation topics for you:

  • Importance of biometrics in computer security
  • Windows vs. Linux vs. MacOS security
  • DDoS attacks on vehicular ad hoc networks
  • Describe a software-defined network
  • Using artificial neural networks effectively
  • Model-free versus model-based reinforcement learning
  • The future of wireless: the 5G network

PhD Research Topics in Computer Science

Are you looking to start on your PhD but don’t know which topic to choose? We have some ideas of PhD research topics in computer science you might like:

  • Database architecture: literature review
  • Develop a novel search algorithm
  • Creating a new network protocol
  • Advanced computer vision in robots
  • Write a deep learning algorithm
  • Solving the decoherence problem with quantum computers

Computer Forensics Research Paper Topics

Ever wonder how law enforcement manages to catch cyber criminals? We have some of the best computer forensics research paper topics right here:

  • Recovering data for computer forensics
  • Identifying users on the darknet
  • The Fourth Amendment and computer forensics
  • Discuss digital steganography
  • What is criminal profiling?
  • Cyber surveillance

Artificial Intelligence Topics

AI is what everyone’s talking about right now, so it’s the perfect topic for 2023. Fortunately, we have some very nice artificial intelligence topics:

  • What is reinforcement learning?
  • Discuss recommender systems
  • The 4 types of AI
  • Is AI dangerous?
  • Predicting housing price using AI
  • AI in the medical profession

Interesting Cyber Security Ideas

Are you interested in cyber security? It’s an awesome field, we have to admit. Pick one of these interesting cyber security ideas and start writing:

  • Best anti-virus system today
  • Discuss secure passwords
  • What is a brute force attack?
  • The human element in cyber security
  • Security awareness
  • Working remotely security solutions

Trends in Computer Science Topics

If you like to analyze trends, computer science is one of the best subjects to try your hand on. Take a look at our trends in computer science topics:

  • The appearance of Mini-VGA connectors
  • Discuss multiprocessing in 2023
  • How was the Small-Scale Experimental Machine built?
  • Computers and education in 2023
  • Computers and medicine in 2023
  • The evolution of computing power
  • What is a neural computer?
  • The Internet of Things in 2023

Need More Computer Topics?

Didn’t find the computer topics you were looking for? No problem! In addition to our computer architecture topics, computer science controversial topics and PhD research topics in computer science, we can help students with many others. Professionals providing computer science homework help can quickly put together a list of unique computer related topics for you. All you have to do is ask.

If you need more computer science topics for research or if you just need some simple computer science essay topics, don’t hesitate to contact us. We can send you a list of original computer research topics in no time. Each one of our topics can win you a top grade.

So, what are you waiting for? Get your list of computer science research papers topics right now. Get in touch with us!

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100 Computer Science Topics For You Next Project

Computer Science Topics

Table of Contents

Great computer science topics, interesting topics in computer science, computer science research topics for undergraduates, controversial topics in computer science, ph.d. research topics in computer science.

Are you looking for the best computer science topics for academic papers or essays? If yes, you’ve come to the right place. Here, you will find a list of research topics in computer science from which you can choose what to write about. Computer science entails the study of computational systems and computers. The work of computer scientists mostly revolves around software systems and software. This includes design, development, theory, and application.

Since computer science keeps evolving, new computer science research topics are always emerging. Educators ask students to write academic papers and essays on these topics to familiarize themselves with the subject. However, some learners have difficulties choosing research topics in computer science. That’s because they have many options to consider and these can bombard them. If you feel overwhelmed by the many computer science project topics that you have to choose from, consider this list of the best ideas from our experts who can get your homework done in no time.

To impress your educator and earn a superior grade, you need a captivating topic in computer science. This category has some of the best topics that will capture the attention of your educator and compel them to award you the top grade.

  • What are search algorithms?
  • Explain the evolution of search algorithms
  • Discuss the hazards of most computer viruses
  • Is SCRUM methodology the best computer science invention?
  • How useful is networking in the development of future computer systems?
  • How has artificial intelligence evolved over the years?
  • How unique is software development for mobile gadgets?
  • What are the pros and cons of cloud storage?
  • Discuss the limits of computation and communication
  • How can computer data security be improved?
  • Discuss database management and architecture
  • Explain the relationship between computer science and medicine
  • Discuss the relationship between computer science and biotechnology
  • Discuss privacy, memory, and security in the cloud storage era
  • Give an overview of quantum computing
  • What is the future of quantum computing?
  • How can DDOS attacks be prevented?
  • Discuss the DDOS attack hazard globally
  • Why is having several programming languages important?
  • How important is usability when it comes to human-computer interactions?

These are great research topics in computer science that will earn you the top grade if you research extensively and write your paper well. Nevertheless, pick a topic in this category if you find it interesting.

Perhaps, you’re looking for an interesting research topic in computer science for your paper or essay. Maybe you need a topic that will enable you to learn more about something you’re interested in while researching and writing. In that case, choose one of these interesting computer science research papers topics.

  • Discuss the connection between human perception and virtual reality
  • Discuss computer-assisted education’s future
  • Discuss high-dimensional data modeling and computer science
  • Explain the use of artificial intelligence and blockchain for algorithmic regulations
  • Computer science: Declarative versus imperative languages
  • Discuss blockchain technology and the banking industry
  • Parallel computing and languages- Discuss
  • Discuss the use of mesh generation in computational domains
  • How can a persistent data structure be optimized?
  • Explain the effects of machine architecture on the coding efficiency
  • How can phishing be eliminated?
  • Provide an overview of software security
  • What are the most efficient protocols for cryptographic
  • Explain the effects of computational thinking on science
  • Network economics and game theory
  • Discuss the systems programming languages development
  • Discuss the computer graphics development
  • Cyber-physical system versus sensor networks
  • What is the non-photorealistic rendering case in computer science?
  • Discuss the programming language and floating-point

If looking for interesting computer science topics from which you can get ideas for your thesis title, consider this category. You can also get a great topic for your speech in this category. Nevertheless, choose a topic that you will be happy to research and write about.

If pursuing an undergraduate program in computer science, you need a topic for your research project. The topic that you choose should help you accomplish your study goals. Here are some of the best undergraduate topics in computer science.

  • Can computers understand natural and human language?
  • Is two-way verification a premium technology for ensuring computer or internet security?
  • How HTML5 technology affects websites
  • What role do computers play in the development of operations research?
  • What is the Internet of Things?
  • How does the Internet of Things impact human life?
  • Can AI diagnosis systems be an alternative to doctors?
  • What are the benefits of VOIP phone systems?
  • Can data mining help in fighting crime?
  • What are the advantages and disadvantages of open-source software?
  • Discuss the advanced web design technology and how it benefits visually impaired persons
  • Discuss the applications and roles of artificial intelligence
  • How important are micro-chips in lost pets tracing?
  • How computer science help us understand time travel
  • Computer gaming and virtual reality
  • Discuss the advantages and disadvantages of blockchain technology
  • Analyze ATMs and advanced bank security
  • Advantages and disadvantages of Biometric systems
  • How to improve human-computer interactions
  • Discuss the advancement and evolution of torrents in the data sharing field
  • Discuss the quality elements in digital forensics
  • Explain the relationship between computer games and physics
  • Discuss computer programs and programming- How does it work?
  • What is ethical hacking?
  • Is ethical hacking important?
  • Discuss advanced computer programs and programming systems
  • How important is big data analysis for an established business?
  • Analyze the neutral networks and deep learning
  • Discuss the fate of robotics, computers, and computing in the next ten years
  • How do search engine algorithms work?

These are great computer science research paper topics for undergraduate students who chose computer science as major . But, each of these computer science essay topics requires extensive research and careful analysis of information. Therefore, be ready to spend some hours working if you choose any of these computer science topics for research.

Maybe you need a topic that will elicit mixed reactions from the audience. In that case, choose what to write about from these computer science controversial topics.

  • Discuss the long-term effects of using computers for a long time.
  • What are the negative and positive effects of growing up in a computer-driven world?
  • Is there an OS that providers more privacy protection to people that use public internet services?
  • What potential threats do the new computer viruses have?
  • How does virtual reality impact human perception?
  • What are the pros and cons of virtual reality?
  • Explain the challenges facing data security
  • Over-reliance on computers has made people less social
  • Online medicine applications cannot substitute real doctors
  • Discuss the future of the 5G wireless systems

These may be controversial research paper topics in computer science but they are interesting to research, write and read about. The most important thing is to take the time to research and analyze information before you write a paper or essay on any of these topics.

Do you need a topic for your post-graduate dissertation? If yes, this category has the best computer science thesis topics that you can choose from.

  • Discuss the ethical questions that surround the use of big data banks to store human DNA
  • Explain the ability of computers to process information faster than human brains
  • Will human workers become obsolete due to the continued use of computer technologies?
  • Are companies likely to embrace computer technologies more to run businesses after the coronavirus pandemic?
  • Explain the role of computer science in solving health problems
  • Discuss the future of quantum computers in detail
  • Discuss how computer viruses work and the hazards associated with them
  • How can robotics and artificial intelligence be used to enhance human capabilities?
  • How effective is computer-assisted education?
  • How to approach education using the latest computing technologies
  • Discuss the business process modeling technology
  • How does big data analytics work?
  • Discuss how machine learning and pattern recognition works
  • How can machine learning be used in the analysis of medical images?
  • Discuss distributed computing and algorithms
  • Audio, language, and speech processing
  • Computer security and forensics
  • Communication and computation limits
  • Environments and programming languages
  • Computer systems security and support for the digital democracy

This list comprises hot topics in computer science. Pick one of them and research it extensively to write a brilliant academic paper or essay.

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100+ Computer Science Topics: A Comprehensive Guide

computer science topics

Computer Science is a vast and dynamic field that plays a fundamental role in today’s technological landscape. This blog aims to provide an overview of various computer science topics, from core concepts to specialized areas and emerging trends. 

Whether you’re a student considering a computer science degree or someone interested in the latest developments in technology, this guide will help you navigate the world of computer science.

What Are The Core Concepts of Computer Science?

Table of Contents

Algorithms and Data Structures

At the heart of computer science lies the study of algorithms and data structures. Algorithms are step-by-step procedures for solving problems, and data structures are the ways we organize and store data. 

They are crucial for problem-solving and efficient software development. Understanding algorithms and data structures is fundamental for any computer scientist.

Popular data structures include arrays, linked lists, trees, and hash tables, while common algorithms encompass sorting, searching, and graph algorithms. The data structure and method used can have a big influence on how well software runs.

Programming Languages

Computer science relies on a multitude of programming languages. From classics like C, C++, and Java to modern languages like Python and JavaScript, each language has its strengths and weaknesses. 

The choice of programming language is based on the particular task at hand as well as elements like usability, performance, and library accessibility.

Learning multiple languages can make you a versatile programmer and open doors to different job opportunities. For instance, web development often requires JavaScript, while data science frequently employs Python.

How To Select Computer Science Topics?

Selecting computer science topics can be a daunting task, given the vastness of the field. Here are 10 steps to help you choose the right computer science topics:

  • Identify Your Interests: Start by reflecting on one’s interests within computer science. Are you passionate about artificial intelligence, web development, cybersecurity, or data science? Knowing what excites you will make the selection process more manageable.
  • Assess Your Knowledge: Consider your current knowledge and experience. If you’re a beginner, you may want to explore foundational topics like algorithms and data structures. For more advanced learners, specialized or emerging topics might be suitable.
  • Research Current Trends: Stay updated (with trends) on the latest trends and emerging technologies in computer science. Read blogs, research papers, and news articles to understand what’s hot in the field. Topics like blockchain, quantum computing, and AI ethics are currently trending.
  • Consider Your Career Goals: Think about your long-term career goals. If you aspire to become a data scientist, topics related to machine learning, data analysis, and big data are relevant. Tailor your choices to align with your career aspirations.
  • Consult with Professors or Mentors: If you’re a student, reach out to your professors or mentors for guidance. They can recommend topics that match your skills and career goals and may even suggest research opportunities.
  • Explore Core Concepts: Ensure you have a strong foundation by exploring core computer science concepts like algorithms, data structures, and programming languages. These fundamentals are essential for building expertise in other areas.
  • Assess Practicality: Consider the practicality of the topic. Some topics may have limited real-world applications, while others can lead to tangible projects or research. Choose topics that allow you to apply your knowledge.
  • Review Project Opportunities: If you’re looking to gain hands-on experience, assess the availability of projects related to your chosen topic. Many universities and online platforms offer project-based courses that can deepen your understanding.
  • Balance Depth and Breadth: Strive for a balance between depth and breadth. While it’s essential to specialize in a particular area, computer science is an interdisciplinary field, and having a broad understanding can be valuable.
  • Stay Flexible: Be open to changing your focus over time. As technology evolves, new topics emerge, and your interests may shift. Stay flexible and willing to adapt to the changing landscape of computer science.

Remember that selecting computer science topics is a personal and evolving process. 

Your interests, career goals, and knowledge level will influence your choices. Keep learning, exploring, and adapting as you progress in your computer science journey.

100+ Computer Science Topics: Category Wise

  • Sorting algorithms
  • Graph algorithms
  • Hashing techniques
  • Binary search
  • Tree data structures
  • Python Programming
  • JavaScript development
  • C++ language features
  • Functional programming
  • Language paradigms

Artificial Intelligence and Machine Learning

  • Neural networks
  • Reinforcement learning
  • Natural language processing
  • Computer vision
  • Deep learning frameworks


  • Network security
  • Ethical hacking
  • Cryptography techniques
  • Security Protocols
  • Intrusion detection

Database Management

  • SQL vs. NoSQL databases
  • Query optimization
  • Big Data technologies
  • Database design principles
  • Data warehousing

Computer Graphics and Visualization

  • 3D rendering
  • Animation techniques
  • Virtual reality (VR)
  • Augmented reality (AR)
  • Computer-aided design (CAD)

Quantum Computing

  • Quantum gates
  • Quantum algorithms
  • Quantum cryptography
  • Quantum hardware
  • Quantum supremacy

Internet of Things (IoT)

  • IoT protocols
  • Smart homes
  • Industrial IoT
  • Edge computing
  • IoT security

Blockchain Technology

  • Distributed ledger technology
  • Smart contracts
  • Cryptocurrency platforms
  • Blockchain for supply chain

Computer Science Education

  • Computer science degrees
  • Online coding bootcamps
  • Data science courses
  • AI certifications
  • MOOC platforms

Career Paths in Computer Science

  • Software developer roles
  • Data scientist jobs
  • Network engineer careers
  • Cybersecurity analyst positions
  • Cloud computing specialists

Web Development

  • Front-end development
  • Back-end programming
  • Full-stack development
  • Responsive web design
  • Web application frameworks

Operating Systems

  • Linux distributions
  • Windows internals
  • Real-time operating systems
  • File systems
  • Process management

Computer Networks

  • TCP/IP protocol suite
  • Network topologies
  • Wireless networks
  • Network virtualization
  • SDN and NFV

Software Engineering

  • Agile methodologies
  • DevOps practices
  • Software testing
  • Code quality and refactoring
  • Project management tools

Data Science and Big Data

  • Data preprocessing
  • Machine learning pipelines
  • Data visualization tools
  • Hadoop and Spark
  • Data analysis techniques

Game Development

  • Game engines
  • Unity and Unreal Engine
  • Game design principles
  • Game monetization strategies
  • Mobile game development

Ethical AI and AI Ethics

  • AI fairness
  • AI accountability
  • AI regulations
  • AI for social good

Human-Computer Interaction (HCI)

  • Usability testing
  • User experience (UX) design
  • HCI principles
  • User interface (UI) guidelines
  • Accessibility in HCI

Cloud Computing

  • Cloud service providers
  • Infrastructure as a Service (IaaS)
  • Platform as a Service (PaaS)
  • Serverless computing
  • Cloud security
  • Robotic sensors
  • Robot control systems
  • Swarm robotics
  • Industrial robotics
  • Humanoid robots

Emerging Trends and Technologies With Computer Science Topics

Utilizing the ideas of quantum physics, quantum computing is an interesting and relatively new topic that allows computations to be completed at rates that are not possible with traditional computers. 

Drug research, optimization, and encryption are just a few of the industries that quantum computers have the potential to completely transform. Research in quantum computing is rapidly progressing, with companies like IBM and Google making significant strides.

The network of networked items and gadgets that gather and share data is referred to as the Internet of Things (IoT). From smart homes to industrial sensors, IoT is transforming the way we live and work. However, with the convenience and connectivity IoT offers, come concerns about security and privacy.

In order to solve these issues and guarantee the secure and effective operation of IoT devices, computer scientists will be essential as the Internet of Things grows.

Blockchain technology, known for its association with cryptocurrencies like Bitcoin, is finding applications in various sectors beyond finance. Blockchains provide secure and transparent ledgers for recording transactions and data. 

Use cases range from supply chain management and voting systems to intellectual property protection.

As blockchain technology matures, computer scientists will find opportunities to develop innovative solutions and address its scalability and environmental concerns.

Computer Science Education and Career Paths

Computer science degrees and courses.

For those interested in pursuing a career in computer science, there are various educational paths to consider. These include bachelor’s, master’s, and Ph.D. programs, as well as online learning options. 

When choosing a program, it’s essential to consider your goals, the curriculum, and the reputation of the institution.

Online learning platforms and coding bootcamps offer flexible options for acquiring computer science skills. They can be a good fit for those looking to pivot into a tech career or acquire specific programming skills.

Career Opportunities in Computer Science

Computer science offers a broad range of career opportunities. Job roles include software developer, data scientist, network engineer, cybersecurity analyst, and AI specialist, among others. 

Salaries and job prospects vary depending on the role and your level of experience.

Computer science professionals are in demand in virtually every industry, from technology giants like Google and Amazon to healthcare, finance, and government agencies.

Computer science is a field of limitless potential and continuous growth. It underpins the technology that powers our world and shapes the future. 

From the fundamentals of algorithms and data structures to the cutting-edge technologies of AI, quantum computing, and blockchain, computer science is a dynamic and ever-evolving discipline.

Whether you’re a student embarking on a computer science journey or a technology enthusiast exploring the latest trends, the diverse and exciting world of computer science offers something for everyone. 

By staying informed and continually learning (with topics like computer science topics), you can contribute to the ongoing transformation of our digital landscape.

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  • Frontiers in Artificial Intelligence
  • Machine Learning and Artificial Intelligence
  • Research Topics

Machine Learning for CyberSecurity

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About this Research Topic

Cyber-physical systems (CPSs) have become a common component in critical infrastructures thanks to the economic benefits they bring by improving productivity. These systems depend on computer and information technologies (CITs) to maintain operation, supporting communication within sub-system components as well as the outside world. Despite the economic advantages of CITs, they also have made critical infrastructure and CPSs vulnerable to various cyber threats such as interception, replacement, and removal of information from communication channels. This is particularly troublesome in mission-critical systems such as power plants, medical infrastructures, and transportation infrastructures as they constantly carry sensitive information. If attackers gain access to these systems, it can result in massive economic losses and at worst, threaten human lives. CPSs produce massive amounts of data, which creates opportunities to use predictive Machine Learning (ML) as a viable solution to enhance the cybersecurity of these systems. Machine Learning (ML) is a branch of computer science and artificial intelligence. Over the last couple of decades, ML has transformed the world by releasing its immense power of extracting knowledge in big data streams. This Research Topic focuses on developing, adapting, and optimizing machine learning approaches for enhancing cybersecurity. This Research Topic will provide researchers a platform for the convergence of interdisciplinary research techniques that combine methods from computer science, machine learning, and social science towards designing, developing, optimizing, and evaluating AI systems applied to improve cybersecurity. The scope of this special issue includes but is not limited to: Use of Machine Learning/ Artificial Intelligence/ Neural Networks for Cyber Security: Theory, Recent Advancements and Applications Explainable Artificial Intelligence for CyberSecurity Application Cyber-physical health characterization in CPSs Application of Large Language Models for CyberSecurity Uncertainty quantification in cyber security Trustworthy AI in CyberSecurity The article types accepted in this topic are Original Research, Methods, Reviews, Brief Research Reports, Perspectives, Hypothesis and Theory.

Keywords : Cyber Security, Machine Learning, Artificial Intelligence, Neural Networks, XAI, Model Interpretability, Big Data, LLM

Important Note : All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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50+ Best Topics for Research in Computer Science

What is computer science.

Computer science is the study of the design, implementation, and maintenance of software systems. It is a rapidly growing field that is constantly evolving, and computer science graduates are in high demand.

How To Choose Computer Science Research Topics? 

There is no one answer to this question since it depends on your individual interests and goals. However, here are five general tips to keep in mind when choosing a research topic in computer science: 

  • Choose a topic that you are passionate about.
  • Make sure the topic is feasible and that you have the necessary resources to complete the research.
  • Consider the impact of your research. Will it be useful or interesting to others?
  • Make sure the topic is not too narrow or too broad.
  • Get advice from your supervisor or other experts in the field.

Interesting Computer Science Research Topics

  • How can computer science be used to solve real-world problems?
  • What are some of the most promising areas of research in computer science?
  • What are some of the challenges faced by computer science researchers?
  • What are some of the ethical issues involved in computer science research?
  • What are some of the social implications of computer science research?

Top Technology Research Paper Topics

  • How will 5G technology change the way we live and work?
  • What are the implications of quantum computing?
  • How will autonomous vehicles change transportation?
  • What are the ethical implications of artificial intelligence?
  • How will the Internet of Things change the way we live and work?
  • What are the implications of 3D printing?
  • How will virtual reality change the way we interact with the world?
  • What are the implications of blockchain technology?
  • How will wearables change the way we live and work?
  • What are the implications of artificial intelligence in healthcare?

Research Paper Topics in Computer Science 

  • What are the most important research areas in computer science?
  • What are the most important challenges facing computer science today?
  • What are the most important trends in computer science research?
  • What are the most important challenges facing computer science education today?

Computer Science Engineering Research Topics

  • The history of computer science and engineering.
  • The future of computer science and engineering.
  • The impact of computer science and engineering on society.
  • The ethical implications of computer science and engineering.
  • The role of computer science and engineering in education.
  • The role of computer science and engineering in business.
  • The role of computer science and engineering in government.
  • The impact of computer science and engineering on the environment.
  • The impact of computer science and engineering on the economy.
  • The future of computer science and engineering jobs.

Computer Science Research Paper Topics For High School 

  • The history of computer science and its impact on society.
  • The future of computer science and its impact on society.
  • The impact of computer science on society and the economy.
  • The impact of computer science on education.
  • The impact of computer science on the environment.

Controversial Topics in Computer Science

  • Should the government regulate the internet?
  • Should the government provide free internet access?
  • Should the government censor the internet?
  • Should the government tax internet use?
  • Should the government subsidize internet access?
  • Should the government build a national broadband network?
  • Should the government require net neutrality?
  • Should the government ban online gambling?
  • Should the government ban online pornography?
  • Should the government regulate online speech?

Latest Research Topics in Computer Science

  • Big data and machine learning
  • Cloud computing
  • Internet of Things
  • Blockchain technology
  • Cybersecurity 6. Natural language processing
  • Augmented reality and virtual reality
  • Artificial intelligence

Research Topics in Computer Science for Undergraduate Students

  • How can machine learning be used to improve the accuracy of predictions made by computer systems?
  • How can big data be effectively managed and analyzed using computer systems? 3. How can computer systems be designed to better protect user privacy?
  • How can computer systems be made more secure against attacks?
  • How can the performance of computer systems be improved?

Quick and Easy Computer Science Research Paper Topics

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topics for research computer science

Computer Science Research Paper Topics

Almost every element of our lives involves computer science. With the advancement of technology in computer science, the field is constantly changing and generating new research topics in computer science. These research topics seek to answer diverse research questions in computer science and their implications for the tech industry as well as the wider world.

Topics in research on computer science can be classified into various categories like artificial Intelligence, big data, and human-computer interaction, as well as security and privacy and engineering software if you're a college student or researcher in search of computer-related research paper subjects. If that is the case, this article will provide ideas for computing research topics and issues.

What makes a strong Computer Science Research Topic?

A good computer science topic is well-defined, clear, and simple to comprehend. It must also reflect the research's goal as well as its scope, purpose, or objective. Additionally, a solid computer science research subject is free of abbreviations not commonly used, but it may contain industry terms that are widely recognized.

Tips to Select the right Computer Science Research Topic

  • Brainstorm. Brainstorming can help you come up with several ideas and determine the most appropriate subject for you. The most important questions to ask yourself are: What are some questions that you can ask regarding computer science? What are your specific research interests? What are the current technological developments that are happening in computing?
  • Select a sub-field. There are numerous different subfields and career options that are related to computer science. Before you choose a topic for your research, make sure you spell the specific aspect of computer science your research will concentrate on. This could be the theoretical aspect of computer science, current technology, or distributed computing research areas.
  • Aim to answer a question. When selecting a topic for your research within computer science, you must always keep a question in the back of your mind that you'd like to know the answer to. This helps you narrow your research objectives to reach the specified objectives.
  • Conduct a thorough study of the literature. When you are beginning a research undertaking, it is vital that you have a clear understanding of the subject you intend to research. This means conducting a thorough study of the literature to understand what was learned about your subject over time.
  • Make the subject simple and easy to understand. The subject should be reflective of the purpose and scope of the research that it will be addressing. It should be clear and clear of any ambiguous words. Thus, some researchers have suggested that the subject be restricted to 5 to 15 meaningful words. It could take as a question or declaration.

How to Make Strong Computer Science Research Questions

To formulate significant computing research issues, it is essential to first know the subject in question. In addition, the research question must bring new knowledge to the table and aid in the development of the area. It might be a question that hasn't been dealt with previously or has only been partially addressed. It is also crucial to think about the possibility of finding the answer.


Every student knows the challenges that arise from selecting and deciding on a good subject in computer science. In general, a good topic must be original, exciting, fascinating, and demanding. It must push the boundaries of the area of study but still be able to answer the primary questions raised by the research.

We know the anxiety students can experience. This is why we've taken the time to search the internet and print sources to locate the most current computer science subjects that are causing the most excitement within the discipline. Here's a list of the most relevant Computer Science research topic of 2022 that you can use in your senior thesis or essay:


  • What impact has big data had on the way small companies carry out market research?
  • Does machine learning have a negative impact on the way that neurons within the brain function?
  • Has biotech changed the way medicines are administered to patients?
  • What is the impact on human perception by technology that simulates reality?
  • What can educators gain from the use of virtual reality in the classroom?
  • Quantum computers are the technology of the future, or is it just another trend?
  • Did the Covid-19 pandemic slow technological advances in computer science?


  • How successful has distance-learning technology been since the age of Covid-19?
  • Can computer-aided companies eliminate the need for customer service?
  • How has the state of the technology of encryption and decryption changed over the past 20 years?
  • Can AI influence the management of computers and make them automatized?
  • Why are programmers hesitant to create an all-purpose programming language?
  • What is the importance of human interactions with computers in the development process?
  • What will the future of computers look like over the coming five to ten years?

CONTROVERSIAL Topics in COMPUTER Science for Grade School Students

  • How can you tell the differences between art and math modeling?
  • What are the effects of big-budget Hollywood films affected by CGI technology?
  • Should students be allowed to utilize technology in classes other than those in comp science?
  • What is the most important thing to do? Restrict our time using social media?
  • Are quantum computers designed for personal or household use real?
  • How are embedded systems transforming the world of business?
  • How can human-computer interaction be enhanced?

Computer Science Capstone Project Ideas for COURSES IN COLLEGE

  • Which are the physical limits of computation and communication?
  • Is the SCRUM method still relevant for software development?
  • Are ATMs still safe machines to withdraw cash, or do they pose a threat?
  • What are the top advantages of making use of free software?
  • What is the future of distributed systems and their use in networks?
  • Does the increase in usage of social networks negatively or positively change our relations?
  • How can machine learning be affected through artificial Intelligence?

INTERESTING Computer Science Topics for College STUDENTS

  • What do you feel Blockchain had an impact on large corporations?
  • Do people need to use internal chips to monitor their pets?
  • How should we pay attention to the information we read on the internet?
  • What are the ways computers can aid the sequencing of human genes?
  • What can we do to improve IT security at banks?
  • What will the digitalization of medical practices mean for the privacy of patients?
  • How effective are backup data strategies in your businesses?


  • Is distance learning becoming the new standard for earning postgraduate degrees?
  • In the wake of the Covid-19 pandemic, are more students taking online classes?
  • What role can game theory play in the study of algorithms?
  • What impact will technology have on future elections of government?
  • Why are females underrepresented in the field of computer science?
  • Do the world's largest operating systems collaborate?
  • Is it safe to conduct payments on the internet?

PH.D. RESEARCH TOPICS IN Computer Science for Grade School Students

  • How can technology aid computer-aided professional athletes in increasing their performance?
  • What have Next Gen Stats changed the coach's game plan?
  • What impact has technology from computers had on medical technology?
  • What impact does MatLab software have on the field of medical engineering?
  • What is the impact of self-adaptable applications on the online learning experience?
  • What is the future of the field of information technology?
  • Do we need to be concerned about the dangers of addiction to technology?

Computer Science Research Topics for UNDERGRADUATES

  • What has the impact of online sports betting changed IT requirements in homes?
  • In what ways can computers be used to improve learning?
  • How can learning be improved by interactive multimedia and other similar technology?
  • Which are your psychological implications of IT advances?
  • What is the right balance between high engagement and addiction to video games?
  • How is the world of video gaming evolved over time?
  • Has social media been helpful or detrimental to our habits of communication?


  • What is the most crucial technique for planning projects?
  • What has technology done to improve people's odds of winning at bets on sports?
  • What impact has artificial technology had on how it has impacted the U.S. economy?
  • Is there any efficient process for managing projects in IT?
  • What do IT security systems aid in the process of generating fraud scores?
  • Has technology had an influence on the religion of your choice?
  • What is the importance of keeping your online media profiles current?


  • There isn't a single aspect of human society not affected by AI?
  • How can adaptive learning help professionals in today's world?
  • Do computer programs that were written a decade ago be effective?
  • What has the medical image analysis changed due to IT?
  • What ethical issues are associated with data mining?
  • Should universities and colleges be granted the power to block specific websites?
  • What are the most important elements of computing math?


  • What can sets and logic be utilized in computing?
  • How has online gambling affected betting in person?
  • What is the impact of the 5G network generation affect the way we communicate?
  • What are the biggest obstacles for IT caused by Covid-19?
  • Do you think assembly language is an innovative method of determining the health of a data mine?
  • What can technology in computers do to assist in locating criminals?


  • Why do girls and boys learn about technology in different ways?
  • How effective are computer-based education classes geared toward young girls?
  • How can technology impact the way the administration of medicines is done?
  • Are further technological advances likely to result in people being laid off from work?
  • How has computer science impacted the way teachers teach?
  • What do you think are the most efficient methods to stop identity theft?

Excellent Computer Science Thesis Topic Ideas

  • What are the computer-related needs of businesses that computers can address?
  • What are the advantages and disadvantages of using smart home technology?
  • How will the modernization of computers at the office impact productivity?
  • How has technology enabled computers to lead to the outsourcing of more jobs?
  • Are self-service customer services able to offer solutions?
  • What can a small-scale business do to remain competitive without the latest technology in computer systems?

Computer Science Topics for PRESENTATION

  • What is the future of virtual reality?
  • What are the latest developments in computer science?
  • What are the advantages and disadvantages of automatizing your daily life?
  • Are hackers really a security threat to our privacy or only to companies?
  • What are the most efficient five methods of storing personal information?
  • Which are the top essential foundations of engineering software?

Some more research topics in COMPUTER SCIENCE

  • In what ways are computers different than human brains?
  • Can global problems be solved by advances in the field of video game technology?
  • What have computers done to aid Human genome mapping?
  • What are the advantages and disadvantages of designing self-operating vehicles?
  • What has computer science done to help to create genetically modified food?
  • What are the applications of computers in the field of reproductive technologies?

Choosing the Best Computer Science Research Topic

Research in computer science is a broad field, and it isn't easy to pick the right subject. There are a few aspects to think about while making this choice. Pick a subject you are passionate about. This will allow you to stay focused and complete quality research to earn that computer science education.

Pick a topic pertinent to your field of study. This will enable you to develop expertise in the field. Pick a subject with potential for further research. This will guarantee that your research is current and current. Typically, boot camps for coding offer a framework to help streamline the students' work to specific fields, making their quest for a unique solution much easier.

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224 Research Topics on Technology & Computer Science

Are you new to the world of technology? Do you need topics related to technology to write about? No worries, Custom-writing.org experts are here to help! In this article, we offer you a multitude of creative and interesting technology topics from various research areas, including information technology and computer science. So, let’s start!

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  • 🔝 Top 10 Topics

👋 Introduction

  • đŸ’Ÿ Top 10 Computer Science Topics

⚙ Artificial Intelligence

💉 biotechnology, 📡 communications and media.

  • đŸ’»Computer Science & Engineering

🔋 Energy & Power Technologies

🍗 food technology, đŸ˜· medical devices & diagnostics, 💊 pharmaceutical technologies.

  • 🚈 Transportation

✋ Conclusion

🔍 references, 🔝 top 10 technology topics.

  • The difference between VR and AR
  • Is genetic engineering ethical?
  • Can digital books replace print ones?
  • The impact of virtual reality on education
  • 5 major fields of robotics
  • The risks and dangers of biometrics
  • Nanotechnology in medicine
  • Digital technology’s impact on globalization
  • Is proprietary software less secure than open-source?
  • The difference between deep learning and machine learning

Is it a good thing that technologies and computer science are developing so fast? No one knows for sure. There are too many different opinions, and some of them are quite radical! However, we know that technologies have changed our world once and forever. Computer science affects every single area of people’s lives.

Arthur clarke quote.

Just think about Netflix . Can you imagine that 23 years ago it didn’t exist? How did people live without it? Well, in 2023, the entertainment field has gone so far that you can travel anywhere while sitting in your room. All you would have to do is just order a VR (virtual reality) headset. Moreover, personal computers give an unlimited flow of information, which has changed the entire education system.

Every day, technologies become smarter and smaller. A smartphone in your pocket may be as powerful as your laptop. No doubt, the development of computer science builds our future. It is hard to count how many research areas in technologies and computer science are there. But it is not hard to name the most important of them.

Artificial intelligence tops the charts, of course. However, engineering and biotechnology are not far behind. Communications and media are developing super fast as well. The research is also done in areas that make our lives better and more comfortable. The list of them includes transport, food and energy, medical, and pharmaceutical areas.

So check out our list of 204 most relevant computer science research topics below. Maybe one of them will inspire you to do revolutionary research!

đŸ’Ÿ Top 10 Computer Science Research Topics

💡 technologies & computer science: research ideas.

Many people probably picture robots from the movie “I, Robot” when they hear about artificial intelligence. However, it is far from the truth.

AI is meant to be as close to a rational way of thinking as possible. It uses binary logic (just like computers) to help solve problems in many areas. Applied AI is only aimed at one task. A generalized AI branch is looking into a human-like machine that can learn to do anything.

Robotic hand pressing keyboard laptop.

Applied AI already helps researchers in quantum physics and medicine. You deal with AI every day when online shops suggest some items based on your previous purchases. Siri and self-driving cars are also examples of applied AI.

Generalized AI is supposed to be a copy of multitasking human intelligence. However, it is still in the stage of development. Computer technology has yet to reach the level necessary for its creation.

One of the latest trends in this area is improving healthcare management. It is done through the digitalization of all the information in hospitals and even helping diagnose the patients.

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Also, privacy issues and facial recognition technologies are being researched. For example, some governments collect biometric data to reduce and even predict crime.

Research Topics on Artificial Intelligence Technology

Since AI development is exceptionally relevant nowadays, it would be smart to invest your time and effort into researching it. Here are some ideas on artificial intelligence research topics that you can look into:

  • What areas of life machine learning are the most influential? 
  • How to choose the right algorithm for machine learning ? 
  • Supervised vs. unsupervised machine learning : compare & contrast 
  • Reinforcement machine learning algorithms 
  • Deep learning as a subset of machine learning 
  • Deep learning & artificial neural networks 
  • How do artificial neural networks work? 
  • A comparison of model-free & model-based reinforcement learning algorithms 
  • Reinforcement learning: single vs. multi-agent 
  • How do social robots interact with humans? 
  • Robotics in NASA  
  • Natural language processing: chatbots 
  • How does natural language processing produce natural language? 
  • Natural language processing vs. machine learning 
  • Artificial intelligence in computer vision 
  • Computer vision application: autonomous vehicles  
  • Recommender systems’ approaches 
  • Recommender systems: content-based recommendation vs. collaborative filtering 
  • Internet of things & artificial intelligence: the interconnection 
  • How much data do the Internet of things devices generate? 

Biotechnology uses living organisms to modify different products. Even the simple thing as baking bread is a process of biotechnology. However, nowadays, this area went as far as changing the organisms’ DNA. Genetics and biochemistry are also a part of the biotechnology area.

The development of this area allows people to cure diseases with the help of new medicines. In agriculture, more and more research is done on biological treatment and modifying plants. Biotechnology is even involved in the production of our groceries, household chemicals, and textiles.

Trends in biotechnology.

There are many exciting trends in biotechnology now that carry the potential of changing our world! For example, scientists are working on creating personalized drugs. This is feasible once they apply computer science to analyze people’s DNA.

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Also, thanks to using new technologies, doctors can collect exact data and provide the patients with correct diagnosis and treatment. Now, you don’t even need to leave your place to get a doctor’s check-up. Just use telehealth!

Data management is developing in the biotechnology area as well. Thanks to that, doctors and scientists can store and access a tremendous amount of information.

The most exciting is the fact that new technology enables specialists to assess genetic information to treat and prevent illnesses! It may solve the problem of some diseases that were considered untreatable before.

Research Topics on Biotechnology

You can use the following examples of research questions on biotechnology for presentation or even a PhD paper! Here is a wide range of topics on biotechnology and its relation to agriculture, nanotechnology, and many more:

  • Self-sufficient protein supply and biotechnology in farming 
  • Evaporation vs. evapotranspiration 
  • DNA cloning and a southern blot  
  • Pharmacogenetics & personalized drugs 
  • Is cloning “playing God”?  
  • Pharmacogenetics : cancer medicines 
  • How much can we control our genetics, at what point do we cease to be human?  
  • Bio ethics and stem cell research  
  • Genetic engineering: gene therapy  
  • The potential benefits of genetic engineering  
  • Genetic engineering: dangers and opportunities  
  • Mycobacterium tuberculosis : counting the proteins 
  • Plant genetic enhancement: developing resistance to scarcity 
  • Y-chromosome genotyping: the case of South Africa 
  • Agricultural biotechnology: GMO crops 
  • How are new vaccines developed? 
  • Nanotechnology in treating HIV 
  • Allergenic potential & biotechnology  
  • Whole-genome sequencing in biotechnology  
  • Genes in heavy metal tolerance: an overview 
  • Food biotechnology & food-borne illnesses 
  • How to eliminate heat-resistant microorganisms with ultraviolet? 
  • High-throughput screening & biotechnology  
  • How do new food processing technologies affect bacteria related to Aspalathus Linearis? 
  • Is sweet sorghum suitable for the production of bioethanol in Africa? 
  • How can pesticides help to diagnose cancer? 
  • How is embelin used to prevent cancer? 

One of the first areas that technologies affected was communications and media. People from the last century couldn’t have imagined how easy it would be to get connected with anyone! Internet connection starts appearing even in the most remote places.

Nowadays, media is used not only for social interaction but for business development and educational purposes as well. You can now start an entirely online business or use special tools to promote the existing one. Also, many leading universities offer online degrees.

In communications and media, AI has been playing the role of enhancement recently. The technology helps create personalized content for always demanding consumers.

Developing media also create numerous job opportunities. For instance, recently, an influencer has become a trending career. Influencers always use the most relevant communication tools available. At the moment, live videos and podcasting are on the top.

Now, you just need to reach your smartphone to access all the opportunities mentioned above! You can apply for a college, find a job, or reach out to all your followers online. It is hard to imagine how far communication and media can go

Communications and Media Technology Research Topics

There are quite a few simple yet exciting ideas for media and communications technology research topics. Hopefully, you will find THE ONE amongst these Information and Communications Technology (ICT) research proposal topics:

  • New media: the importance of ethics in the process of communication 
  • The development of computer-based communication over the last decade 
  • How have social media changed communication?  
  • Media during the disasters : increasing panic or helping reduce it? 
  • Authorities’ media representations in different countries: compare & contrast 
  • Do people start preferring newspapers to new media again? 
  • How has the Internet changed media?  
  • Communication networks  
  • The impact of social media on super bowl ads  
  • Communications: technology and personal contact  
  • New content marketing ideas  
  • Media exposure and its influence on adolescents  
  • The impact of mass media on personal socialization  
  • Internet and interactive media as an advertising tool  
  • Music marketing in a digital world  
  • How do people use hype in the media? 
  • Psychology of videoblog communication 
  • Media & the freedom of speech  
  • Is it possible to build trustful relationships in virtual communication? 
  • How to maintain privacy in social media ? 
  • Communication technologies & cyberbullying  
  • How has the interpersonal communication changed with the invention of computers? 
  • The future of the communication technologies  
  • Yellow journalism in new media 
  • How enterprises use ICT to get a competitive advantage? 
  • Healthcare and ICT 
  • Can we live without mass media ? 
  • Mass media and morality in the 21st century 

đŸ’» Computer Science & Engineering

If you have ever wondered how computers work, you better ask a professional in computer science and engineering. This major combines two different, yet interconnected, worlds of machines.

Computer science takes care of the computer’s brain. It usually includes areas of study, such as programming languages and algorithms. Scientists also recognize three paradigms in terms of the computer science field.

For the rationalist paradigm, computer science is a part of math. The technocratic paradigm is focused on software engineering, while the scientific one is all about natural sciences. Interestingly enough, the latter can also be found in the area of artificial intelligence!

Stephen Hawking quote.

On the other hand, computer engineering maintains a computer’s body – hardware and software. It relies quite heavily on electrical engineering. And only the combination of computer science and engineering gives a full understanding of the machine.

If talking about trends and innovations, artificial intelligence development is probably the main one in the area of computer science technology. Big data is the field that has been extremely popular in recent years.

Cybersecurity is and will be one of the leading research fields in our Information Age. The latest trend in computer science and engineering is also virtual reality.

Computer Science Research Topics

If you want to find a good idea for your thesis or you are just preparing for a speech, check out this list of research topics in computer science and engineering:

  • How are virtual reality & human perception connected? 
  • The future of computer-assisted education 
  • Computer science & high-dimensional data modeling 
  • Computer science: imperative vs. declarative languages 
  • The use of blockchain and AI for algorithmic regulations 
  • Banking industry & blockchain technology  
  • How does the machine architecture affect the efficiency of code? 
  • Languages for parallel computing 
  • How is mesh generation used for computational domains? 
  • Ways of persistent data structure optimization 
  • Sensor networks vs. cyber-physical system 
  • The development of computer graphics: non-photorealistic rendering case 
  • The development of the systems programming languages 
  • Game theory & network economics 
  • How can computational thinking affect science? 
  • Theoretical computer science in functional analysis  
  • The most efficient cryptographic protocols 
  • Software security types: an overview 
  • Is it possible to eliminate phishing? 
  • Floating point & programming language 

Without energy, no technological progress is possible. Scientists are continually working on improving energy and power technologies. Recently, efforts have been aimed at three main areas.

Developing new batteries and fuel types helps create less expensive ways of storing energy. For example, fuel cells can be used for passenger buses. They need to be connected to a source of fuel to work. However, it guarantees the constant production of electricity as long as they have fuel.

One of the potential trends of the next years is hydrogen energy storage. This method is still in the stage of development. It would allow the use of hydrogen instead of electricity.

Trends in energy technologies.

A smart grid is another area that uses information technology for the most efficient use of energy. For instance, the first-generation smart grid tracks the movement of electric energy on the go and sends the information back. It is a great way to correct the consumption of energy in real-time. More development is also done on the issue of electricity generation. It aims at technologies that can produce power from the sources that haven’t been used. The trends in this area include second-generation biofuels and photovoltaic glass.

Energy Technologies Research Topics

Since humanity cannot be using fossil fuels forever, the research in the area of energy can be extremely fruitful. The following list of energy and power technology research paper topics can give you an idea of where to dig:

  • How can fuel cells be used for stationary power generation? 
  • Lithium-ion vs. lithium-air batteries: energy density 
  • Are lithium-air batteries better than gasoline ? 
  • Renewable energy usage: advantages and disadvantages  
  • The nuclear power usage in the UAE  
  • India’s solar installations  
  • Gas price increasing and alternative energy sources  
  • How can methods of energy transformation be applied with hydrogen energy? 
  • Is hydrogen energy our future? 
  • Thermal storage & AC systems 
  • How to load balance using smart grid? 
  • Distributed energy generation to optimize power waste 
  • Is the smart energy network a solution to climate change ? 
  • The future of the tidal power 
  • The possibility of 3D printing of micro stirling engines 
  • How can robots be used to adjust solar panels to weather? 
  • Advanced biofuels & algae  
  • Can photovoltaic glass be fully transparent? 
  • Third-generation biofuels : algae vs. crop-based 
  • Space-based solar power: myth or reality of the future? 
  • Can smaller nuclear reactors be more efficient? 
  • Inertial confinement fusion & creal energy 
  • Renewable energy technologies: an overview 
  • How can thorium change the nuclear power field? 

The way we get our food has changed drastically with the technological development. Manufacturers look for ways to feed 7.5 billion people more efficiently. And the demand is growing every year. Now technology is not only used for packaging, but for producing and processing food as well.

Introducing robots into the process of manufacturing brings multiple benefits to the producer. Not only do they make it more cost-efficient, but they also reduce safety problems.

Surprisingly enough, you can print food on the 3D printer now! This technology is applied to produce soft food for people who can’t chew. NASA decided to use it for fun as well and printed a pizza!

Drones now help farmers to keep an eye on crops from above. It helps them see the full picture and analyze the current state of the fields. For example, a drone can spot a starting disease and save the crop.

The newest eco trends push companies to become more environmentally aware. They use technologies to create safer packaging. The issue of food waste is also getting more and more relevant. Consumers want to know that nothing is wasted. Thanks to the new technologies, the excess food is now used more wisely.

Food Technology Research Topics

If you are looking for qualitative research topics about technology in the food industry, here is a list of ideas you don’t want to miss:

  • What machines are used in the food industry? 
  • How do robots improve safety in butchery? 
  • Food industry & 3D printing 
  • 3D printed food – a solution to help people with swallowing disorder? 
  • Drones & precision agriculture 
  • How is robotics used to create eco-friendly food packaging ? 
  • Is micro packaging our future? 
  • The development of edible cling film 

Healthy food plastic bags.

  • Technology & food waste : what are the solutions? 
  • Additives and preservatives & human gut microbiome 
  • The effect of citric acid on the orange juice: physicochemical level 
  • Vegetable oils in mass production: compare & contrast 
  • Time-temperature indicators & food industry 
  • Conventional vs. hydroponic farming  
  • Food safety: a policy issue in agriculture today  
  • How to improve the detection of parasites in food? 
  • What are the newest technologies in the baking industry? 
  • Eliminating byproducts in edible oils production 
  • Cold plasma & biofilms 
  • How good are the antioxidant peptides derived from plants? 
  • Electronic nose in food industry and agriculture 
  • The harm of polyphenols in food 

Why does the life expectancy of people get higher and higher every year? One of the main aspects of it is the promotion of innovation in the medical area. For example, the development of equipment helps medical professionals to save many lives.

Thanks to information technology, the work is much more structured now in the medical area. The hospitals use tablets and the method of electronic medical records. It helps them to access and share the data more efficiently.

If talking about medical devices, emerging technologies save more lives than ever! For instance, operations done by robots are getting more and more popular. Don’t worry! Doctors are still in charge; they just control the robots from the other room. It allows operations to be less invasive and precise.

Moreover, science not only helps treat diseases but also prevent them! The medical research aims for the development of vaccines against deadly illnesses like malaria.

Some of the projects even sound more like crazy ideas from the future. But it is all happening right now! Scientists are working on the creation of artificial organs and the best robotic prosthetics.

All the technologies mentioned above are critical for successful healthcare management.

Medical Technology Research Topics

If you feel like saving lives is the purpose of your life, then technological research topics in the medical area are for you! These topics would also suit for your research paper:

  • How effective are robotic surgeries ? 
  • Smart inhalers as the new solution for asthma treatment  
  • Genetic counseling – a new way of preventing diseases? 
  • The benefits of the electronic medical records  
  • Erythrocytapheresis to treat sickle cell disease  
  • Defibrillator & cardiac resynchronization therapy 
  • Why do drug-eluting stents fail? 
  • Dissolvable brain sensors: an overview 
  • 3D printing for medical purposes  
  • How soon will we be able to create artificial organs? 
  • Wearable technologies & healthcare 
  • Precision medicine based on genetics 
  • Virtual reality devices for educational purposes in medical schools 
  • The development of telemedicine  
  • Clustered regularly interspaced short palindromic repeats as the way of treating diseases 
  • Nanotechnology & cancer treatment  
  • How safe is genome editing? 
  • The trends in electronic diagnostic tools development 
  • The future of the brain-machine interface 
  • How does wireless communication help medical professionals in hospitals? 

In the past years, technologies have been drastically changing the pharmaceutical industry. Now, a lot of processes are optimized with the help of information technology. The ways of prescribing and distributing medications are much more efficient today. Moreover, the production of medicines itself has changed.

For instance, electronic prior authorization is now applied by more than half of the pharmacies. It makes the process of acquiring prior authorization much faster and easier.

The high price of medicines is the number one reason why patients stop using prescriptions. Real-time pharmacy benefit may be the solution! It is a system that gives another perspective for the prescribers. While working with individual patients, they will be able to consider multiple factors with the help of data provided.

The pharmaceutical industry also adopts some new technologies to compete on the international level. They apply advanced data analytics to optimize their work.

Companies try to reduce the cost and boost the effectiveness of the medicines. That is why they look into technologies that help avoid failures in the final clinical trials.

The constant research in the area of pharma is paying off. New specialty drugs and therapies arrive to treat chronic diseases. However, there are still enough opportunities for development.

Pharmaceutical Technologies Research Topics

Following the latest trends in the pharmaceutical area, this list offers a wide range of creative research topics on pharmaceutical technologies:

  • Electronic prior authorization as a pharmacy technological trend 
  • The effectiveness of medication therapy management 
  • Medication therapy management & health information exchanges  
  • Electronic prescribing of controlled substances as a solution for drug abuse issue 
  • Do prescription drug monitoring programs really work? 
  • How can pharmacists help with meaningful use? 
  • NCPDP script standard for specialty pharmacies 
  • Pharmaceutical technologies & specialty medications 
  • What is the patient’s interest in the real-time pharmacy? 
  • The development of the vaccines for AIDS 
  • Phenotypic screening in pharmaceutical researches 
  • How does cloud ERP help pharmaceutical companies with analytics? 
  • Data security & pharmaceutical technologies 
  • An overview of the DNA-encoded library technology 
  • Pharmaceutical technologies: antibiotics vs. superbugs 
  • Personalized medicine: body-on-a-chip approach 
  • The future of cannabidiol medication in pain management 
  • How is cloud technology beneficial for small pharmaceutical companies? 
  • A new perspective on treatment: medicines from plants  
  • Anticancer nanomedicine: a pharmaceutical hope 

🚈 Transportation Technologies

We used to be focused on making transportation more convenient. However, nowadays, the focus is slowly switching to ecological issues.

It doesn’t mean that vehicles can’t be comfortable at the same time. That is why the development of electric and self-driving cars is on the peak.

Transportation technologies also address the issues of safety and traffic jams. There are quite many solutions suggested. However, it would be hard for big cities to switch to the other systems fast.

One of the solutions is using shared vehicle phone applications. It allows reducing the number of private cars on the roads. On the other hand, if more people start preferring private vehicles, it may cause even more traffic issues.

Transportation technologies.

The most innovative cities even start looking for more eco-friendly solutions for public transport. Buses are being replaced by electric ones. At the same time, the latest trend is using private electric vehicles such as scooters and bikes.

So that people use public transport more, it should be more accessible and comfortable. That is why the payment systems are also being updated. Now, all you would need is to download an app and buy a ticket in one click!

Transportation Technologies Research Topics

Here you can find the best information technology research topics related to transportation technologies:

  • How safe are self-driving cars ? 
  • Electric vs. hybrid cars : compare & contrast 
  • How to save your smart car from being hijacked? 
  • How do next-generation GPS devices adjust the route for traffic? 
  • Transportation technologies: personal transportation pods 
  • High-speed rail networks in Japan 
  • Cell phones during driving: threats and solutions  
  • Transportation: electric cars effects  
  • Teleportation: physics of the impossible  
  • How soon we will see Elon Musk’s Hyperloop? 
  • Gyroscopes as a solution for convenient public transportation 
  • Electric trucks: the effect on logistics 
  • Why were electric scooters banned in some cities in 2018? 
  • Carbon fiber as an optional material for unit load devices 
  • What are the benefits of the advanced transportation management systems? 
  • How to make solar roadways more cost-effective? 
  • How is blockchain applied in the transportation industry 
  • Transportation technologies: an overview of the freight check-in 
  • How do delivery companies use artificial intelligence? 
  • Water-fueled cars: the technology of future or fantasy? 
  • What can monitoring systems be used to manage curb space? 
  • Inclusivity and accessibility in public transport: an overview 
  • The development of the mobility-as-a-service 

All in all, this article is a compilation of the 204 most interesting research topics on technology and computer science. It is a perfect source of inspiration for anyone who is interested in doing research in this area.

We have divided the topics by specific areas, which makes it easier for you to find your favorite one. There are 20 topics in each category, along with a short explanation of the most recent trends in the area.

You can choose one topic from artificial intelligence research topics and start working on it right away! There is also a wide selection of questions on biotechnology and engineering that are waiting to be answered.

Since media and communications are present in our everyday life and develop very fast, you should look into this area. But if you want to make a real change, you can’t miss on researching medical and pharmaceutical, food and energy, and transportation areas.

Of course, you are welcome to customize the topic you choose! The more creativity, the better! Maybe your research has the power to change something! Good luck, and have fun!

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12 Most Emerging Research Areas in Computer Science in 2021

By: P. Chaudhary, B. Gupta

  • Artificial Intelligence and Robotics

topics for research computer science

Artificial Intelligence and Robotics [1, 2] field aims at developing computational system that are intelligent in decision making, planning, object recognition, and other complex computational tasks that require minimum human intervention. This field emphasizes upon the development of cognitive algorithms for a variety of domains including e-commerce, healthcare, transport, manufacturing, gaming, defense industry, logistics, to name a few. It includes the application of popular emerging technologies such as Deep leaning, machine learning, Natural language processing (NLP), robotics, evolutionary algorithms, statistical inference, probabilistic methods, and computer vision. Some of the eminent research areas includes the following:

  • Knowledge representation and reasoning
  • Estimation theory
  • Mobility mechanisms
  • Multi-agent negotiation
  • Intelligent agents
  • Semantic segmentation
  • Assistive robotics in medical diagnosis
  • Robot perception and learning
  • Motion planning and control
  • Autonomous vehicles
  • Personal assistive robots
  • Search and information retrieval
  • Speech and language recognition
  • Fuzzy and neural system
  • Intelligent embedded system in industries
  • Object detection and capturing
  • Intelligent information systems

2. Big Data Analytics

topics for research computer science

Big data analytics [3, 4] research field involves design and development of techniques/algorithms/frameworks to explore the large amount of data to fulfill organization’s objectives. This area includes mathematical, statistical and graphical approaches to mine useful knowledge patterns from heterogeneous raw data. It is one of the potential and emerging research domains as almost every organization is attempting to utilize available data to enhance their productivity and services to their customers. Some of the distinguished research areas are following:

  • Predictive analysis
  • Data capturing and transmission
  • Parallel Data processing
  • Uncertainty in data
  • Data anonymization methods
  • Data processing in distributed environment
  • Privacy protecting techniques
  • Semantic analysis on social media
  • Intelligent traffic surveillance
  • Topological data analysis

3. Biometrics and Computational Biology

topics for research computer science

This field embraces enormous potential for researchers as it amalgamates multiple research areas including big data, image processing, biological science, data mining, and machine learning. This field emphasizes on the designing and development of computational techniques for processing biological data [5, 6]. Some of the potential research areas includes:

  • Structure and sequence analysis algorithms
  • Protein structure anticipation
  • Data modeling of scientific applications
  • Virtual screening
  • Brain image analysis using data mining approaches
  • Design predictive models for severe disease analysis
  • Molecular structure modeling and analysis
  • Brain-machine interfaces
  • Computational neuroscience

4. Data Mining and Databases

topics for research computer science

This field motivates research on designing vital methods, prototype schemes and applications in data mining and databases. This field ensembles all methods, techniques, and algorithms used for extracting knowledgeable information from the available heterogenous raw data [7, 8]. It enables classification, characterization, searching and clustering different datasets from wide range of domains including e-commerce, social media, healthcare, to name a few. This field demands parallel and distributed processing of data as it operates on massive quantity of data. It integrates various research domains including artificial intelligence, big data analytics, data mining, database management system, and bioinformatics. Some of the eminent research areas comprises as follows:

  • Distributed data mining
  • Multimedia storage and retrieval
  • Data clustering
  • Pattern matching and analysis
  • High-dimensional data modeling
  • Spatial and scientific data mining for sensor data
  • Query interface for text/image processing
  • Scalable data analysis and query processing
  • Metadata management
  • Graph database management and analysis system for social media
  • Interactive data exploration and visualization
  • Secure data processing

5. Internet of Things (IoTs)

topics for research computer science

Internet of Things has transformed the lives of people through exploring new horizons of networking. It connects physical objects with the internet as per the application to serve the user. This field carries enormous potential in different research areas related to the IoT and its interrelated research domains [9, 10]. These areas include as follows:

  • IoT network infrastructure design
  • Security issues in IoT
  • Architectural issues in Embedded system
  • Adaptive networks for IoT
  • Service provisioning and management in IoT
  • Middleware management in IoT
  • Handling Device Interoperability in IoT
  • Scalability issues in IoT
  • Privacy and trust issues in IoT
  • Data storage and analysis in IoT networks
  • Integration of IoT with other emerging technologies such as fog computing, SDN, Blockchain, etc.
  • Context and location awareness in IoT networks
  • Modeling and management of IoT applications
  • Task scheduling in IoT networks
  • Resource allotment among smart devices in IoT networks.

6.  High-Performance Computing

topics for research computer science

This field encourage the research in designing and development of parallel algorithms/techniques for multiprocessor and distributed systems. These techniques are efficient for data and computationally exhaustive programs like data mining, optimization, super computer application, graph portioning, to name a few [11, 12]. Some of the eminent research challenges includes the following:

  • Information retrieval methods in cloud storage
  • Graph mining in social media networks
  • Distributed and parallel computing methods
  • Development of architecture aware algorithms
  • Big data analytics methods on GPU system
  • Designing of parallel algorithms
  • Designing of algorithms for Quantum computing

7. Blockchain and Decentralized Systems

topics for research computer science

This field [13, 14] revolutionize the digital world through processing network information without any central authority. This field is an emerging computing paradigm and motivates the design and development of algorithms that operate in decentralized environment. These techniques provide security, robustness and scalability in the network. Some of the eminent research areas includes the following:

  • Enhancing IoT security using blockchain
  • Precision agriculture and blockchain
  • Social blockchain networks
  • Blockchain based solutions for intelligent transportation system
  • Security and privacy issues in blockchain networks
  • Digital currencies and blockchain
  • Blockchain and 5G/6G communication networks
  • Integration of cloud/fog computing with blockchain
  • Legislation rules and policies for blockchain
  • Artificial Intelligence for blockchain system

8. Cybersecurity

topics for research computer science

With the development of new technology such as IoT, attackers have wider attack surface to halt the normal functioning of any network. Attackers may have several intentions to trigger cyber-attacks either against an individual person, organization, and/or a country. Now-a-days, we are living in a digital world where everything is connected is to the internet, so we are prone to some form of security attacks [15, 16]. This field carries massive potential for research on different techniques/methods to defend against these attacks. Some of the emerging research areas comprise the following:

  • Intrusion detection system
  • Applied cryptography
  • Privacy issues in RFID system
  • Security challenges in IoT system
  • Malware detection in cloud computing
  • Security and privacy issues in social media
  • Wireless sensor network security
  • Mobile device security
  • Lawa and ethics in cybersecurity
  • Cyber physical system security
  • Software defined network security
  • Security implications of the quantum computing
  • Blockchain and its security
  • AI and IoT security
  • Privacy issues in big data analytics
  • Phishing detection in finance sector

9. AI and Cyber Physical System

topics for research computer science

Specifically, Cyber physical system integrates computation and physical methods whose functionalities is determined by both physical and cyber component of the system. Research in this area motivates the development of tools, techniques, algorithms and theories for the CPS and other interrelated research domains [17, 18]. Research topics includes the following:

  • Human computer interaction
  • Digital design of CPS interfaces
  • Embedded system and its security
  • Industrial Interne to things
  • Automation in manufacturing industries
  • Robotics in healthcare sector
  • Medical informatics
  • AI, robotics and cyber physical system
  • Robot networks
  • Cognitive computing and CPS

10. Networking and Embedded Systems

topics for research computer science

This field [19, 20] encourages research on the designing of contemporary theories and approaches, effective and scalable methods and protocols, and innovative network design structure and services. These mechanisms improve the reliability, availability, security, privacy, manageability of current and future network and embedded systems. Research in this domain comprises of following topics:

  • Cyber physical system
  • Design of novel network protocols
  • Cognitive radio networks
  • Network security for lightweight and enterprise networks
  • Resource allocation schemes in resource-constrained networks
  • Network coding
  • Energy efficient protocols for wireless sensor networks
  • AI and embedded system
  • Embedded system for precision agriculture

11. Computer Vision and Augmented Reality

topics for research computer science

Computer vision [21, 22] is a multidisciplinary field that make computer system to understand and extract useful information from digital images and videos. This field motivates the research in designing the tools and techniques for understanding, processing, extracting, and storing, analyzing the digital images and videos. It embraces multiple domains such as image processing, artificial intelligence, pattern recognition, virtual reality, augmented reality, semantic structuring, statistics, and probability. Some of the eminent research topics includes the following:

  • Computer vision for autonomous robots
  • Object detection in autonomous vehicles
  • Object detection and delineation in UAVs network.
  • Biomedical image analysis
  • Augmented reality in gaming
  • Shape analysis in digital images
  • Computer vision for forensics
  • Robotics navigation
  • Deep learning techniques for computer vision
  • Automation in manufacturing sector
  • 3D object recognition and tracking

12. Wireless Networks and Distributed Systems

topics for research computer science

The research in this field emphasizes on the developments of techniques that facilitate communication and maintain coordination among distributed nodes in a network [23, 24]. It is a broad area that embraces numerous domains including cloud computing, wireless networks, mobile computing, big data, and edge computing. Some of the eminent research topics includes the following:

  • Message passing models in distributed system
  • Parallel distributed computing
  • Fault tolerance and load balancing
  • Dynamic resource allocation in distributed system
  • Resource discovery and naming
  • Low-latency consistency protocols
  • Designing of consensus protocols
  • Efficient communication protocols in distributed system
  • Security issues in distributed networks
  • Privacy and trust models
  • Optimization of distributed storage
  • Distributed and federated machine learning

[1] Wisskirchen, G., Biacabe, B. T., Bormann, U., Muntz, A., Niehaus, G., Soler, G. J., & von Brauchitsch, B. (2017). Artificial intelligence and robotics and their impact on the workplace . IBA Global Employment Institute, 11(5), 49-67. [2] Kortenkamp, D., Bonasso, R. P., & Murphy, R. (Eds.). (1998). Artificial intelligence and mobile robots: case studies of successful robot systems. MIT Press. [3] Dai, H. N., Wang, H., Xu, G., Wan, J., & Imran, M. (2020). Big data analytics for manufacturing internet of things: opportunities, challenges and enabling technologies . Enterprise Information Systems, 14(9-10), 1279-1303. [4] MĂŒller, O., Junglas, I., Vom Brocke, J., & Debortoli, S. (2016). Utilizing big data analytics for information systems research: challenges, promises and guidelines . European Journal of Information Systems, 25(4), 289-302. [5] Waterman, M. S. (2018). Introduction to computational biology: maps, sequences and genomes. Chapman and Hall/CRC. [6] Imaoka, H., Hashimoto, H., Takahashi, K., Ebihara, A. F., Liu, J., Hayasaka, A., 
 & Sakurai, K. (2021). The future of biometrics technology: from face recognition to related applications. APSIPA Transactions on Signal and Information Processing, 10. [7] Zhu, X., & Davidson, I. (Eds.). (2007). Knowledge Discovery and Data Mining: Challenges and Realities: Challenges and Realities . Igi Global. [8] Tseng, L., Yao, X., Otoum, S., Aloqaily, M., & Jararweh, Y. (2020). Blockchain-based database in an IoT environment: challenges, opportunities, and analysis. Cluster Computing, 23(3), 2151-2165. [9] Stoyanova, M., Nikoloudakis, Y., Panagiotakis, S., Pallis, E., & Markakis, E. K. (2020). A survey on the internet of things (IoT) forensics: challenges, approaches, and open issues. IEEE Communications Surveys & Tutorials, 22(2), 1191-1221. [10] NiĆŸetić, S., Ć olić, P., GonzĂĄlez-de, D. L. D. I., & Patrono, L. (2020). Internet of Things (IoT): Opportunities, issues and challenges towards a smart and sustainable future. Journal of Cleaner Production, 274, 122877. [11] Hager, G., & Wellein, G. (2010). Introduction to high performance computing for scientists and engineers. CRC Press. [12] Wang, G. G., Cai, X., Cui, Z., Min, G., & Chen, J. (2017). High performance computing for cyber physical social systems by using evolutionary multi-objective optimization algorithm . IEEE Transactions on Emerging Topics in Computing, 8(1), 20-30. [13] Zheng, Z., Xie, S., Dai, H. N., Chen, X., & Wang, H. (2018). Blockchain challenges and opportunities: A survey. International Journal of Web and Grid Services, 14(4), 352-375. [14] Nguyen, D. C., Ding, M., Pham, Q. V., Pathirana, P. N., Le, L. B., Seneviratne, A., 
 & Poor, H. V. (2021). Federated learning meets blockchain in edge computing: Opportunities and challenges . IEEE Internet of Things Journal. [15] Tawalbeh, L. A., Muheidat, F., Tawalbeh, M., & Quwaider, M. (2020). IoT Privacy and security: Challenges and solutions. Applied Sciences, 10(12), 4102. [16] Boubiche, D. E., Athmani, S., Boubiche, S., & Toral-Cruz, H. (2021). Cybersecurity Issues in Wireless Sensor Networks: Current Challenges and Solutions. Wireless Personal Communications, 117(1). [17] Gupta, R., Tanwar, S., Al-Turjman, F., Italiya, P., Nauman, A., & Kim, S. W. (2020). Smart contract privacy protection using ai in cyber-physical systems: Tools, techniques and challenges. IEEE Access, 8, 24746-24772. [18] Kravets, A. G., Bolshakov, A. A., & Shcherbakov, M. V. (2020). Cyber-physical Systems: Industry 4.0 Challenges . Springer. [19] Duan, Q., Wang, S., & Ansari, N. (2020). Convergence of networking and cloud/edge computing: Status, challenges, and opportunities. IEEE Network, 34(6), 148-155. [20] Wang, C. X., Di Renzo, M., Stanczak, S., Wang, S., & Larsson, E. G. (2020). Artificial intelligence enabled wireless networking for 5G and beyond: Recent advances and future challenges. IEEE Wireless Communications, 27(1), 16-23. [21] Chen, C. H. (Ed.). (2015). Handbook of pattern recognition and computer vision . World Scientific. [22] Esteva, A., Chou, K., Yeung, S., Naik, N., Madani, A., Mottaghi, A., 
 & Socher, R. (2021). Deep learning-enabled medical computer vision. NPJ digital medicine, 4(1), 1-9. [23] Farahani, B., Firouzi, F., & Luecking, M. (2021). The convergence of IoT and distributed ledger technologies (DLT): Opportunities, challenges, and solutions. Journal of Network and Computer Applications, 177, 102936. [24] Alfandi, O., Otoum, S., & Jararweh, Y. (2020, April). Blockchain solution for iot-based critical infrastructures: Byzantine fault tolerance. In NOMS 2020-2020 IEEE/IFIP Network Operations and Management Symposium (pp. 1-4). IEEE.

Cite this article:

P. Chaudhary, B. Gupta (2021) 12 Most Emerging Research Areas in Computer Science in 2021 , Insights2Techinfo, pp. 1

FAQ on this topic

Artificial Intelligence and Robotics, Big Data Analytics,  Biometrics and Computational Biology, Data Mining and Databases, Internet of Things (IoTs), High-Performance Computing, Blockchain and Decentralized Systems,Cybersecurity

Big data research field involves design and development of techniques/algorithms/frameworks to explore the large amount of data to fulfill organization’s objectives. Some of the distinguished research areas are following: Data capturing and transmission, Parallel Data processing,Data anonymization methods,Data processing in distributed environment

Artificial Intelligence field aims at developing computational systems that are intelligent in decision making, planning, object recognition, and other complex computational tasks that require minimum human intervention. Some of the eminent research areas includes the following: Knowledge representation and reasoning Autonomous vehicles, Fuzzy and neural system, Intelligent information systems 

Some of the eminent research areas comprises as follows:Distributed data mining, Multimedia storage and retrieval, Data clustering, Pattern matching and analysis, High-dimensional data modeling, Spatial and scientific data mining for sensor data.

The research areas in IoT include as follows: IoT network infrastructure design, Security issues in IoT,Architectural issues in Embedded system, Service provisioning and management in IoT, Middleware management in IoT

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Computer Science Trends

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Liz Simmons

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Learn about our editorial process .

Updated September 12, 2022

topics for research computer science

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Are you ready to discover your college program?

Computer science offers a sought-after, lucrative career path for tech-savvy people interested in the latest computer advancements. The U.S. Bureau of Labor Statistics projects 11% growth for computer and information technology (IT) occupations from 2019 to 2029, a faster-than-average growth rate. Computer science trends like cloud computing, information security, and big data collection and storage contribute to this field's promising outlook.

IT professionals who understand computer science trends remain competitive for the best career opportunities. This guide explores recent computing developments and trends in IT, including artificial intelligence, cybersecurity, and robotics.

Top Computer Science Trends

Artificial Intelligence | Edge Computing | Quantum Computing | Robotics | Cybersecurity | Bioinformatics

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Explore programs of your interests with the high-quality standards and flexibility you need to take your career to the next level.

Artificial Intelligence

Artificial intelligence (AI) centers on machine coding that mimics human and animal intelligence. AI professionals develop algorithms and program machines to perform humanlike tasks. Already ubiquitous, AI helps detect credit card fraud, identify disease outbreaks, and optimize satellite navigation.

In their annual technology prediction report, the Institute of Electrical and Electronics Engineers Computer Society predicts several AI concepts will be widely adopted in 2021. Computing developments in AI purportedly include reliability and safety for intelligent autonomous systems, AI for digital manufacturing, and trustworthy and explainable AI and machine learning.

A master's or Ph.D. leads to the best work opportunities in artificial intelligence.

Computer and information research scientists, one potential AI career, earned a median annual salary of $126,830 as of 2020 , with the BLS projecting much-faster-than-average growth for the profession from 2019 to 2029.

According to PayScale , as of June 2021, machine learning engineers earn an average annual salary of $112,840, with late-career professionals making an average annual salary of $162,000.

Entry-level AI jobs require at least a bachelor's degree, but a master's or Ph.D. leads to the best work opportunities in artificial intelligence.

Potential Jobs:

  • Machine Learning Engineer
  • Senior Data Scientist
  • Artificial Intelligence/Machine Learning Research Scientist
  • Deep Learning Engineer
  • Algorithm Engineer

Edge Computing

In contrast to cloud computing, where data is processed and stored far away from the end user in large data centers, edge computing puts computer data at "the edge," close to the end user. Experts do not expect the cloud to disappear completely, but rather work in tandem with edge computing as it brings processing to users, streamlining anything from factory production to self-driving car response.

Technology like autonomous cars, video conferencing, and augmented reality all benefit from edge computing. For example, when an autonomous car makes a split-second decision to brake and avoid a collision, an on-board computer system — edge computing — eliminates the delay of waiting for a server in the cloud to respond.

The BLS projects a 22% job growth rate from 2019 to 2029 for software developers , including edge computing software developers, and reports a median annual salary of $110,140 as of 2020.

Industries like telecommunications, security, and oil and gas employ workers with edge computing expertise. Entry-level positions such as software developer or computer network architect usually require a bachelor's. Managerial, administrative, and research positions often require at least a master's degree.

  • Edge Computing Specialist
  • Software Developer
  • Application Developer
  • Computer Network Architect
  • Computer Systems Analyst

Quantum Computing

Quantum computing uses powerful computers to solve problems at the atomic and subatomic levels. Unlike classic computers, which perform calculations and store data in binary code, quantum computers use quantum bits, also known as qubits. This allows quantum computers to crunch numbers and solve problems much more quickly than previously possible.

While large tech companies like Google and IBM make strides towards quantum computing advances, the field remains in its infancy. Other fields that can benefit from quantum computing include banking, transportation, and agriculture.

Researchers may use quantum computing to find the best truck delivery routes, determine the most efficient flight schedule for an airport, or develop new medicines quickly and cheaply. Scientists see promise in quantum computing to develop sustainable technologies and solve environmental problems.

Quantum computing careers usually require a master's or doctoral degree. ZipRecruiter reports salaries as high as $160,000 for quantum computing professionals, with an average annual salary of $96,900 as of May 2021. As an emerging computer science specialization, many future quantum computing careers may not yet exist.

  • Quantum Computer Architect
  • Quantum Software Developer
  • Quantum Algorithm Researcher
  • Quantum Computer Research Scientist

The robotics field studies and develops robots in the pursuit of make life easier. A multidisciplinary field, robotics incorporates computer science and electrical and mechanical engineering. Robotics uses artificial intelligence, machine learning, and other computer science technologies.

Robots aim to increase safety and efficiency in industries like manufacturing, farming, and food preparation. People use robotics technologies to manufacture cars, complete dangerous tasks like bomb diffusion, and conduct complex surgeries.

Students need a bachelor's degree at minimum to work in robotics. Many employers prefer robotics professionals with a master's or a Ph.D. for managerial or advanced research positions.

The BLS reports mechanical engineers — including robotics engineers — earned a median annual salary of $90,160 as of 2020. Robotics research scientists , which the BLS categorizes as a type of computer and information research scientist, earned a median annual salary of $126,830 as of 2020. The field is projected to grow much faster than average from 2019 to 2029.

Robots.jobs lists occupations and other resources for robotics professionals.

  • Robotics Engineer
  • Data Scientist
  • Software Engineer
  • Robotics Research Scientist


Cybersecurity focuses on protecting computer systems and networks from cyberthreats and attacks. As companies continue storing information on the cloud and conduct operations online, the need for improved cybersecurity also grows.

Individuals, businesses, and governments experience significant financial losses due to cyberattacks. For example, the ransomware attack in the eastern U.S. in May 2021 cost the Colonial Pipeline about $5 million and inflated gas prices for consumers.

Most industries, including healthcare, financial institutions, and insurance, need better cybersecurity technologies to protect their proprietary and customer data. Because of this demand, the BLS projects a 31% job growth rate for information security analysts from 2019 to 2029. Information security analysts earned a median annual salary of $103,590 as of 2020 .

Cybersecurity specialists work at consulting firms, computer companies, and business and financial organizations. Major employers include Apple, Lockheed Martin, and Capital One. The best cybersecurity jobs require at least a bachelor's, though some employers prefer a master's degree.

  • Information Security Analyst
  • Chief Information Security Officer
  • Information Security Consultant
  • IT Security Manager


Bioinformatics professionals study, store, and analyze biological information. A multidisciplinary subfield combining computer science and biology, bioinformatics looks for patterns in sequences of genetic material like DNA, genes, RNA, and protein. Bioinformatics workers develop the methods and software applications that accomplish these tasks.

The medical and pharmaceutical, industrial, environmental/government, and information technology fields benefit significantly from bioinformatics computer science technologies. Bioinformatics helps doctors in preventative and precision medicine detect diseases earlier to offer efficient targeted treatment.

PayScale reports that, as of June 2021, bioinformatics scientists earn an average annual salary of $96,230. The BLS projects faster-than-average job growth for bioengineers and biomedical engineers from 2019 to 2029.

Major employers for bioinformatics professionals include the Bureau of Land Management, the Department of Defense, hospitals, and research laboratories. Bioinformatics jobs require at least a bachelor's degree. Administrative, teaching, and supervisory positions may require a master's or Ph.D.

  • Bioinformatics Research Scientist
  • Bioinformatics Engineer
  • Biomedical Researcher
  • Bioengineer/Biomedical Engineer
  • Biostatistician
  • Computational Biologist
  • Agriculturalist
  • Software Programmer

Emerging Trends in IT

In addition to the computer science trends described above, IT professionals should keep an eye on other computing developments. Emerging trends in IT include big data analytics, virtual and augmented reality, 5G, and the internet of things.

Computer science workers can learn about computer science current events and new technologies by joining a professional organization. These groups offer online discussion groups, conferences, and industry journals. Staying current on trends in computer science can help computing professionals remain competitive in job interviews and promotion tracks.

Computer Science Topics to Study

Students can improve their career prospects by studying IT trends like those included on this page. They can pursue concentrations or elective classes in information security, machine learning, and bioinformatics. Some schools even offer entire degrees in artificial intelligence, cybersecurity, and robotics for learners seeking a specialized subfield education.

Check out the links below for more about computer science degrees.

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The latest computer science trends include artificial intelligence, edge computing, and quantum computing. IT professionals are also knowledgeable about developments in robotics and cybersecurity.

What new technologies in computer science should I learn?

Study new computer science technologies like artificial intelligence, data analytics, and machine learning. Other emerging areas of technology include virtual and augmented reality, UI/UX design, and quantum computing.

Which fields of computer science are in most demand?

Cybersecurity is one of the most in-demand fields in computer science. The BLS projects 31% job growth for information security analysts from 2019 to 2029.

What is the highest-paid job in IT?

The BLS reports that computer and information systems managers earned a median annual salary of $151,150 as of 2020, making it one of the highest-paid IT jobs.

How do I start a career in computer science?

Launch a computer science career by pursuing a degree that explores the latest technologies. Graduates with an associate degree qualify for entry-level IT jobs, but most computer science careers require at least a bachelor's in the field.

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Explained: Generative AI

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A quick scan of the headlines makes it seem like generative artificial intelligence is everywhere these days. In fact, some of those headlines may actually have been written by generative AI, like OpenAI’s ChatGPT, a chatbot that has demonstrated an uncanny ability to produce text that seems to have been written by a human.

But what do people really mean when they say “generative AI?”

Before the generative AI boom of the past few years, when people talked about AI, typically they were talking about machine-learning models that can learn to make a prediction based on data. For instance, such models are trained, using millions of examples, to predict whether a certain X-ray shows signs of a tumor or if a particular borrower is likely to default on a loan.

Generative AI can be thought of as a machine-learning model that is trained to create new data, rather than making a prediction about a specific dataset. A generative AI system is one that learns to generate more objects that look like the data it was trained on.

“When it comes to the actual machinery underlying generative AI and other types of AI, the distinctions can be a little bit blurry. Oftentimes, the same algorithms can be used for both,” says Phillip Isola, an associate professor of electrical engineering and computer science at MIT, and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL).

And despite the hype that came with the release of ChatGPT and its counterparts, the technology itself isn’t brand new. These powerful machine-learning models draw on research and computational advances that go back more than 50 years.

An increase in complexity

An early example of generative AI is a much simpler model known as a Markov chain. The technique is named for Andrey Markov, a Russian mathematician who in 1906 introduced this statistical method to model the behavior of random processes. In machine learning, Markov models have long been used for next-word prediction tasks, like the autocomplete function in an email program.

In text prediction, a Markov model generates the next word in a sentence by looking at the previous word or a few previous words. But because these simple models can only look back that far, they aren’t good at generating plausible text, says Tommi Jaakkola, the Thomas Siebel Professor of Electrical Engineering and Computer Science at MIT, who is also a member of CSAIL and the Institute for Data, Systems, and Society (IDSS).

“We were generating things way before the last decade, but the major distinction here is in terms of the complexity of objects we can generate and the scale at which we can train these models,” he explains.

Just a few years ago, researchers tended to focus on finding a machine-learning algorithm that makes the best use of a specific dataset. But that focus has shifted a bit, and many researchers are now using larger datasets, perhaps with hundreds of millions or even billions of data points, to train models that can achieve impressive results.

The base models underlying ChatGPT and similar systems work in much the same way as a Markov model. But one big difference is that ChatGPT is far larger and more complex, with billions of parameters. And it has been trained on an enormous amount of data — in this case, much of the publicly available text on the internet.

In this huge corpus of text, words and sentences appear in sequences with certain dependencies. This recurrence helps the model understand how to cut text into statistical chunks that have some predictability. It learns the patterns of these blocks of text and uses this knowledge to propose what might come next.

More powerful architectures

While bigger datasets are one catalyst that led to the generative AI boom, a variety of major research advances also led to more complex deep-learning architectures.

In 2014, a machine-learning architecture known as a generative adversarial network (GAN) was proposed by researchers at the University of Montreal. GANs use two models that work in tandem: One learns to generate a target output (like an image) and the other learns to discriminate true data from the generator’s output. The generator tries to fool the discriminator, and in the process learns to make more realistic outputs. The image generator StyleGAN is based on these types of models.  

Diffusion models were introduced a year later by researchers at Stanford University and the University of California at Berkeley. By iteratively refining their output, these models learn to generate new data samples that resemble samples in a training dataset, and have been used to create realistic-looking images. A diffusion model is at the heart of the text-to-image generation system Stable Diffusion.

In 2017, researchers at Google introduced the transformer architecture, which has been used to develop large language models, like those that power ChatGPT. In natural language processing, a transformer encodes each word in a corpus of text as a token and then generates an attention map, which captures each token’s relationships with all other tokens. This attention map helps the transformer understand context when it generates new text.

These are only a few of many approaches that can be used for generative AI.

A range of applications

What all of these approaches have in common is that they convert inputs into a set of tokens, which are numerical representations of chunks of data. As long as your data can be converted into this standard, token format, then in theory, you could apply these methods to generate new data that look similar.

“Your mileage might vary, depending on how noisy your data are and how difficult the signal is to extract, but it is really getting closer to the way a general-purpose CPU can take in any kind of data and start processing it in a unified way,” Isola says.

This opens up a huge array of applications for generative AI.

For instance, Isola’s group is using generative AI to create synthetic image data that could be used to train another intelligent system, such as by teaching a computer vision model how to recognize objects.

Jaakkola’s group is using generative AI to design novel protein structures or valid crystal structures that specify new materials. The same way a generative model learns the dependencies of language, if it’s shown crystal structures instead, it can learn the relationships that make structures stable and realizable, he explains.

But while generative models can achieve incredible results, they aren’t the best choice for all types of data. For tasks that involve making predictions on structured data, like the tabular data in a spreadsheet, generative AI models tend to be outperformed by traditional machine-learning methods, says Devavrat Shah, the Andrew and Erna Viterbi Professor in Electrical Engineering and Computer Science at MIT and a member of IDSS and of the Laboratory for Information and Decision Systems.

“The highest value they have, in my mind, is to become this terrific interface to machines that are human friendly. Previously, humans had to talk to machines in the language of machines to make things happen. Now, this interface has figured out how to talk to both humans and machines,” says Shah.

Raising red flags

Generative AI chatbots are now being used in call centers to field questions from human customers, but this application underscores one potential red flag of implementing these models — worker displacement.

In addition, generative AI can inherit and proliferate biases that exist in training data, or amplify hate speech and false statements. The models have the capacity to plagiarize, and can generate content that looks like it was produced by a specific human creator, raising potential copyright issues.

On the other side, Shah proposes that generative AI could empower artists, who could use generative tools to help them make creative content they might not otherwise have the means to produce.

In the future, he sees generative AI changing the economics in many disciplines.

One promising future direction Isola sees for generative AI is its use for fabrication. Instead of having a model make an image of a chair, perhaps it could generate a plan for a chair that could be produced.

He also sees future uses for generative AI systems in developing more generally intelligent AI agents.

“There are differences in how these models work and how we think the human brain works, but I think there are also similarities. We have the ability to think and dream in our heads, to come up with interesting ideas or plans, and I think generative AI is one of the tools that will empower agents to do that, as well,” Isola says.

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New computer code for mechanics of tissues and cells in three dimensions

Open-source supercomputer algorithm predicts patterning and dynamics of living materials and enables studying their behavior in space and time.

Biological materials are made of individual components, including tiny motors that convert fuel into motion. This creates patterns of movement, and the material shapes itself with coherent flows by constant consumption of energy. Such continuously driven materials are called "active matter." The mechanics of cells and tissues can be described by active matter theory, a scientific framework to understand shape, flows, and form of living materials. The active matter theory consists of many challenging mathematical equations.

Scientists from the Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG) in Dresden, the Center for Systems Biology Dresden (CSBD), and the TU Dresden have now developed an algorithm, implemented in an open-source supercomputer code, that can for the first time solve the equations of active matter theory in realistic scenarios. These solutions bring us a big step closer to solving the century-old riddle of how cells and tissues attain their shape and to designing artificial biological machines.

Biological processes and behaviors are often very complex. Physical theories provide a precise and quantitative framework for understanding them. The active matter theory offers a framework to understand and describe the behavior of active matter -- materials composed of individual components capable of converting a chemical fuel ("food") into mechanical forces. Several scientists from Dresden were key in developing this theory, among others Frank Jülicher, director at the Max Planck Institute for the Physics of Complex Systems, and Stephan Grill, director at the MPI-CBG. With these principles of physics, the dynamics of active living matter can be described and predicted by mathematical equations. However, these equations are extremely complex and hard to solve. Therefore, scientists require the power of supercomputers to comprehend and analyze living materials. There are different ways to predict the behavior of active matter, with some focusing on the tiny individual particles, others studying active matter at the molecular level, and yet others studying active fluids on a large scale. These studies help scientists see how active matter behaves at different scales in space and over time.

Solving complex mathematical equations

Scientists from the research group of Ivo Sbalzarini, TU Dresden Professor at the Center for Systems Biology Dresden (CSBD), research group leader at the Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG), and Dean of the Faculty of Computer Science at TU Dresden, have now developed a computer algorithm to solve the equations of active matter. Their work was published in the journal "Physics of Fluids" and was featured on the cover. They present an algorithm that can solve the complex equations of active matter in three dimensions and in complex-shaped spaces. "Our approach can handle different shapes in three dimensions over time," says one of the first authors of the study, Abhinav Singh, a studied mathematician. He continues, "Even when the data points are not regularly distributed, our algorithm employs a novel numerical approach that works seamlessly for complex biologically realistic scenarios to accurately solve the theory's equations. Using our approach, we can finally understand the long-term behavior of active materials in both moving and non-moving scenarios for predicting their dynamics. Further, the theory and simulations could be used to program biological materials or create engines at the nano-scale to extract useful work." The other first author, Philipp Suhrcke, a graduate of TU Dresden's Computational Modeling and Simulation M.Sc. program, adds, "thanks to our work, scientists can now, for example, predict the shape of a tissue or when a biological material is going to become unstable or dysregulated, with far-reaching implications in understanding the mechanisms of growth and disease."

A powerful code for everyone to use

The scientists implemented their software using the open-source library OpenFPM, meaning that it is freely available for others to use. OpenFPM is developed by the Sbalzarini group for democratizing large-scale scientific computing. The authors first developed a custom computer language that allows computational scientists to write supercomputer codes by specifying the equations in mathematical notation and let the computer do the work to create a correct program code. As a result, they do not have to start from scratch every time they write a code, effectively reducing code development times in scientific research from months or years to days or weeks, providing enormous productivity gains. Due to the tremendous computational demands of studying three-dimensional active materials, the new code is scalable on shared and distributed-memory multi-processor parallel supercomputers, thanks to the use of OpenFPM. Although the application is designed to run on powerful supercomputers, it can also run on regular office computers for studying two-dimensional materials.

The Principal Investigator of the study, Ivo Sbalzarini, summarizes: "Ten years of our research went into creating this simulation framework and enhancing the productivity of computational science. This now all comes together in a tool for understanding the three-dimensional behavior of living materials. Open-source, scalable, and capable of handling complex scenarios, our code opens new avenues for modeling active materials. This may finally lead us to understand how cells and tissues attain their shape, addressing the fundamental question of morphogenesis that has puzzled scientist for centuries. But it may also help us design artificial biological machines with minimal numbers of components."

The computer code that support the findings of this study are openly available in the 3Dactive-hydrodynamics github repository located at https://github.com/mosaic-group/3Dactive-hydrodynamics

The open source framework OpenFPM is available at https://github.com/mosaic-group/openfpm_pdata

Related Publications for the embedded computer language and the OpenFPM software library: https://doi.org/10.1016/j.cpc.2019.03.007 and https://doi.org/10.1140/epje/s10189-021-00121-x

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Materials provided by Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG) . Note: Content may be edited for style and length.

Journal Reference :

  • Abhinav Singh, Philipp H. Suhrcke, Pietro Incardona, Ivo F. Sbalzarini. A numerical solver for active hydrodynamics in three dimensions and its application to active turbulence . Physics of Fluids , 2023; 35 (10) DOI: 10.1063/5.0169546

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New research explores innovative methods in data visualization with Jaya algorithm

by Gulf University for Science and Technology

New research explores innovative methods in data visualization with Jaya algorithm

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    Please describe two topics of interest in Computer Science, one in your area of research and one related to COVID 19 but outside your area of research. Each paragraph must have: Title Clear and short explanation of the topic, understandable to a broader CS audience. Reason why the idea is interesting

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  13. Research in computer science: an empirical study

    Our objective in this study is to provide a detailed characterization of computer science research, along the dimensions identified above, by examining articles published in major computer science journals from 1995-1999. Our interest in this study goes beyond topic and research methods and includes other ways of characterizing research such ...

  14. 201 Best Computer Science Topics for 2023

    Here are some of the best computer science research topics you can find in 2023: How important is machine learning? The latest advancements in quantum computing The next level of the Internet Virtualization technologies Real-life applications of bioinformatics Introducing computer science in high school Define deep learning

  15. Top 100 Computer Science Topics for Research

    Computer security and forensics. Communication and computation limits. Environments and programming languages. Computer systems security and support for the digital democracy. This list comprises hot topics in computer science. Pick one of them and research it extensively to write a brilliant academic paper or essay.

  16. 100+ Computer Science Topics: A Comprehensive Guide

    By Gurpreet Kumar Computer Science is a vast and dynamic field that plays a fundamental role in today's technological landscape. This blog aims to provide an overview of various computer science topics, from core concepts to specialized areas and emerging trends.

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    This Research Topic focuses on developing, adapting, and optimizing machine learning approaches for enhancing cybersecurity. This Research Topic will provide researchers a platform for the convergence of interdisciplinary research techniques that combine methods from computer science, machine learning, and social science towards designing ...

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    Latest Research Topics in Computer Science. Big data and machine learning. Cloud computing. Internet of Things. Blockchain technology. Cybersecurity 6. Natural language processing. Augmented reality and virtual reality. Robotics.

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    Topics in research on computer science can be classified into various categories like artificial Intelligence, big data, and human-computer interaction, as well as security and privacy and engineering software if you're a college student or researcher in search of computer-related research paper subjects.

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    đŸ’Ÿ Top 10 Computer Science Topics 💡 Technologies & Computer Science: Research Ideas ⚙ Artificial Intelligence 💉 Biotechnology 📡 Communications and Media đŸ’»Computer Science & Engineering 🔋 Energy & Power Technologies 🍗 Food Technology đŸ˜· Medical Devices & Diagnostics 💊 Pharmaceutical Technologies 🚈 Transportation Conclusion 🔍 References

  21. 12 Most Emerging Research Areas in Computer Science in 2021

    12 Most Emerging Research Areas in Computer Science in 2021 1 Comment By: P. Chaudhary, B. Gupta Artificial Intelligence and Robotics

  22. 30 Interesting Computer Science Research Paper Topics

    30 Interesting Computer Science Research Paper Topics Computer science is science that changes, perhaps, the faster of all. Every month something happens - the machines become more powerful, the new languages of programming are invented and the new possibilities are opened before computer scientists.

  23. Computer Science Trends

    Computer and information research scientists, one potential AI career, earned a median annual salary of $126,830 as of 2020, with the BLS projecting much-faster-than-average growth for the profession from 2019 to 2029. ... Computer Science Topics to Study.

  24. Research Guides Homepage: CSC 200: Computer Science: Home

    Subjects: Computer Science Tags: computer science View this page in a format suitable for printers and screen-readers or mobile devices. 915 South Jackson Street ‱ Montgomery, Alabama 36101 ‱ (334) 604-9093

  25. Explained: Generative AI

    A generative AI system is one that learns to generate more objects that look like the data it was trained on. "When it comes to the actual machinery underlying generative AI and other types of AI, the distinctions can be a little bit blurry. Oftentimes, the same algorithms can be used for both," says Phillip Isola, an associate professor of ...

  26. Internet & Technology

    About Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions.

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    A numerical solver for active hydrodynamics in three dimensions and its application to active turbulence. Physics of Fluids, 2023; 35 (10) DOI: 10.1063/5.0169546. Max Planck Institute of Molecular ...

  28. New research explores innovative methods in data visualization with

    The Jaya algorithm, known for its simplicity and lack of need for extensive parameter tuning, has been adapted for this purpose by Dr. Fadi K. Dib, assistant professor in computer science at Gulf University for Science and Technology in Kuwait, and Prof. Peter Rodgers, a professor in the school of computing at the University of Kent in the U.K. ...