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Where Can I Get Help Writing My Thesis Online?
You’ve spent years preparing for your master’s degree or PhD. You’ve read, studied and spent hours of time and energy writing papers. Now you’ve arrived at the culmination of all this effort: writing your thesis. There are plenty of compelling stories about the time and energy that students have spent drafting their dissertations and theses.
The good news is that you’re not alone. While you certainly don’t want to hire someone to write your thesis for you, which goes against most institution policies and puts your academic integrity at risk, you can get plenty of help with certain aspects of your thesis online. Whether you’re looking for a little guidance or extensive assistance, various services can make writing or editing your thesis go smoothly.
Dissertation Editor
One of the greatest challenges of writing your thesis can be juggling your family or job responsibilities with your studies. The time that writing takes can add another layer of obligation to your already-packed schedule. Dissertation Editor is a company whose founder is a PhD-educated writer and professor, and it promises to help you complete your thesis or dissertation on time and in compliance with your university’s rules and regulations.
Dissertation Editor’s primary function is to guide you along in the writing process and provide a helping hand in understanding everything you need to take care of. It places you with a writer who specializes in your area of study, and this individual can help you organize and analyze your research while making sure that your thesis fits your writing style and personality. This company also specializes in helping with any statistical analysis that you use in your thesis.
Thesis Helpers
If you’re concerned about using a service to help you write your thesis because you think it’ll be obvious that you hired help, don’t worry. Thesis Helpers puts its team of experienced writers to work for you to help you craft a thesis that finishes your degree on a high note. No matter what level of help you need, from narrowing down a topic to advanced editing and proofreading, they’re available to help.
The writers have advanced degrees in their areas of expertise, and one of the best things about Thesis Helpers is that it gives you ultimate say in the final product of your thesis. This company can help you with revisions and additional research, and you can rest assured that your thesis will meet anti-plagiarism standards.
Best Dissertation
Sometimes when you’re writing a thesis or dissertation, you can get stuck on one section or chapter. You may not need assistance writing the whole thing, but getting some help with the exact portion you’re struggling with can come in handy. That’s one of the strengths of using Best Dissertation . You don’t have to rely on it for help with your entire thesis if it’s not what you need.
Like most of the top thesis-assistance services, Best Dissertation employs writers with advanced degrees who specialize in various fields of study. What truly sets this company apart is the live support that it offers any time of the day or night. It claims to take the stress and strain out of writing your dissertation or thesis.
While some companies place a premium on helping you get your thesis written, others emphasize the editing and proofreading process. If you don’t need help with writing but need a hand with proofreading and editing, Scribbr is a good option for you. Its editors can help you get a grasp on the grammar and tone that are appropriate for academic writing.
Scribbr doesn’t just provide boilerplate feedback that you can find anywhere. It offers personalized feedback aimed at helping you become a better writer in the long run. You can even see examples of how its editors work by looking at the company’s website.
My Assignment Help
Writing a thesis has its own challenges that other academic writing simply doesn’t, which is why the team at My Assignment Help offers its particular brand of expertise. If you need assistance with a dissertation or thesis at the PhD or master’s level, its writers have the level of education and experience to help you write an expertly crafted and edited thesis.
My Assignment Help prides itself on hiring subject matter experts, meaning you can pair up with a helper who already has an advanced degree in your field. They understand the nuances of academic writing that are specific to your area of study, and they can provide advice on everything from making your abstract more unique to crafting a thought-provoking conclusion.
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Molecules 2022 Best PhD Thesis Award
Dear Colleagues,
We are pleased to announce the winners of the Molecules 2022 Best PhD Thesis Award. This award is for two PhD students or recently qualified PhDs who have produced a highly anticipated thesis with great academic potential.
The award has been granted to:
“Development of Modern Sample Preparation Techniques Utilizing Novel Materials Combined with Chromatographic and Spectrometric Methods for the Determination of Environmental Pollutants in Food and Environmental Samples” By Dr. Natalia Manousi, Aristotle University of Thessaloniki, Greece
“Studying Protein Function with Fluorescent Nanoantennas” By Dr. Scott G. Harroun, University of Montreal, Canada
Each winner will receive CHF 800, an electronic certificate, and an offer to publish a featured paper in Molecules with the article processing charge (APC) waived before the end of 2023.
On behalf of the evaluation committee, we would like to congratulate the winners on their accomplishments. We would also like to take this opportunity to thank all the applicants for submitting their exceptional theses and the Award Committee for voting and helping with this award.
Kind regards, Molecules Award Team
Further Information
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Best Thesis Award
STAG 2022 will host, as usual, the EG-Italy thesis award ceremony. The award is dedicated to the memory of Matteo Dellepiane , whose passion, enthusiasm, and commitment for science and computer graphics will be of inspiration for all the recipients of this prize. The Matteo Dellepiane Award recognizes high-quality research thesis produced in Italian institutions. We aim to motivate young researchers, giving them the opportunity to present their works at an international scientific venue. Three awards will be granted: one for the best PhD thesis, one for the best MSc thesis, and one for the best BSc thesis. Awarded researchers will be announced in a dedicated session of the STAG 2022 Conference, during which they will also be invited to give a presentation about their work. They will also be granted a free registration ticket to the main conference.
Eligibility
We invite nominations of young researchers whose PhD/MS/BS thesis respect the following requirements: Subject in Computer Graphics, Image Processing, Visualization, Computer Vision, Virtual and Augmented Reality, Fabrication (in general all topics covered by Eurographics / Siggraph call for papers) Research mainly conducted in Italy or in collaboration with Italian institutes (at least one supervisor must be affiliated to an Italian institute) Diploma granted between January 1st 2021 and July 31st, 2022 .
Important dates
Submission instructions.
To participate to the contest, researchers need to provide the following documentation: - The name and affiliation of the candidate - A pdf version of the Thesis (in English or Italian) - Eventual slides of the Thesis presentation/defense in pdf format - The name of supervisors - Explicit mention of which publications resulted from the Thesis.
The documentation of candidate nominations must be sent to the Thesis Awards chairs, Luca Cosmo ([email protected]) and Alberto Jaspe-Villanueva ([email protected]), not later than August 31st, 2022.
Nominations can be sent by the candidate, the supervisor, or any other researcher. If you do not receive an acknowledgment, please contact the Thesis Awards chair again.
The Thesis Award Committee is composed by members of the international communities of the topics covered by the STAG conference. The Committee will consider the quality of the work, the review reports, the quality and impact of the publications derived from the Thesis, the completeness and coherence of the state of the art section in the Thesis, and any other relevant aspect of the work.
Thesis Award Committee members: Dario Allegra (University of Catania), Francesco Banterle (CNR-ISTI Pisa), Stefano Berretti (University of Firenze), Daniela Cabiddu (CNR-IMATI Genoa), Umberto Castellani (University of Verona), Gianmarco Cherchi (University of Cagliari), Paolo Cignoni (CNR-ISTI Pisa), Luca Cosmo (University of Venice), Alberto Jaspe (KAUST King Abdullah University of Science and Technology), Valeria Garro (Blekinge Institute of Technology), Andrea Giachetti (University of Verona), Enrico Gobbetti (CRS4 Cagliari), Prashant Goswami (Blekinge Institute of Technology), Marco Livesu (CNR-IMATI Genoa), Michela Mortara (CNR-IMATI Genoa), Ruggero Pintus (CRS4 Cagliari), Enrico Puppo (University of Genoa), Alberto Signoroni (University of Brescia), Marc Stamminger (Universität Erlangen-Nürnberg).
Additional information
For any question concerning best thesis award please send an e-mail to [email protected] or contact the members of the organization:
Luca Cosmo ( [email protected] ), Ca' Foscari University of Venice, Thesis Award co-chair Alberto Jaspe-Villanueva ( [email protected] ), KAUST King Abdullah University of Science and Technology, Thesis Award co-chair Gianmarco Cherchi ( [email protected] ), University of Cagliari (Italy), Conference chair
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Graduate College
Outstanding thesis and dissertation award.
The 2022 competition closes at 4 pm (CT) on 10/12/22.
The Graduate College’s annual Outstanding Thesis and Dissertation Award is given to the most outstanding master’s thesis and doctoral dissertation in each of the four Graduate Program divisions, as determined by the Graduate College Awards Committee. Each of the eight awards includes a monetary award.
Details Heading link Copy link
Eligibility details.
Historically, each program has been limited to ONE master’s and ONE doctoral nomination. We now allow large graduate programs (master’s + doctoral = 100 students enrolled annually, averaged over last five years) to submit TWO master’s theses and TWO dissertations for consideration. Please use our enrollment reports to verify eligibility.
Recipients of a first professional degree prior to the thesis are eligible for this award; however, nominees may not have received a PhD or comparable research degree in any discipline prior to the master’s or doctoral thesis that is being nominated, i.e., a student pursuing a second PhD and a student with a PhD pursuing a master’s degree would both be ineligible for this competition.
For the 2022 cycle, eligible students are those whose degree was officially awarded by UIC in Fall 2021, Spring 2022, or Summer 2022.
- Check enrollments at "data resources" tab
An application for the award consists of six parts
The program/department collects and submits the following items as a single PDF file to the Graduate College via a secure link, for review by the Awards Committee:
- A completed Annual Graduate College Outstanding Thesis and Dissertation Award Application (fillable Word template).
- A letter of nomination from the Director of Graduate Studies or Department Head discussing the student in general terms.
- A letter of support from the primary advisor (i.e., the advisor responsible for the research for the thesis) that explains the impact of this research on the field.
- An abstract. Theses may use the “summary” submitted to the Graduate College (cf. the Graduate College Thesis Manual ); however, it should be renamed “Abstract.” Doctoral dissertation abstracts may not exceed five (5) pages double-spaced and appendices (with charts, tables, figures, and/or references) may not exceed an additional ten (10) pages. S upplied by student.
- A concise (300-word maximum) description of the significance of the research presented in the dissertation or thesis. This must be written in language suitable for the educated lay person. Supplied by student.
- A current curriculum vita that includes a list of awards, honors, publications, and presentations. Maximum of 5 pages. Supplied by student.
- 2022 OTD Application
- GC Thesis Manual
frequently asked questions
FAQs on Nominating Students and Preparing Applications for the OUTSTANDING THESIS AND DISSERTATION AWARD
Brief Description of the Award
The Graduate College’s annual Outstanding Thesis and Dissertation Award is given to the most outstanding master’s thesis and doctoral dissertation in each of the four Graduate Program divisions, as determined by the Graduate College Awards Committee. Each of the eight awards include a monetary award.
Eligibility and Department Selection
- Who is eligible for nomination for this award?
- Only a student whose program is within and whose dissertation/thesis is approved by the Graduate College
- Only a student who already has graduated.
- For the 2022 cycle, students whose degree was awarded by UIC in Fall 2021, Spring 2022, or Summer 2022.
- How many Outstanding Thesis nominees can a department put forward?
- Each calendar year, a graduate program under the auspices of the Graduate College can nominate one master’s and one doctoral student. We allow large graduate programs (master’s + doctoral = 100 students enrolled annually, averaged over the last five years) to submit TWO master’s theses and TWO dissertations.
- Up to one master’s and one doctoral award is given per disciplinary division: Arts and Humanities; Behavioral and Social Sciences; Engineering, Mathematics and Physical Sciences; and Life Sciences.
- How are the nominations reviewed and the winners chosen? What criteria are used by the Outstanding Thesis and Dissertation Awards Committee to evaluate the nominations? And, how will this affect selection of nominees?
- The Graduate Awards Committee has six members for each of the major disciplinary divisions in at the University: Arts and Humanities, Behavioral and Social Sciences, Engineering, Mathematics and Physical Sciences, and Life Sciences. The committees include faculty from a wide range of disciplines.
- Applications are reviewed by the Graduate Awards Committee and evaluated for academic excellence on the basis of research significance, thesis abstract, and other criteria as deemed appropriate by the committee, including the recommendation from the nominating program/department.
- Nominating departments can ensure that their applicants have the best possible chance by:
(1) Selecting students who are truly competitive for the award; i.e. have significant accomplishments that stand out from the pool of other candidates.
(2) Providing a detailed and clear explanation in the DGS/Head/Chair Nomination Letter and in the letter of support by the thesis advisor for why the student was selected. Some points to highlight may include not only the scholarly merits of the thesis and thesis research, but also significant publications or papers that came out of the thesis research, external recognition of the excellence of the research, and post-doctoral opportunities that have arisen from the research.
(3) Encouraging the student to carefully craft their statement of the Significance of the Research, with the understanding that the statement should be written for an evaluation committee that include faculty from related disciplines.
(4) Submitting an abstract that is reflective of the thesis, and is well written and logically organized. The abstract for this submission does have a maximum length of five (5) pages double-spaced in addition to appendices (charts, tables, references) not exceeding ten pages.
(5) Preparing the application well, minimizing errors, checking for missing components, avoiding poorly written text, and allowing enough time for both the student and faculty involved to craft strong statements.
Preparing and Submitting the Nominating Packets in the Department
- How are the nominating packets submitted?
- Departments should submit their nominations electronically. All of the forms required in the nomination packet are on-line and consist of fillable Microsoft Word documents [see below] that are easily converted into a single pdf file for each student nominee [see below for instructions on how to convert files to pdfs]. Letters written by recommenders and the Director of Graduate Studies will need to be scanned (or otherwise reproduced) and added to the student’s pdf file. Most departments will have access to Adobe Acrobat software that converts both scanned documents and Word files to a pdf format.
- Department Program Coordinators or DGSs who are experiencing difficulties in creating digital files should contact Benn Williams ( [email protected] ).
- Where do I start in obtaining the paperwork and instructions for preparing and submitting the applications?
- The instructions for preparing and submitting the applications, and the relevant forms, are found here: https://grad.uic.edu/funding-awards/otd-2/ .
- Submission is via email to a Box account dedicated to the four disciplinary divisions. If you do have a problem, please contact Benn Williams ( [email protected] ) with any questions.
- How should the paperwork be organized?
- To nominate a student for the Annual Outstanding Thesis and Dissertation Award , programs must submit a single PDF file for each nominee (contents described below) to the Graduate College by published deadline. No individual extensions will be granted. Nomination PDF files consist of the following materials compiled in the order specified.
- A completed Annual Graduate College Outstanding Thesis and Dissertation Award Application (fillable Word template).
- An abstract. Theses may use the “summary” submitted to the Graduate College (cf. the Graduate College Thesis Manual ); however, it should be renamed “Abstract.” Doctoral dissertation abstracts may not exceed five (5) pages double-spaced and appendices (with charts, tables, figures, and/or references) may not exceed an additional ten (10) pages. (This element has been modified for the 2022 competition to better synchronize with regional and national competitions.) S upplied by student.
- A concise (300-word maximum) description of the significance of the research presented in the dissertation or thesis. This must be written in language suitable for the educated lay person. Supplied by student.
- Please prepare nomination packets carefully. Poorly prepared or incomplete nominations are a common cause of otherwise deserving students failing to receive fellowship awards.
- How do I digitize and submit the nomination?
- Save the forms/documents listed above as one single PDF per nominee . We recommend using Adobe DC to combine files. Alternatively, most of the required documents likely originate in Microsoft Word and can be converted to pdfs using most standard office software. Letters, statements, and other components of the student applications coming into the department as paper files can be scanned into pdfs.
- The PDF files should be saved using the following naming convention :
OTD_YearofAward_ProgramNameAbbreviation_Degree_NomineeLastNameFirstI nitial.pdf
Example – A 2022 PhD nominee from philosophy named Tony Fauci:
OTD_2022_Phil_PHD_FauciT.pdf
- Do not leave spaces in the pdf name.
- There is a 1GB size limit on each individual file.
- The Graduate College uses University of Illinois Box for all submissions. Box accounts are free to all UIC students, staff and faculty. Box should send you a receipt upon a successful submission.
Revised September 9, 2022 bw
When you are ready to upload the PDF file to the Graduate College, click on the Box link provided below and file in the correct division by 4 p.m. (Central) on October 12, 2022 .
- The Graduate College uses the secure University of Illinois Box for all submissions. Box accounts are free to all UIC students, staff and faculty.
- Email your file(s) to the appropriate division.
Documents Collected and Submitted by the Department/Program
The Director of Graduate Studies (or designate) puts forth the nomination of the outstanding thesis or dissertation.
- Completed award application (fillable Word template).
- Letter of nomination from the Director of Graduate Studies or Department Head/Chair discussing the student in general terms.
- Letter of support from the primary advisor (i.e., the advisor responsible for the research for the thesis) that explains the impact of this research on the field.
- Concise description of the significance of the research; written for the the educated lay person.
Nomination will be a single PDF following the naming convention:
Example – A 2022 PhD nominee from philosophy named Tony Fauci: OTD_2022_Phil_PHD_FauciT.pdf
**Staff/faculty uploaders: Simply select the correct divisional folder below to initiate your email. Attach your PDF(s). Box should send you a receipt upon a successful submission.**
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DEADLINE: 4pm (Central) on October 12, 2022
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PhD Thesis Award
Crs phd thesis award – best doctoral thesis in drug delivery research.
We are inviting scientists who completed their PhD thesis in 2022 Drug Delivery Science to apply for the CRS PhD Thesis Award . This award serves to recognize outstanding doctoral research and scientific contributions to the multidisciplinary field of drug delivery.
Eligibility
- Application deadline is March 16. 2023 by 11:59 pm ET
- PhD thesis defense date - January 1, 2022, and December 31, 2022
- Nomination needs to be supported by the thesis supervisor(s)
- Nominee and supervisor need to be CRS Members
- Max. 1 candidate per department/supervisor per year
Nomination Requirements - OPEN
- Full-text version of your thesis (pdf; included in the applicant form)
- Curriculum Vitae (pdf; max. 3 page; included in the applicant form)
- Completed and signed applicant form
- Completed and signed supervisor form
PLEASE NOTE, both the applicant form AND the supervisor form are required to be considered.
Selection Process
The PhD Thesis Award Committee will make the selection of the awardee.
- Will receive a certificate and monetary award
- Will present his/her research at the CRS Annual Meeting
- Bronze and silver awards for the finalists / runners-up
- Finalists will be presented on the CRS website
- Finalists will summarize their work as an invited paper in the Journal of Controlled Release
Past Recipients
PhD Dissertation Award
About the award for best dissertation in public policy and management.
The Association for Public Policy Analysis and Management (APPAM) seeks to recognize emergent scholars in the field by presenting an award for the best PhD dissertation in public policy and management.
For the 2022 nominations, any dissertation that has been completed in the academic years 2020 - 2021 or 2021 - 2022, and granted a degree in that period, is eligible for consideration. No dissertation that has been completed prior to May 1, 2020 will be accepted and previously submitted dissertations will not be considered. Dissertations from any discipline are acceptable as long as they deal substantively with public policy issues and are nominated by a faculty member from an APPAM institutional member university. The faculty member does not need to be the major adviser or supervisor of the student’s dissertation, but can nominate the dissertation based on the belief that it makes a strong contribution to policy analysis.
Nominations for 2022 are now closed. Applications for 2023 will open in late spring.
Congratulations to the 2022 Recipient!
Katharine Nelson Housing Initiative at Penn (HIP) The 2022 PhD Dissertation Award for Best Dissertation in Public Policy and Management will be presented to Katharine Nelson . Currently serving as Director of Research at the Housing Initiative at Penn (HIP), Nelson received her PhD from Rutgers University in 2022. Her dissertation, FHA and the Dual Mortgage Delivery System in Philadelphia , covers topics that address urban change, racial inequality, and public policy. Learn more about her work here .
Honorable Mentions Hector Blanco, Massachusetts Institute of Technology The Economic Effects of Public Housing Programs Isabelle Cohen, University of California, Berkeley Essays on Public Finance and Development
Prior Winners
2020 - 2021.
George Zuo, University of Maryland Essays on Bridging Economic and Educational Disparities in America
Honorable Mentions Zachary Bleemer, University of California Berkeley On the Meritocratic Allocation of Higher Education Brandyn Churchill, Vanderbilt University Three Essays in Health Economics Ezra Karger, University of Chicago Essays on the Measurement of Income in Economic Analysis
Jun Li, University of Michigan Medicare Incentives, Payment Reform, and Quality in the Nursing Home Health Care Sector
2019 - 2020
Cody Tuttle, University of Maryland Government Responses to Crime and Racial Inequality
Honorable mentions Theresa Anderson, George Washington University What If Mom Went Back to School? A Mixed Methods Study of Effects and Experiences for Both Generations When Mothers Return to School
Andrew Bacher-Hicks, Harvard University Essays on the Economics of Education
2018 - 2019
Shiran Victoria Shen, Stanford University Political Pollution Cycle: An Inconvenient Truth and How to Break It
Honorable mentions: Patricio Dominiquez Rivera, Inter-American Development Bank and Elizabeth Pérez-Chiqués, CIDE
2017 - 2018
Garima Siwach, University at Albany, State University of New York Impact of Employment Barriers on Individuals with Criminal Records: An Econometric Evaluation of Criminal Records in New York Honorable mentions: Y. Nina Gao, University of Chicago and Allison C. Kelly, University of Washington
2016 - 2017
Mallory Flowers, Georgia Tech School of Public Policy Green Certification Pathways: The Roles of Public Goods, Private Goods and Certification Schemes Honorable mentions: Alan Zarychta, University of Colorado - Boulder
2015 - 2016
Vincent Reina, University of Southern California, Sol Price School of Public Policy The Impact of Mobility and Government Rental Subsidies on the Welfare of Households and Affordability of Markets Honorable mentions: Eric Roberts, John Hopkins University and Daniel Sebastian Tello-Trillo, Vanderbilt University
Manasi Deshpande, Massachusetts Institute of Technology (MIT) Essays on the Effects of Disability Insurance Honorable mentions: Alexander Smith, University of Virginia and Gabriel Cardona-Fox, University of Texas, Austin
Anjali Adukia, Harvard University The Role of Basic Needs in Educational Decisions: Essays in Education and Development Economics Honorable mention: Sara Heller, University of Chicago
Sarah Anzia, University of California, Berkeley Election Timing and the Political Influence of the Organized Honorable mention: Hosung Sohn, University of California, Berkeley
Daeho Kim, Brown University
Essays in Health Economics
Honorable mentions:
Chloe Gibbs, University of Chicago
Christopher Robert, Harvard University JFK School of Government
Kurt Lavetti, University of California-Berkeley
Essays on the Estimation of Prices in Implicit Markets
Cassandra Marie Doll Hart, Northwestern University
Heidi Williams, MIT
Essays on Technological Change in Healthcare Markets
Kristin Seefeldt, University of Michigan
Judith Scott-Clayton, Harvard University JFK School of Government
2008 - 2009
Steven Hemelt, University of Maryland-Baltimore County
Essays in Education Policy: Accountability, Achievement, and Access
None recognized for this year
Haitao Yin, University of Pennsylvania
The Environmental and Economic Impacts of Environmental Regulations: The Case of Underground Storage Tank Regulations
Maria Fitzpatrick, University of Virginia
Jeremy Rosner, University of Maryland
Christopher Herbst, University of Maryland-College Park
Effects of Social Policy Reforms and the Economy on Welfare Participation and Employment of Single Mothers
Douglas Carr, University of Kentucky
Stephanie Cellini, University of California-Los Angeles
Kilkon Ko, University of Pittsburgh, Graduate School of Public and International Affairs
Behaviors of Policy Analysts in Public Investment Decisions: How Policy Analysts Make Decisions
Leah Brooks, University of California-Los Angeles
Elizabeth Votruba-Drzal, Northwestern University
Asim Zia, Georgia Institute of Technology School of Public Policy
Cooperative and Non-Cooperative Decision Behaviors in Response to the Inspection and Maintenance Program in the Atlanta Airshed, 1997-2001
Margaret Patrick Haist, University of Kentucky
Sergio Fernandez, University of Georgia
Shreyasi Jha, University of North Carolina-Chapel Hill
Linkages Between Trade and Liberalization and Environmental Policy: Evidence from India
Zhong Yi Tong, University of Maryland
Jesse Levin, University of Amsterdam and Tinbergen Institute
Rucker Johnson, University of Michigan
Essays on Urban Spatial Structure, Job Search and Job Mobility
R. Karl Rethemeyer, Harvard University JFK School of Government
Gail Corrado, University of North Carolina-Chapel Hill
Mark Long, University of Michigan
The Effects of Education Policy on College Entry and Household Savings
Brian Jacob, University of Chicago
Katherine Magnuson, Northwestern University
Jacob Hacker, Yale University
Boundary Wars: Political Struggle Over Public and Private Social Benefits in the U.S.
Jean Marie Abraham, Carnegie Mellon University
Shanti Rabindran Gamper, Massachusetts Institute of Technology
Susanna Loeb, University of Michigan
Economic Analyses of Elementary and Secondary School Resource Provision
Laura J. Dugan, Carnegie Mellon University
Patrick McEwan, Stanford University
Meredith Phillips, Northwestern University
Early Inequalities: The Development of Ethnic Differences in Academic Achievement During Childhood
Karen Baehler, University of Maryland
Carol Silva, University of Rochester
Kevin Volpp, University of Pennsylvania
Market-based Reforms and the Impact on Quality of Care: An Examination of the Quality Impacts of the Transition from Hospital Rate-Setting to Price Competition in New Jersey
Kim Rueben, Massachusetts Institute of Technology
Katherine Baicker, Harvard University JFK School of Government
Xavier De Souza Briggs, Harvard University JFK School of Government
Brown Kids in White Suburbs: Housing Mobility, Neighborhood Effects and the Social Capital of Poor Youth
Ingrid Gould Ellen, New York University
Rebecca London, Northwestern University
Sheila E. Murray, University of Maryland-College Park
Two Essays on the Distribution of Education Resources and Outcomes
Johannes M. Bos, New York University Wagner School of Public Service
The Labor Market Value of Remedial Education: Evidence from Time Series Data on an Experimental Program for School Dropouts
Kathryn A. Foster, Princeton University Woodrow Wilson School of Public and International Affairs
Special Districts and the Political Economy of Metropolitan Service Delivery
Thomas J. Nechyba, University of Rochester
Fiscal Federalism and Local Public Finance: A General Equilibrium Approach with Voting
Kenneth Langa, University of Chicago Harris School of Policy Studies
Medicaid Cost-Containment in the 1980s: Did It Encourage Interpayer Differences in Hospital Care
Thomas J. Kane, Harvard University JFK School of Government
College Entry by Blacks Since 1970: The Role of Tuition, Financial Aid, Local Economic Conditions and Family Background
About the 2019 Recipient: Shiran Victoria Shen, University of Virginia
European Association For Signal Processing
Best PhD Award
EURASIP has created and is maintaining a database of PhD manuscripts at https://theses.eurasip.org , which is presently the World’s database of this kind in the area of signal processing. The database also keeps track of download statistics.
Each year a committee of experts nominated by the EURASIP BoD selects up to three theses related to different areas of signal processing to be awarded. The selection process is based on the evaluation of the impact of the theses, their subsequent journal and conference publications and related citations received, on the review reports of three independent reviewers, and on the download statistics. The download statistics are instrumental only in forming a shortlist of the top 30 theses.
To nominate a thesis that is not uploaded in the d atabase of PhD manuscripts at https://theses.eurasip.org , you can use the nomination form .
In order to select the 20xx best PhD theses, a temporal window of the years 20xx-5 through 20xx-3 is considered. The awards are presented during the Awards Ceremony at EUSIPCO 20xx. Each award consists of a certificate and of a monetary prize of 1K to cover travelling & accommodation cost for attending the Awards Ceremony
Thesis Title : Informed spatial filters for speech enhancement Author : Maja Taseska Institution : Friedrich-Alexander Universitat Erlangen-Nornberg, Germany Year : 2018 Supervisor : Emanuel A. P. Habets
Thesis Title : Mining the ECG: Algorithms and Applications Author : Carolina Varon Institution : KU Leuven, Belgium Year : 2015 Supervisor : Sabine Van Huffel and Johan Suykens
Thesis Title : Dereverberation and noise reduction techniques based on acoustic multi-channel equalization Author : Ina Kodrasi Institution : University of Oldenburg, Germany Year : 2015 Supervisor : Simon Doclo
Thesis Title : Matrix Designs and Methods for Secure and Efficient Compressed Sensing Author : Valerio Cambareri Institution : University of Bologna, Italy Year : 2015 Supervisor : Riccardo Rovatti and Gianluca Setti
Thesis Title : Distributed Caching Methods in Small Cell Networks Author : Ejder Bastug Institution : CentraleSupélec, Université Paris-Saclay, France Year : 2015 Supervisor : Mérouane Debbah, Jean-Claude Belfiore, Mehdi Bennis
Thesis Title : Flexible Multi-Microphone Acquisition and Processing of Spatial Sound Using Parametric Sound Field Representations Author : Oliver Thiergart Institution : Friedrich-Alexander-Universitat Erlangen-Nurnberg, Germany Year : 2015 Supervisor : Emanuël Habets
Thesis Title : Sparsity Models for Signals: Theory and Applications Author : Raja Giryes Institution : Technion, Israel Year : 2014 Supervisor : Michael Elad
Thesis Title : Signal Design for Active Sensing and Communications Author : Mojtaba Soltanalian Institution : Uppsala University, Sweden Year : 2014 Supervisor : Petre Stoica
Thesis Title : Massive MIMO: Fundamentals and System Designs Author : Quoc Hien Ngo Institution : Linkoping University, Sweden Year : 2016 Supervisor : Erik G. Larsson
Thesis Title : Design and Analysis of Duplexing Modes and Forwarding Protocols for OFDM(A) Relay Links Author : Taneli Riihonen Institution : Aalto University, Finland Year : 2014 Supervisor : Risto Wichman and Stefan Werner
Thesis Title : Distributed Video Coding for Wireless Lightweight Multimedia Applications Author : Nikos Deligiannis Institution : Vrije Universiteit Brussel, Belgium Year : 2012 Supervisor : Adrian Munteanu and Peter Schelkens
Thesis Title : Multiantenna Cellular Communications: Channel Estimation, Feedback, and Resource Allocation Author : Emil Bjornson Institution : KTH Royal Institute of Technology, Sweden Year : 2011 Supervisor : Björn Ottersten, Mats Bengtsson
Thesis Title : Advanced Algebraic Concepts for Efficient Multi-Channel Signal Processing Author : Florian Roemer Institution : Technische Universität Ilmenau, Germany Year : 2012 Supervisor : Martin Haardt
Thesis Title : Design and Exploration of Radio Frequency Identification Systems by Rapid Prototyping Author : Christoph Angerer Institution : Technische Universität Wien, Austria Year : 2010 Supervisor : Markus Rupp, Robert Weigel
Thesis Title : GNSS Array-based Acquisition: Theory and Implementation Author : Javier Arribas Institution : Universitat Politècnica de Catalunya, Spain Year : 2012 Supervisor : Carles Fernandez
Thesis Title : Bayesian Signal Processing Techniques for GNSS Receivers: from multipath mitigation to positioning Author : Pau Closas Institution : Universitat Politècnica de Catalunya, Spain Year : 2009 Supervisor : Carles Fernandez
Thesis Title : Filter Bank Techniques for the Physical Layer in Wireless Communications Author : Tobias Hidalgo Stitz Institution : Tampere University of Technology, Finland Year : 2010 Supervisor : Markku Renfors
Thesis Title : Multiple-description lattice vector quantization Author : Jan Ostergaard Institution : Delft University of Technology, The Netherlands Year : 2007 Supervisor : R. I. Lagendijk, R. Heusdens
Thesis Title : Estimation of Nonlinear Dynamic Systems: Theory and Applications Author : Thomas Schon Institution : Linkoping University, Sweden Year : 2006 Supervisor : Fredrik Gustafsson
Thesis Title : Adapted Fusion Schemes for Multimodal Biometric Authentication Author : Julian Fierrez Institution : Escuela Politecnica Superior, Universidad Autonoma de Madrid, Spain Year : 2006 Supervisor : Javier Ortega-Garcia
Thesis Title : Super-Resolution Image Reconstruction Using Non-Linear Filtering Techniques Author : Mejdi Trimeche Institution : Tampere University of Technology, Finland Year : 2006 Supervisor : Moncef Gabbouj
Thesis Title : Geometric Approach to Statistical Learning Theory through Support Vector Machines (SVM) with Application to Medical Diagnosis Author : Michael Mavroforakis Institution : University of Athens, Greece Year : 2008 Supervisor : Sergios Theodoridis
Thesis Title : Signal processing of FMCW Synthetic Aperture Radar data Author : Adriano Meta Institution : Delft University of Technology, The Netherlands Year : 2006 Supervisors : P. Hoogeboom, L. P. Ligthart
2022 Dean’s Awards for Outstanding PhD Theses
Fifty-nine UNSW PhD candidates have been honoured in this year’s awards.
The Dean’s Awards for Outstanding PhD Theses recognises high quality PhD theses produced at UNSW.
To receive this award, candidates must produce a thesis that requires only minimal corrections, receive outstanding and/or excellent levels of achievement for all examination criteria, and in the opinion of both examiners is in the top ten per cent of PhD theses they have examined. Examiners are external to the University and are leaders in their fields.
“UNSW’s PhD candidates are a vital part of our research efforts and these awards recognise the outstanding theses examined in the last year,” said Professor Jonathan Morris, Pro Vice-Chancellor Research Training & Entrepreneurship and Dean of Graduate Research.
“Given the challenges of the past two years, these graduates should be commended for their achievements.”
The awards are listed below by Faculty. Further details about this award have been published on the HDR Hub .
Faculty of Arts, Design & Architecture
UNSW Business School
Faculty of Engineering
Faculty of Law & Justice
Faculty of Medicine & Health
Faculty of Science
UNSW Canberra
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Student Contributions ACM Doctoral Dissertation Award
Superior research and writing by doctoral candidates in computer science and engineering
- ACM Doctoral Dissertation Award
About ACM Doctoral Dissertation Award
Presented annually to the author(s) of the best doctoral dissertation(s) in computer science and engineering. The Doctoral Dissertation Award is accompanied by a prize of $20,000, and the Honorable Mention Award is accompanied by a prize totaling $10,000. Winning dissertations will be published in the ACM Digital Library as part of the ACM Books Series.
Recent Doctoral Dissertation Award News
2021 acm doctoral dissertation award.
Manish Raghavan is the recipient of the 2021 ACM Doctoral Dissertation Award for his dissertation " The Societal Impacts of Algorithmic Decision-Making ." Raghavan’s dissertation makes significant contributions to the understanding of algorithmic decision making and its societal implications, including foundational results on issues of algorithmic bias and fairness.
Algorithmic fairness is an area within AI that has generated a great deal of public and media interest. Despite being at a very early stage of his career, Raghavan has been one of the leading figures shaping the direction and focus of this line of research.
Raghavan is a Postdoctoral Fellow at the Harvard Center for Research on Computation and Society. His primary interests lie in the application of computational techniques to domains of social concern, including algorithmic fairness and behavioral economics, with a particular focus on the use of algorithmic tools in the hiring pipeline. Raghavan received a BS degree in Electrical Engineering and Computer Science from the University of California, Berkeley, and MS and PhD degrees in Computer Science from Cornell University.
Honorable Mentions for the 2021 ACM Doctoral Dissertation Award go to Dimitris Tsipras of Stanford University, Pratul Srinivasan of Google Research and Benjamin Mildenhall of Google Research.
Dimitris Tsipras’ dissertation, “ Learning Through the Lens of Robustness ,” was recognized for foundational contributions to the study of adversarially robust machine learning (ML) and building effective tools for training reliable machine learning models. Tsipras made several pathbreaking contributions to one of the biggest challenges in ML today: making ML truly ready for real-world deployment.
Tsipras is a Postdoctoral Scholar at Stanford University. His research is focused on understanding and improving the reliability of machine learning systems when faced with the real world. Tsipras received a Diploma in Electrical and Computer Engineering from the National Technical University of Athens, as well as SM and PhD degrees in computer science from the Massachusetts Institute of Technology (MIT).
Pratul Srinivasan and Benjamin Mildenhall are awarded Honorable Mentions for their co-invention of the Neural Radiance Field (NeRF) representation, associated algorithms and theory, and their successful application to the view synthesis problem. Srinivasan’s dissertation, " Scene Representations for View Synthesis with Deep Learning ," and Mildenhall’s dissertation, “ Neural Scene Representations for View Synthesis ,” addressed a long-standing open problem in computer vision and computer graphics. That problem, called “view synthesis” in vision and “unstructured light field rendering” in graphics, involves taking just a handful of photographs of a scene and predicting new images from any intermediate viewpoint. NeRF has already inspired a remarkable volume of follow-on research, and the associated publications have received some of the fastest rates of citation in computer graphics literature—hundreds in the first year of post-publication.
Srinivasan is a Research Scientist at Google Research, where he focuses on problems at the intersection of computer vision, computer graphics, and machine learning. He received a BSE degree in Biomedical Engineering and BA in Computer Science from Duke University and a PhD in Computer Science from the University of California, Berkeley.
Mildenhall is a Research Scientist at Google Research, where he works on problems in computer vision and graphics. He received a BS degree in Computer Science and Mathematics from Stanford University and a PhD in Computer Science from the University of California, Berkeley.
2020 ACM Doctoral Dissertation Award
Chuchu Fan is the recipient of the 2020 ACM Doctoral Dissertation Award for her dissertation, “ Formal Methods for Safe Autonomy: Data-Driven Verification, Synthesis, and Applications .” The dissertation makes foundational contributions to verification of embedded and cyber-physical systems, and demonstrates applicability of the developed verification technologies in industrial-scale systems.
Fan’s dissertation also advances the theory for sensitivity analysis and symbolic reachability; develops verification algorithms and software tools (DryVR, Realsyn); and demonstrates applications in industrial-scale autonomous systems.
Key contributions of her dissertation include the first data-driven algorithms for bounded verification of nonlinear hybrid systems using sensitivity analysis. A groundbreaking demonstration of this work on an industrial-scale problem showed that verification can scale. Her sensitivity analysis technique was patented, and a startup based at the University of Illinois at Urbana-Champaign has been formed to commercialize this approach.
Fan also developed the first verification algorithm for “black box” systems with incomplete models combining probably approximately correct (PAC) learning with simulation relations and fixed point analyses. DryVR, a tool that resulted from this work, has been applied to dozens of systems, including advanced driver assist systems, neural network-based controllers, distributed robotics, and medical devices.
Additionally, Fan’s algorithms for synthesizing controllers for nonlinear vehicle model systems have been demonstrated to be broadly applicable. The RealSyn approach presented in the dissertation outperforms existing tools and is paving the way for new real-time motion planning algorithms for autonomous vehicles.
Fan is the Wilson Assistant Professor of Aeronautics and Astronautics at the Massachusetts Institute of Technology, where she leads the Reliable Autonomous Systems Lab. Her group uses rigorous mathematics including formal methods, machine learning, and control theory for the design, analysis, and verification of safe autonomous systems. Fan received a BA in Automation from Tsinghua University. She earned her PhD in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign.
Honorable Mentions for the 2020 ACM Doctoral Dissertation Award go to Henry Corrigan-Gibbs and Ralf Jung .
Corrigan-Gibbs’s dissertation, “ Protecting Privacy by Splitting Trust ,” improved user privacy on the internet using techniques that combine theory and practice. Corrigan-Gibbs first develops a new type of probabilistically checkable proof (PCP), and then applies this technique to develop the Prio system, an elegant and scalable system that addresses a real industry need. Prio is being deployed at several large companies, including Mozilla, where it has been shipping in the nightly version of the Firefox browser since late 2019, the largest-ever deployment of PCPs.
Corrigan-Gibbs’s dissertation studies how to robustly compute aggregate statistics about a user population without learning anything else about the users. For example, his dissertation introduces a tool enabling Mozilla to measure how many Firefox users encountered a particular web tracker without learning which users encountered that tracker or why. The thesis develops a new system of probabilistically checkable proofs that lets every browser send a short zero-knowledge proof that its encrypted contribution to the aggregate statistics is well formed. The key innovation is that verifying the proof is extremely fast.
Corrigan-Gibbs is an Assistant Professor in the Electrical Engineering and Computer Science Department at the Massachusetts Institute of Technology, where he is also a member of the Computer Science and Artificial Intelligence Lab. His research focuses on computer security, cryptography, and computer systems. Corrigan-Gibbs received his PhD in Computer Science from Stanford University.
Ralf Jung’s dissertation, “ Understanding and Evolving the Rust Programming Language ,” established the first formal foundations for safe systems programming in the innovative programming language Rust. In development at Mozilla since 2010, and increasingly popular throughout the industry, Rust addresses a longstanding problem in language design: how to balance safety and control. Like C++, Rust gives programmers low-level control over system resources. Unlike C++, Rust also employs a strong “ownership-based” system to statically ensure safety, so that security vulnerabilities like memory access errors and data races cannot occur. Prior to Jung’s work, however, there had been no rigorous investigation of whether Rust’s safety claims actually hold, and due to the extensive use of “unsafe escape hatches” in Rust libraries, these claims were difficult to assess.
In his dissertation, Jung tackles this challenge by developing semantic foundations for Rust that account directly for the interplay between safe and unsafe code. Building upon these foundations, Jung provides a proof of safety for a significant subset of Rust. Moreover, the proof is formalized within the automated proof assistant Coq and therefore its correctness is guaranteed. In addition, Jung provides a platform for formally verifying powerful type-based optimizations, even in the presence of unsafe code.
Through Jung's leadership and active engagement with the Rust Unsafe Code Guidelines working group, his work has already had profound impact on the design of Rust and laid essential foundations for its future.
Jung is a post-doctoral researcher at the Max Planck Institute for Software Systems and a research affiliate of the Parallel and Distributed Operating Systems Group at the Massachusetts Institute of Technology. His research interests include programming languages, verification, semantics, and type systems. He conducted his doctoral research at the Max Planck Institute for Software Systems, and received his PhD, Master's, and Bachelor's degrees in Computer Science from Saarland University.
2020 ACM Doctoral Dissertation Award Honorable Mention
Chuchu Fan is the recipient of the 2020 ACM Doctoral Dissertation Award for her dissertation, “ Formal Methods for Safe Autonomy: Data-Driven Verification, Synthesis, and Applications .” Honorable Mentions go to Henry Corrigan-Gibbs of the Massachusetts Institute of Technology and Ralf Jung of the Max Planck Institute for Software Systems and MIT.
Fan’s dissertation makes foundational contributions to verification of embedded and cyber-physical systems, and demonstrates applicability of the developed verification technologies in industrial-scale systems. Her dissertation also advances the theory for sensitivity analysis and symbolic reachability; develops verification algorithms and software tools (DryVR, Realsyn); and demonstrates applications in industrial-scale autonomous systems.
2019 ACM Doctoral Dissertation Award
Dor Minzer of Tel Aviv University is the recipient of the 2019 ACM Doctoral Dissertation Award for his dissertation, “ On Monotonicity Testing and the 2-to-2-Games Conjecture .” Honorable Mentions go to Jakub Tarnawski of École polytechnique fédérale de Lausanne (EPFL) and JiaJun Wu of Massachusetts Institute of Technology.
Dor Minzer's dissertation, “ On Monotonicity Testing and the 2-to-2-Games Conjecture ,” settles the complexity of testing monotonicity of Boolean functions and makes a significant advance toward resolving the Unique Games Conjecture, one of the most central problems in approximation algorithms and complexity theory.
Property-testers are extremely efficient randomized algorithms that check whether an object satisfies a certain property, when the data is too large to examine. For example, one may want to check that the distance between any two computers in the internet network does not exceed a given bound. In the first part of his thesis, Minzer settled a famous open problem in the field by introducing an optimal tester that checks whether a given Boolean function (voting scheme) is monotonic.
The holy grail of complexity theory is to classify computational problems to those that are feasible and those that are infeasible. The PCP theorem (for probabilistically checkable proofs) establishes the framework that enables classifying approximation problems as infeasible, showing they are NP-hard. In 2002, Subhash Khot proposed the Unique Games Conjecture (UGC), asserting that a very strong version of the PCP theorem should still hold. The conjecture has inspired a flurry of research and has had far-reaching implications. If proven true, the conjecture would explain the complexity of a whole family of algorithmic problems. In contrast to other conjectures, UGC has been controversial, splitting the community into believers and skeptics. While progress toward validating the conjecture has stalled, evidence against it had been piling up, involving new algorithmic techniques.
In the second part of his dissertation, Minzer went halfway toward establishing the conjecture, and in the process nullified the strongest known evidence against UGC. Even if UGC is not resolved in the immediate future, Minzer’s dissertation makes significant advances toward solving research problems that have previously appeared out of reach.
Minzer is a postdoctoral researcher at the Institute for Advanced Study (IAS) in Princeton, New Jersey, and will be joining MIT as an Assistant Professor in the fall of 2020. His main research interests are in computational complexity theory, PCP, and analysis of Boolean functions. Minzer received a BA in Mathematics, as well as an MSc and PhD in Computer Science from Tel Aviv University.
Dor Minzer of Tel Aviv University is the recipient of the 2019 ACM Doctoral Dissertation Award for his dissertation, “ On Monotonicity Testing and the 2-to-2-Games Conjecture .” The key contributions of Minzer’s dissertation are settling the complexity of testing monotonicity of Boolean functions and making a significant advance toward resolving the Unique Games Conjecture, one of the most central problems in approximation algorithms and complexity theory.
Honorable Mentions for the 2019 ACM Doctoral Dissertation Award go to Jakub Tarnawski , École polytechnique fédérale de Lausanne (EPFL) and JiaJun Wu , Massachusetts Institute of Technology (MIT).
Jakub Tarnawski’s dissertation “ New Graph Algorithms via Polyhedral Techniques ” made groundbreaking algorithmic progress on two of the most central problems in combinatorial optimization: the matching problem and the traveling salesman problem. Work on deterministic parallel algorithms for the matching problem is motivated by one of the unsolved mysteries in computer science: does randomness help in speeding up algorithms? Tarnawski’s dissertation makes significant progress on this question by almost completely derandomizing a three-decade-old randomized parallel matching algorithm by Ketan Mulmuley, Umesh Vaziriani, and Vijay Vazirani.
The second major result of Tarnawski’s dissertation relates to the traveling salesman problem: find the shortest tour of n given cities. Already in 1956, George Dantzig et al. used a linear program to solve a special instance of the problem. Since then the strength of their linear program has become one of the main open problems in combinatorial optimization. Tarnawski’s dissertation resolves this question asymptotically and gives the first constant-factor approximation algorithm for the asymmetric traveling salesman problem.
Tarnawski is a researcher at Microsoft Research. He is broadly interested in theoretical computer science and combinatorial optimization, particularly in graph algorithms and approximation algorithms. He received his PhD from EPFL and an MSc in Mathematics and Computer Science from the University of Wrocław, Poland.
JiaJun Wu’s dissertation, “ Learning to See the Physical World ,” has advanced AI for perceiving the physical world by integrating bottom-up recognition in neural networks with top-down simulation engines, graphical models, and probabilistic programs. Despite phenomenal progress in the past decade, current artificial intelligence methods tackle only specific problems, require large amounts of training data, and easily break when generalizing to new tasks or environments. Human intelligence reveals how far we need to go: from a single image, humans can explain what we see, reconstruct the scene in 3D, predict what’s going to happen, and plan our actions accordingly.
Wu addresses the problem of physical scene understanding—how to build efficient and versatile machines that learn to see, reason about, and interact with the physical world. The key insight is to exploit the causal structure of the world, using simulation engines for computer graphics, physics, and language, and to integrate them with deep learning. His dissertation spans perception, physics and reasoning, with the goal of seeing and reasoning about the physical world as humans do. The work bridges the various disciplines of artificial intelligence, addressing key problems in perception, dynamics modeling, and cognitive reasoning.
Wu is an Assistant Professor of Computer Science at Stanford University. His research interests include physical scene understanding, dynamics models, and multi-modal perception. He received his PhD and SM degree in Electrical Engineering and Computer Science from MIT, and Bachelor’s degrees in Computer Science and Economics from Tsinghua University in Beijing, China.
2019 ACM Doctoral Dissertation Award Honorable Mention
2018 acm doctoral dissertation award.
Chelsea Finn of the University of California, Berkeley is the recipient of the 2018 ACM Doctoral Dissertation Award for her dissertation, “ Learning to Learn with Gradients .” In her thesis, Finn introduced algorithms for meta-learning that enable deep networks to solve new tasks from small datasets, and demonstrated how her algorithms can be applied in areas including computer vision, reinforcement learning and robotics.
Deep learning has transformed the artificial intelligence field and has led to significant advances in areas including speech recognition, computer vision and robotics. However, deep learning methods require large datasets, which aren’t readily available in areas such as medical imaging and robotics.
Meta-learning is a recent innovation that holds promise to allow machines to learn with smaller datasets. Meta-learning algorithms “learn to learn” by using past data to learn how to adapt quickly to new tasks. However, much of the initial work in meta-learning focused on designing increasingly complex neural network architectures. In her dissertation, Finn introduced a class of methods called model-agnostic meta-learning (MAML) methods, which don’t require computer scientists to manually design complex architectures. Finn’s MAML methods have had tremendous impact on the field and have been widely adopted in reinforcement learning, computer vision and other fields of machine learning.
At a young age, Finn has become one of the most recognized experts in the field of robotic learning. She has developed some of the most effective methods to teach robots skills to control and manipulate objects. In one instance highlighted in her dissertation, she used her MAML methods to teach a robot reaching and placing skills, using raw camera pixels from just a single human demonstration.
Finn is a Research Scientist at Google Brain and a postdoctoral researcher at the Berkeley AI Research Lab (BAIR). In the fall of 2019, she will start a full-time appointment as an Assistant Professor at Stanford University. Finn received her PhD in Electrical Engineering and Computer Science from the University of California, Berkeley and a BS in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology.
Honorable Mentions for the 2018 ACM Doctoral Dissertation Award go to Ryan Beckett and Tengyu Ma , who both received PhD degrees in Computer Science from Princeton University.
Ryan Beckett developed new, general and efficient algorithms for creating and validating network control plane configurations in his dissertation, “ Network Control Plane Synthesis and Verification .” Computer networks connect key components of the world’s critical infrastructure. When such networks are misconfigured, several systems people rely on are interrupted—airplanes are grounded, banks go offline, etc. Beckett’s dissertation describes new principles, algorithms and tools for substantially improving the reliability of modern networks. In the first half of his thesis, Beckett shows that it is unnecessary to simulate the distributed algorithms that traditional routers implement—a process that is simply too costly—and that instead, one can directly verify the stable states to which such algorithms will eventually converge. In the second half of his thesis, he shows how to generate correct configurations from surprisingly compact high-level specifications.
Beckett is a researcher in the mobility and networking group at Microsoft Research. He received his PhD and MA in Computer Science from Princeton University, and both a BS in Computer Science and a BA in Mathematics from the University of Virginia.
Tengyu Ma’s dissertation, " Non-convex Optimization for Machine Learning: Design, Analysis, and Understanding ,” develops novel theory to support new trends in machine learning. He introduces significant advances in proving convergence of nonconvex optimization algorithms in machine learning, and outlines properties of machine learning models trained via such methods. In the first part of his thesis, Ma studies a range of problems, such as matrix completion, sparse coding, simplified neural networks, and learning linear dynamical systems, and formalizes clear and natural conditions under which one can design provable correct and efficient optimization algorithms. In the second part of his thesis, Ma shows how to understand and interpret the properties of embedding models for natural languages, which were learned using nonconvex optimization.
Ma is an Assistant Professor of Computer Science and Statistics at Stanford University. He received a PhD in Computer Science from Princeton University and a BS in Computer Science from Tsinghua University.
2018 ACM Doctoral Dissertation Award Honorable Mention
Chelsea Finn of the University of California, Berkeley is the recipient of the 2018 ACM Doctoral Dissertation Award for her dissertation, “ Learning to Learn with Gradients .” Honorable Mentions go to Ryan Beckett and Tengyu Ma , who both received PhD degrees in Computer Science from Princeton University.
Beckett developed new, general and efficient algorithms for creating and validating network control plane configurations in his dissertation, “ Network Control Plane Synthesis and Verification .” Computer networks connect key components of the world’s critical infrastructure. When such networks are misconfigured, several systems people rely on are interrupted—airplanes are grounded, banks go offline, etc. Beckett’s dissertation describes new principles, algorithms and tools for substantially improving the reliability of modern networks. In the first half of his thesis, Beckett shows that it is unnecessary to simulate the distributed algorithms that traditional routers implement—a process that is simply too costly—and that instead, one can directly verify the stable states to which such algorithms will eventually converge. In the second half of his thesis, he shows how to generate correct configurations from surprisingly compact high-level specifications.
Ma’s dissertation, " Non-convex Optimization for Machine Learning: Design, Analysis, and Understanding ,” develops novel theory to support new trends in machine learning. He introduces significant advances in proving convergence of nonconvex optimization algorithms in machine learning, and outlines properties of machine learning models trained via such methods. In the first part of his thesis, Ma studies a range of problems, such as matrix completion, sparse coding, simplified neural networks, and learning linear dynamical systems, and formalizes clear and natural conditions under which one can design provable correct and efficient optimization algorithms. In the second part of his thesis, Ma shows how to understand and interpret the properties of embedding models for natural languages, which were learned using nonconvex optimization.
2017 ACM Doctoral Dissertation Award
Aviad Rubinstein is the recipient of the Association for Computing Machinery (ACM) 2017 Doctoral Dissertation Award for his dissertation “ Hardness of Approximation Between P and NP .” In his thesis, Rubinstein established the intractability of the approximate Nash equilibrium problem and several other important problems between P and NP-completeness—an enduring problem in theoretical computer science.
For several decades, researchers in areas including economics and game theory have developed mathematical equilibria models to predict how people in a game or economic environment might act given certain conditions.
When applying computational approaches to equilibria models, important questions arise, including how long it would take a computer to calculate an equilibrium. In theoretical computer science, a problem that can be solved in theory (given finite resources, such as time) but for which, in practice, any solution takes too many resources (that is, too much time) to be useful is known as an intractable problem. In 2008, Daskalakis, Goldberg and Papadimitriou demonstrated the intractability of the Nash equilibrium, an often-examined scenario in game theory and economics where no player in the game would take a different action as long as every other player in the game remains the same. But a very large question remained in theoretical computer science as to whether an approximate Nash equilibrium (a variation of the Nash equilibrium that allows the possibility that a player may have a small incentive to do something different) is also intractable.
Rubinstein’s dissertation introduced brilliant new ideas and novel mathematical techniques to demonstrate that the approximate Nash equilibrium is also intractable. Beyond solving this important question, Rubinstein’s thesis also insightfully addressed other problems around P and NP completeness, the most important question in theoretical computer science. Rubinstein is a postdoctoral researcher at Harvard University and will be starting an appointment as an Assistant Professor at Stanford University in the fall of 2018. He received a PhD in Computer Science from the University of California, Berkeley, an MSc in Computer Science from Tel Aviv University (Israel) and a BSc in Mathematics and Computer Science from Technion (Israel).
Honorable Mentions for the 2017 ACM Doctoral Dissertation Award went to Mohsen Ghaffari , who received his PhD from the Massachusetts Institute of Technology’s Department of Electrical Engineering and Computer Science (MIT EECS) and Stefanie Mueller , who received her PhD from the Hasso Plattner Institute (Germany).
In Ghaffari’s dissertation, “ Improved Distributed Algorithms for Fundamental Graph Problems ,” he presents novel distributed algorithms that significantly lower the costs of solving fundamental graph problems in networks, including structuring problems, connectivity problems, and scheduling problems. Ghaffari’s dissertation includes both breakthrough algorithmic contributions and interesting methodology. The first part of the dissertation presents a new maximal independent set (MIS) algorithm, which is a breakthrough because it achieves a better time bound than previous algorithms for this three-decades-old problem. The second part of the dissertation contains a collection of related results about vertex connectivity decompositions. Finally, in the third part of his dissertation, Ghaffari introduces a time-efficient algorithm for concurrent scheduling of multiple distributed algorithms. Ghaffari is an Assistant Professor of Computer Science at ETH Zurich. He received a PhD and SM in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology and received a double major in Computer Science and Electrical Engineering from Sharif University (Iran).
Mueller’s dissertation, “ Interacting with Personal Fabrication Devices ,” demonstrates how to make personal fabrication machines interactive. Her approach involves two steps: speeding of batch processing and turn taking, and real-time interaction. Her software systems faBrickator, WirePrint and Platener allow users to fabricate 10 times faster, a process she calls low-fidelity fabrication or low-fab. In her dissertation she also outlines how to add interactivity. Constructable, a tool she developed, allows workers to fabricate by sketching directly on the workpiece, causing a laser cutter to implement these sketches when the user stops drawing. Another of Mueller’s tools, LaserOrigami, extends this work to 3D. Mueller is an Assistant Professor of Computer Science at MIT EECS and MIT CSAIL. She received a PhD in Computer Science as well as an MSc in IT-Systems Engineering from the Hasso Plattner Institute (Germany). Earlier, she received a BSc in Computer Science and Media from the University of Applied Science Harz (Germany).
Honorable Mentions for the 2017 ACM Doctoral Dissertation Award went to Mohsen Ghaffari , who received his PhD from the Massachusetts Institute of Technology’s Department of Electrical Engineering and Computer Science (MIT EECS) and Stefanie Mueller , who received her PhD from the Hasso Plattner Institute (Germany).
2017 ACM Doctoral Dissertation Award Honorable Mention
Aviad Rubinstein is the recipient of the Association for Computing Machinery (ACM) 2017 Doctoral Dissertation Award for his dissertation “ Hardness of Approximation Between P and NP .” Honorable Mentions for the award went to Mohsen Ghaffari , who received his PhD from the Massachusetts Institute of Technology’s Department of Electrical Engineering and Computer Science (MIT EECS) and Stefanie Mueller , who received her PhD from the Hasso Plattner Institute (Germany).
2017 ACM Doctoral Dissertation Award Award Honorable Mention
Aviad Rubinstein is the recipient of the Association for Computing Machinery (ACM) 2017 Doctoral Dissertation Award for his dissertation “ Hardness of Approximation Between P and NP .” Honorable Mentions for the award went to Mohsen Ghaffari , who received his PhD from the Massachusetts Institute of Technology’s Department of Electrical Engineering and Computer Science (MIT EECS) and Stefanie Mueller , who received her PhD from the Hasso Plattner Institute (Germany).
2016 ACM Doctoral Dissertation Award
Haitham Hassanieh is the recipient of the ACM 2016 Doctoral Dissertation Award . Hassanieh developed highly efficient algorithms for computing the Sparse Fourier Transform, and demonstrated their applicability in many domains including networks, graphics, medical imaging and biochemistry. In his dissertation, The Sparse Fourier Transform: Theory and Practice , he presented a new way to decrease the amount of computation needed to process data, thus increasing the efficiency of programs in several areas of computing.
In computer science, the Fourier transform is a fundamental tool for processing streams of data. It identifies frequency patterns in the data, a task that has a broad array of applications. For many years, the Fast Fourier Transform (FFT) was considered the most efficient algorithm in this area. With the growth of Big Data, however, the FFT cannot keep up with the massive increase in datasets. In his doctoral dissertation Hassanieh presents the theoretical foundation of the Sparse Fourier Transform (SFT), an algorithm that is more efficient than FFT for data with a limited number of frequencies. He then shows how this new algorithm can be used to build practical systems to solve key problems in six different applications including wireless networks, mobile systems, computer graphics, medical imaging, biochemistry and digital circuits. Hassanieh’s Sparse Fourier Transform can process data at a rate that is 10 to 100 times faster than was possible before, thus greatly increasing the power of networks and devices.
Hassanieh is an Assistant Professor in the Department of Electrical and Computer Engineering and the Department of Computer Science at the University of Illinois at Urbana-Champaign. He received his MS and PhD in Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT). A native of Lebanon, he earned a BE in Computer and Communications Engineering from the American University of Beirut. Hassanieh’s Sparse Fourier Transform algorithm was chosen by MIT Technology Review as one of the top 10 breakthrough technologies of 2012. He has also been recognized with the Sprowls Award for Best Dissertation in Computer Science, and the SIGCOMM Best Paper Award.
Honorable Mention for the 2016 ACM Doctoral Dissertation Award went to Peter Bailis of Stanford University and Veselin Raychev of ETH Zurich.
In Bailis’s dissertation, Coordination Avoidance in Distributed Databases , he addresses a perennial problem in a network of multiple computers working together to achieve a common goal: Is it possible to build systems that scale efficiently (process ever-increasing amounts of data) while ensuring that application data remains provably correct and consistent? These concerns are especially timely as Internet services such as Google and Facebook have led to a vast increase in the global distribution of data. In addressing this problem, Bailis introduces a new framework, invariant confluence, that mitigates the fundamental tradeoffs between coordination and consistency. His dissertation breaks new conceptual ground in the areas of transaction processing and distributed consistency—two areas thought to be fully understood. Bailis is an Assistant Professor of Computer Science at Stanford University. He received a PhD in Computer Science from the University of California, Berkeley and his AB in Computer Science from Harvard College.
Raychev’s dissertation, Learning from Large Codebases , introduces new methods for creating programming tools based on probabilistic models of code that can solve tasks beyond the reach of current methods. As the size of publicly available codebases has grown dramatically in recent years, so has interest in developing programming tools that solve software tasks by learning from these codebases. Raychev’s dissertation takes a novel approach to addressing this challenge that combines advanced techniques in programming languages with machine learning practices. In the thesis, Raychev lays out four separate methods that detail how machine learning approaches can be applied to program analysis in order to produce useful programming tools. These include: code completion with statistical language models; predicting program properties from big code; learning program from noisy data; and learning statistical code completion systems. Raychev’s work is regarded as having the potential to open up several promising new avenues of research in the years to come. Raychev is currently a co-founder and Chief Technology Officer of DeepCode, a company developing artificial intelligence-based programming tools. He received a PhD in Computer Science from ETH Zurich. A native of Bulgaria, he received MS and BS degrees from Sofia University.
2016 ACM Doctoral Dissertation Honorable Mention Award
Haitham Hassanieh is the recipient of the ACM 2016 Doctoral Dissertation Award . Honorable Mention for the 2016 ACM Doctoral Dissertation Award went to Peter Bailis of Stanford University and Veselin Raychev of ETH Zurich.
Haitham Hassanieh is the recipient of the ACM 2016 Doctoral Dissertation Award . Hassanieh developed highly efficient algorithms for computing the Sparse Fourier Transform, and demonstrated their applicability in many domains including networks, graphics, medical imaging and biochemistry. In his dissertation, The Sparse Fourier Transform: Theory and Practice , he presented a new way to decrease the amount of computation needed to process data, thus increasing the efficiency of programs in several areas of computing.
Hassanieh is an Assistant Professor in the Department of Electrical and Computer Engineering and the Department of Computer Science at the University of Illinois at Urbana-Champaign. He received his MS and PhD in Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT). A native of Lebanon, he earned a BE in Computer and Communications Engineering from the American University of Beirut. Hassanieh’s Sparse Fourier Transform algorithm was chosen by MIT Technology Review as one of the top 10 breakthrough technologies of 2012. He has also been recognized with the Sprowls Award for Best Dissertation in Computer Science, and the SIGCOMM Best Paper Award.
Honorable Mention for the 2016 ACM Doctoral Dissertation Award went to Peter Bailis of Stanford University and Veselin Raychev of ETH Zurich.
In Bailis’s dissertation, Coordination Avoidance in Distributed Databases , he addresses a perennial problem in a network of multiple computers working together to achieve a common goal: Is it possible to build systems that scale efficiently (process ever-increasing amounts of data) while ensuring that application data remains provably correct and consistent? These concerns are especially timely as Internet services such as Google and Facebook have led to a vast increase in the global distribution of data. In addressing this problem, Bailis introduces a new framework, invariant confluence, that mitigates the fundamental tradeoffs between coordination and consistency. His dissertation breaks new conceptual ground in the areas of transaction processing and distributed consistency—two areas thought to be fully understood. Bailis is an Assistant Professor of Computer Science at Stanford University. He received a PhD in Computer Science from the University of California, Berkeley and his AB in Computer Science from Harvard College.
Carnegie Mellon Graduate Earns ACM Doctoral Dissertation Award
Julian Shun has won the 2015 ACM Doctoral Dissertation Award presented by ACM for providing evidence that, with appropriate programming techniques, frameworks and algorithms, shared-memory programs can be simple, fast and scalable. In his dissertation Shared-Memory Parallelism Can Be Simple, Fast, and Scalable , he proposes new techniques for writing scalable parallel programs that run efficiently both in theory and in practice.
While parallelism is essential to achieving high performance in computing, writing efficient and scalable programs can be very difficult. Shun’s three-pronged approach to writing parallel programs that he outlines in his thesis includes:
- proposing tools and techniques for deterministic parallel programming;
- the introduction of Ligra, the first high-level shared-memory framework for parallel graph traversal algorithms; and
- presenting new algorithms for a variety of important problems on graphs and strings that are both efficient in theory and practice.
Shun is a post-doctoral researcher at the University of California, Berkeley, where he was awarded a Miller Research Fellowship. He earned his Ph.D. at Carnegie Mellon University, which nominated him for the ACM Doctoral Dissertation Award. He earned a B.A. in Computer Science from the University of California, Berkeley, where he was ranked first in the 2008 graduating class of computer science students. During the 2013-2014 academic year, he was the recipient of a Facebook Graduate Fellowship.
He will receive the Doctoral Dissertation Award and its $20,000 prize at the annual ACM Awards Banquet on June 11 in San Francisco. Financial sponsorship of the award is provided by Google Inc.
Honorable Mention
Honorable mention for the 2015 ACM Doctoral Dissertation Award went to Aaron Sidford of the Massachusetts Institute of Technology, and Siavash Mirarab of the University of Texas at Austin. They will share a $10,000 prize, with financial sponsorship provided by Google Inc.
In Sidford’s dissertation, Iterative Methods, Combinatorial Optimization, and Linear Programming Beyond the Universal Barrier , he considers the fundamental problems in continuous and combinatorial optimization that occur pervasively in practice, and shows how to improve upon the best-known theoretical running times for solving these problems across a broad range of parameters. Sidford uses and improves techniques from diverse disciplines including spectral graph theory, numerical analysis, data structures, and convex optimization to provide the first theoretical improvements in decades for multiple classic problems ranging from linear programming to linear system solving to maximum flow. Sidford is presently a postdoctoral researcher at Microsoft New England. He received a Ph.D. in Computer Science from the Massachusetts Institute of Technology, which nominated him for this award.
Mirarab’s dissertation, Novel Scalable Approaches for Multiple Sequence Alignment and Phylogenomic Reconstruction , addresses the growing need to analyze large-scale biological sequence data efficiently and accurately. To address this challenge, Mirarab introduces several methods: PASTA, a scalable and accurate algorithm that can align data sets up to one million sequences; statistical binning, a novel technique for reducing noise in estimation of evolutionary trees for individual parts of the genome; and ASTRAL, a new summary method that can run on 1,000 species in one day and has outstanding accuracy. These methods were essential in analyzing very large genomic datasets of birds and plants. Mirarab is currently an Assistant Professor of Electrical and Computer Engineering at the University of California, San Diego. He obtained a Ph.D. in Computer Science from the University of Texas at Austin, which nominated him for this award.
Creator Of Advanced Data Processing Architecture Wins 2014 Doctoral Dissertation Award
Matei Zaharia won the 2014 Doctoral Dissertation Award for his innovative solution to tackling the surge in data processing workloads, and accommodating the speed and sophistication of complex multi-stage applications and more interactive ad-hoc queries. His work proposed a new architecture for cluster computing systems, achieving best-in-class performance in a variety of workloads while providing a simple programming model that lets users easily and efficiently combine them.
To address the limited processing capabilities of single machines in an age of growing data volumes and stalling process speeds, Zaharia developed Resilient Distributed Datasets (RDDs). As described in his dissertation “An Architecture for Fast and General Data Processing on Large Clusters,” RDDs are a distributed memory abstraction that lets programmers perform computations on large clusters in a faulttolerant manner. He implements RDDs in the open source Apache Spark system, which matches or exceeds the performance of specialized systems in many application domains, achieving up to speeds 100 times faster for certain applications. It also offers stronger fault tolerance guarantees and allows these workloads to be combined.
Zaharia, an assistant professor at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), completed his dissertation at the University of California, Berkeley, which nominated him. A graduate of the University of Waterloo, where he won a gold medal at the ACM International Collegiate Programming Contest (ICPC) in 2005, he earned a Bachelor of Mathematics (B. Math) degree. He is a co-founder and Chief Technology Officer of Databricks, the company that is commercializing Apache Spark.
He will receive the Doctoral Dissertation Award and its $20,000 prize at the annual ACM Awards Banquet on June 20 in San Francisco, CA. Financial sponsorship of the award is provided by Google Inc.
Honorable Mention for the 2014 ACM Doctoral Dissertation Award went to John Criswell of the University of Rochester, and John C. Duchi of Stanford University. They will share a $10,000 prize, with financial sponsorship provided by Google Inc.
Criswell’s dissertation, “Secure Virtual Architecture: Security for Commodity Software Systems,” describes a compiler-based infrastructure designed to address the challenges of securing systems that use commodity operating systems like UNIX or Linux. This Secure Virtual Architecture (SVA) can protect both operating system and application code through compiler instrumentation techniques. He completed a Ph.D. degree in Computer Science from the University of Illinois at Urbana-Champaign, which nominated him for this award.
Duchi’s dissertation, “Multiple Optimality Guarantees in Statistical Learning,” explores tradeoffs that occur in modern statistical and machine learning applications. The criteria for these tradeoffs – computation, communication, privacy – must be optimized to maintain statistical performance. He explores examples from optimization, and shows some of the practical benefits that a focus on multiple optimality criteria can bring about. A graduate of the University of California, Berkeley with an M.A. degree in Statistics and a Ph.D. degree in Computer Science, he was also an undergraduate and masters student at Stanford University. He was nominated by UC Berkeley for this award.
ACM will present these and other awards at the ACM Awards Banquet on June 20, 2015 in San Francisco, CA.
Press Release
Doctoral Dissertation Award Recognizes Young Researchers
Cornell University graduate Manish Raghavan receives the 2021 ACM Doctoral Dissertation Award for significant contributions to the understanding of algorithmic decision making and its societal implications, including foundational results on issues of algorithmic bias and fairness. Honorable Mentions go to Dimitris Tsipras of Stanford University, Pratul Srinivasan of Google Research, and Benjamin Mildenhall of Google Research.
View the full list of ACM Awards
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