doctoral dissertation award

The Dennis M. Ritchie Award

Nomination Deadline: August 15th ,2024

The Dennis M. Ritchie Doctoral Dissertation Award was created in 2013 by ACM SIGOPS to recognize research in software systems and to encourage the creativity that Dennis Ritchie embodied, providing a reminder of Ritchie’s legacy and what a difference one person can make in the field of software systems research. This is an annual award presented at SOSP.

Call for Nominations

Eligibility.

Each year, a department is allowed to nominate one Ph.D. thesis produced in that department. The nomination must be made by the chair of the department and must be accompanied by three supporting letters from researchers in the field, clearly specifying the contributions of the thesis and the potential impact on software systems. (If the department chair is the thesis advisor of the nominee, then another senior faculty member in the department must make the nomination.) The thesis must have been defended successfully at most two years before the conference at which the award will be presented. An English-language version of the thesis must accompany the nomination. The Award Committee will determine if the thesis is within scope for the award. The thesis may be nominated simultaneously for other awards, such as the ACM Doctoral Dissertation Award.

The SIGOPS Dennis M. Ritchie Doctoral Dissertation Award is accompanied by a prize of $2,000.

Submission Procedure

Each nomination packet should be emailed by the department chair to the award committee at [email protected] . In the subject title of the email, please write “SIGOPS Dennis M. Ritchie Doctoral Dissertation submission for (nominee name).” Submissions should be sent bundled in one zipped file.

A nomination packet MUST include:

• Nomination letter by the department chair or senior faculty member. This letter must include a one-page summary of the significance of the dissertation.
• Three or more endorsement letters from researchers in the field, at least two of which must come from institutions other than the nominating one.
• A copy of the thesis.
• Information on when and where the thesis was defended.

Steering committee

• Robbert van Renesse
• Nickolai Zeldovich

2023: Rishabh Iyer (winner) EPFL Advisors: George Candea and Katerina Argyraki Latency Interfaces for Systems Code

Chang Lou (Honorable Mention) Johns Hopkins University Advisors: Ryan (Peng) Huang Enhancing Cloud System Runtime to Address Complex Failures

2022: Yongle Zhang (winner) University of Toronto Advisor: Ding Yuan Automating Failure Diagnosis for Distributed Systems

Tej Chajed (honorable mention) MIT Advisors: Frans Kaashoek and Nickolai Zeldovich. Verifying a concurrent, crash-safe file system with sequential reasoning

Akshitha Sriraman (honorable mention) University of Michigan Advisor: Thomas Wenisch Enabling Hyperscale Web Services

2021: Marios Kogias (winner) EPFL Advisor: Edouard Bugnion Operating System and Network Co-design for Latency-Critical Datacenter Applications

Oana Balmau (honorable mention) University of Sydney Advisor: Willy Zwaenepoel Redesigning Persistent Key-Value Stores for Future Workloads, Hardware, and Performance Requirements

Xingda Wei (honorable mention) Shanghai Jiao Tong University Advised by Binyu Zang, Rong Chen, and Haibo Chen Fast Distributed Transaction Processing using RDMA and NVM

2020: Natacha Crooks (winner) Advisors: Lorenzo Alvisi and Simon Peter, University of Texas, Austin. A Client-Centric Approach to Transactional Datastores

Anuj Kalia (Honorable Mention) Advisor: David G. Andersen, Carnegie Mellon University . Efficient Remote Procedure Calls for Datacenters

2019: Sebastian Angel (winner), Advisor: Michael Walfish, University of Texas, Austin Unobservable communications over untrusted infrastructure

2018: Irene Zhang (Winner), Advisors: Hank Levy and Arvind Krishnamurthy, University of Washington Towards a Flexible, High-Performance Operating System for Mobile/Cloud Applications

2016: Vijay Chidambaram (Winner), Advisor: Andrea Arpaci-Dusseau and Remzi Arpaci-Dusseau, University of Wisconsin–Madison “Orderless and Eventually Durable File Systems”

Charles M. Curtsinger (Honorable Mention), Advisor: Emery D. Berger, University of Massachusetts at Amherst “Performance Analysis and Debugging”

2015: Cristiano Giuffrida (Winner), Advisor: Andrew S. Tanenbaum , Vrije University, Netherlands “Safe and Automatic Live Update”

Nadav Amit (Honorable Mention), Advisors: Assaf Schuster and Dan Tsafrir , Technion–Israel Institute of Technology “Alleviating Virtualization Bottlenecks”

2014: Austin T. Clements (Winner), Advisor: M. Frans Kaashoek , Nickolai Zeldovich , MIT “The Scalable Commutativity Rule: Designing Scalable Software for Multicore Processors”

John Criswell (Honorable Mention), Advisor: Vikram Adve , University of Illinois, Urbana-Champaign “Secure Virtual Architecture: Security for Commodity Software Systems”

2013: Mona Attariyan (Winner), Advisor: Jason Flinn , University of Michigan “Improving Software Configuration Troubleshooting with Causality Analysis”

Roxana Geambasu (Honorable Mention), Advisor: Steven D. Gribble , Tadayoshi Kohno , Henry M. Levy , University of Washington “Empowering Users with Control over Cloud and Mobile Data”

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Distinguished Dissertation Award

These awards recognize outstanding and exceptional scholarship and research at the doctoral level. Nominations are due by 5 p.m. PST, May 2, 2024. Each recipient will receive an honorarium of $1,000 and will be publicly recognized by the Graduate School.  

Call for Nominations  

The Graduate School is pleased to announce the 2024 Distinguished Dissertation Awards competition in the following four categories:  

• Biological Sciences  
• Humanities and Fine Arts 
• Mathematics, Physical Sciences and Engineering* 
• Social Sciences* 

* CGS/ProQuest Distinguished Dissertation Award 2024 fields of competition  

These awards recognize outstanding and exceptional scholarship and research at the doctoral level. Nominations are due by 5 p.m. PST, May 2, 2024 . Each department can submit only one nomination per category.  

Eligibility  

The effective date of degree award must fall within the period of July 1, 2023, and June 30, 2024, inclusive, for the nominee selected.  To be eligible, the nominee’s dissertation must be available in the ProQuest database.    

Formatting      

For all documents, fonts are to be no smaller than 11-point and margins no narrower than one inch.  We will strictly observe page-length limits and formatting guidelines.    

Nomination packet materials  

Step 1   

Please provide information about the nominee, the nominating department, etc. via this Microsoft Form .  

Step 2   

Combine the following materials into a single Adobe PDF file, observing the strict page length cap of each component:     

• A non-technical summary (NTS) addressing the purpose, methods, results of the research and its significance within the discipline of this dissertation. An NTS is a concise document that provides a description of the process and its findings in a manner that is both appealing to read and easily understood by the general public. The NTS must not exceed two pages, typed and double-spaced. Please include the nominee’s name and doctoral program on this page.  
• An abstract of the nominee’s dissertation (not to exceed five double-spaced pages).  Appendices containing other material–such as charts, tables, and/or references–may be included as additional pages.  All pages should be numbered, and each should bear the name of the nominee.  
• Three letters of recommendation evaluating the significance and quality of the nominee’s dissertation work. One of these letters is to be from the nominee’s dissertation supervisor, another from a member of the nominee’s dissertation committee, and the third from a person of the nominee’s choice.  
• The nominee’s curriculum vitae (not to exceed five pages) 

Step 3   

The department chair, graduate program advisor, or the graduate program coordinator should submit the complete nomination file c/o [email protected] by the deadline of 5 p.m. PST, May 2, 2024 . This is a firm deadline; in fairness to all nominators, no exceptions or extensions will be granted.   

Please save your file in this format:  

LASTNAMENOMINEE – Nominating Department – Dissertation – [Category] – 2024.pdf    

Ex:  SONG – Astrobiology – Dissertation – Mathematics, Physical Sciences and Engineering – 2024.pdf     

Criteria  

A Graduate School committee will select the award recipients. The nominated dissertations should represent original work that makes an unusually significant contribution to the disciplines.  

Both methodological and substantive quality will be judged. Nominations will be evaluated on the following:  

• Innovation: The degree of innovation, creativity and insight shown by the author.  
• Scope: The scope and importance of the work to the department and to the field.  
• Writing: The effectiveness of the writing (including whether it is written in language that is reasonably understandable to faculty in related disciplines).  

Awards  

Each recipient will receive an honorarium of $1,000 and will be publicly recognized by the Graduate School.  

Council of Graduate Schools Distinguished Dissertation Award national competition   

2024 UW Graduate Schools distinguished dissertation award materials will be forwarded to the Council of Graduate Schools/ProQuest Distinguished Dissertation Award competition when the awardees’ dissertations fall within the current year’s fields of competition as the UW’s institutional nominations for this prestigious recognition.   

The 2024 fields of competition are Mathematics, Physical Sciences and Engineering and Social Sciences. Click here for more details about the CGS/ProQuest Distinguished Dissertation Award and for clarification regarding which fields/disciplines are included under the above categories. 

Address nomination materials c/o:  

Joy Williamson-Lott Dean of The Graduate School [email protected]

Jerry Pangilinan Assistant to the Dean [email protected]

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SIGCOMM Doctoral Dissertation Award

SIGCOMM Doctoral Dissertation Award for Outstanding PhD Thesis in Computer Networking and Data Communication

Recent Awards   2024: Udit Paul (Towards Bridging the Divide: Enhancing Understanding of Digital Inequity) for fundamental contributions to internet measurement and broadband policy, including tools and methodologies to understand digital inequity, and datasets on internet availability and affordability.   Runner-ups:

The committee: Jon Crowcroft (University of Cambridge, chair), Georgios Smaragdakis (Delft University of Technology), Michael Schapira (Hebrew University of Jerusalem).   2023: Kevin Bock (Automating the Discovery of Censorship Evasion Strategies). Runner-up: Siva Kakaria (Formal Methods for a Robust Domain Name System)   2020: Yuliang Li (Hardware-Software Codesign for High-Performance Cloud Networks) for innovations that enable improved performance and robustness of cloud networks through hardware and software codesign, including closing the loop of telemetry and control functions, and identifying the best division of labor between programmable switches, NICs, hosts, and the controller. Honorable Mention: Ming Liu (Building Distributed Systems Using Programmable Networks) for identifying and enabling novel uses of programmable network devices in data centers, including an in-network computing solution for accelerating distributed applications, and a microservice execution platform running on SmartNIC-accelerated servers. The committee: Jorjeta Jetcheva (San Jose State University, chair), Kate Lin (National Chiao Tung University), David Maltz (Microsoft), and Peter Steenkiste (Carnegie Mellon University).   2019: Deepak Vasisht  (Towards Realizing the Internet-of-Things Vision: In-body, Homes, and Farms) for visionary research on internet-of-things services with concrete impact solving human-critical problems in different application areas including in-body, homes, and agriculture. Mina Tahmasbi Arashloo  (Stateful Programming of High-Speed Network Hardware) for pioneering the idea of raising the level of programming abstraction, and for proposing a new architecture for programming network transport protocols in FPGAs and a new language and compiler system for developing stateful applications for software-defined networks.   The committee: Christophe Diot (Google, chair), Jim Kurose (UMass Amherst), Lili Qiu (University of Texas at Austin), Catherine Rosenberg (University of Waterloo), and Geoff Voelker (UC San Diego).   2018: Ryan Beckett  (Network Control Plane Synthesis And Verification) for pioneering contributions in extending the field of network control plane verification and synthesis.  Honorable mention: Arpit Gupta (Flexible and Scalable Systems for Network Management) for impactful work on Software Defined Internet Exchange Point design and implementation.   The committee: John W. Byers (Boston University), Dejan Kostic (KTH), Nikolaos Laoutaris (IMDEA, chair), Anees Shaikh (Google), and Steve Uhlig (Queen Mary University of London).   2017: Anirudh Sivaraman Kaushalram  (Designing Fast and Programmable Routers)

• Sivaraman's dissertation makes pioneering and impactful contributions to the design and implementation of programmable routers that run at hardware line rates.

The committee: Marco Mellia, T.S. Eugene Ng (chair), Xiaowei Yang, and Haifeng Yu   2016:  The two co-winners are  Justine Sherry  (Middleboxes as a Cloud Service) and  Vamsi Talla  (Power, Communication and Sensing Solutions for Energy Constrained Platforms)  

• Sherry’s dissertation proposes that advanced network functions be implemented as software services running in the cloud, and develops in depth the algorithms and system designs needed to realize this vision in practice.
• Talla's dissertation introduces techniques that make it possible to build low-power sensors and devices that consume no energy beyond what is already in the air, in ambient RF signals such as cellular, TV, and Wi-Fi.

The committee: Fabián Bustamante, Rodrigo Fonseca, Dave Levin (chair), and Ellen Zegura   2015:   Mosharaf Chowdhury  (Coflow: A Networking Abstraction for Distributed Data-Parallel Applications)

• Chowdhury’s dissertation provides novel and application-aware networking abstractions which significantly improve the performance of networked applications running in the cloud..

The committee: Ratul Mahajan, Dina Papagiannaki, Laurent Vanbever (chair), and Minlan Yu  

2014:   Keith Winstein  (Transport Architectures for an Evolving Internet)

• Winstein's dissertation proposes and develops the idea of computer-generated congestion control, creatively solving both classical and new problems in network resource management.
• Hongqiang Liu  - (Traffic Planning under Network Dynamics) Liu's dissertation presents the design and implementation of practical, uniform, automatic, and proactive techniques to prevent the link congestion commonly caused by network faults and maintenance.

The committee: Mark Crovella, Arvind Krishnamurthy, Nikolaos Laoutaris, Alan Mislove, Neil Spring,  Arun Venkataramani

2013: Aaron Schulman (Observing and Improving the Reliability of Internet Last-Mile Links)

• The dissertation provides the first observations of fundamental factors that limit the reliability of the Internet’s critical last-mile infrastructure and presents improvements to mitigate the effects of these factors.

2012: Shyamnath Gollakota

• The dissertation provides a fundamentally new and practical way to deal with interference in the wireless medium, often rendering it harmless or even turning it into an advantage. While traditional wireless systems have attempted to avoid interference, this thesis is the first to practically demonstrate techniques to decode packets under significant interference and leveraging it for improved security. The thesis is transformative and can significantly impact the design of future wireless communication systems.
• Runners Up: Ashok Anand The dissertation presents a range of innovative techniques to eliminate redundancies in traffic across diverse hosts, routers, and networks across the Internet. The work itself is rigorous and comprehensive in its treatment of redundancy elimination techniques. As the demand for Internet bandwidth continues to be higher than ever before, such approaches can prove to be highly beneficial. Laurent Vanbever The dissertation provides fundamental innovations that allow a network to be re-configured without causing inconsistencies in network routing structures. The thesis encompasses good theoretical concepts that ensure global correctness properties and practical manifestations of these concepts to make them deployable. As networks around us continue to get more complex, the techniques developed in this thesis provide significant tools to improve efficient management of these networks.

2011: Minlan Yu, Scalable Management of Enterprise and Data-Center Networks

• Minlan Yu’s PhD thesis concerns the design, implementation and evaluation of a scalable management architecture for enterprise and data center networks.  Minlan’s research goes full circle, from important practical problems, to creative data structures and algorithms, to rigorous analysis and modeling, and finally to the design and implementation of novel systems.  Minlan’s thesis is exceptional both in breadth and depth, with proven results on one of the world’s largest datacenters. Runners Up: Michel Piatek, Scalable Data Sharing Without Centralized Trust Michael Piatek’s PhD thesis concerns the design and construction of effective content distribution systems. Amongst other contributions, he uses a mix of clever analysis and experimentation to devise a new peer-to-peer system “BitTyrant” that greatly outperformed alternatives, and which has been downloaded more than a million times. Michael’s dissertation succeeds by delivering insightful theory, demonstrated in practice. Aruna Balasubramanian, Architecting Protocols to Enable Mobile Applications in Diverse Wireless Networks Aruna Balasubramanian's PhD thesis concerns enhancing the experience of mobile users in the face of challenging network conditions, for instance by building disruption-tolerant networks.  Her work is based on careful analysis, and has been deployed and tested out in the field.  Her work has been cited hundreds of times, and has had a large impact on research in the area, raising the bar for experimental verification of wireless network systems.

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SIGSAC Doctoral Dissertation Awards

"for outstanding phd thesis in computer and information security".

The SIGSAC Awards Committee is pleased to announce the awardees of the SIGSAC 2021 awards. The award recipients are:

The ACM Special Interest Group for Security Audit and Control (SIGSAC) is soliciting nominations for the 2024 SIGSAC Doctoral Dissertation Award. This annual award by SIGSAC recognizes excellent research and dissertation by doctoral candidates in computer and information security and privacy. The SIGSAC Doctoral Dissertation Award winner and up to two runners-up will be recognized at the ACM CCS 2024 conference. The award winner will receive a plaque, a $1,500 honorarium and a complimentary registration to CCS 2024. The runners-up each will receive a plaque and complimentary registration to CCS 2024.

Nomination Deadline:

Eligibility:, nomination procedure:.

A nomination statement summarizing the candidate's PhD thesis contribution and potential impact, and justification of the nomination (two pages maximum);

Three endorsement letters supporting the nomination including the significant PhD thesis contributions of the candidate. Each endorsement should be no longer than 500 words with clear specification of nominee PhD thesis contributions and potential impact on the computer and information security field. Note that the three endorsement letters must be submitted in addition to the nomination statement .

Also, the committee will adhere to the ACM Awards Committee Conflict of Interest (COI) guidelines . Particularly, the SIGSAC dissertation award committee members and chair are not allowed to nominate and endorse candidates.

Decision Notification:

The award honorarium, the award plaques and the conference registration fee for the winner to attend the ACM CCS Conference will be fully funded by SIGSAC and included in its annual budget.

Further Information and Inquiry:

Submission instructions and a list of past winners are available at https://www.sigsac.org/award/diss-awards.html . For information not found in this message or for any other inquiries, contact the SIGSAC Doctoral Dissertation Award Committee Chair, Zhiqiang Lin.

A potential conflict of interest occurs when a person is involved in making a decision that:

could result in that person, a close associate of that person, or that person's company or institution receiving significant financial gain, such as a contract or grant, or

could result in that person, or a close associate of that person, receiving significant professional recognition, such as an award or the selection of a paper, work, exhibit, or other type of submitted presentation.

Some examples of instances of associations that could cause a conflict of interest are:

Prior Award Recipients:

SIGMOBILE Doctoral Dissertation Award

We invite you to nominate PhD dissertations for the ACM SIGMOBILE Doctoral Dissertation Award. This award recognizes excellent thesis research by doctoral candidates in the field of mobile, sensor and ubiquitous systems and networking. The winner and up to two runners-up will be recognized at an ACM SIGMOBILE conferences of the winner's choice. The award winner will receive a plaque, a $1,000 honorarium and a complimentary registration to one of the following year ACM SIGMOBILE Conferences. The runners-up will each receive a plaque.

Current Submission Deadline : April 8, 2024 (AOE).

Current Awardees

Sujay Narayana TU Delft Space Internet of Things (Space-IoT)

For contributions in efficient and miniaturized Space-IoT systems.

Justin Chan (Runner Up) University of Washington Intelligent Earable Systems for Equitable Healthcare

For advancing applicability of earable computing to scalable healthcare.

Past Awardees

Jingxian Wang Carnegie Mellon University Blind Wireless Beamforming to Power, Heat, and Move

For advancing the science and interdisciplinary applications of wireless energy Transfer.

Andrea Ferlini (Runner Up) University of Cambridge Exploring the Potential of Earables for Personal-Scale Sensing

For advancing the state of art of sensing in ear-worn devices.

Rajalakshmi Nandakumar University of Washington Computational Wireless Sensing at Scale

For creating an easily-deployed technique for low-cost millimeter-accuracy sensing on commodity hardware, and its bold and high-impact applications to important societal problems.

Tingjun Chen (Runner Up) Columbia University Algorithms and Experimentation for Future Wireless Networks: From Internet-of-Things to Full-Duplex

For contributions to ultra-low-power wireless networks based on energy harvesting, and to the theory and practice of full-duplex wireless networks.

Wenguang Mao UT Austin Acoustic Sensing on Smart Devices

Yasaman Ghasempour (Runner Up) Rice University Next-Generation Wireless Systems for Joint Communication and Sensing in Millimeter-Wave and Terahertz Spectrum

Elahe Soltanaghaei (Runner Up) University of Virginia Sensing the Physical World Using Pervasive Wireless Infrastructure

Fadel Adib MIT Wireless Systems that Extend Our Senses

Fadel's dissertation proposes ways in which Wi-Fi signals, traditionally used for communication, can be extended for use as sensing tools that enable us to learn about our environment without physically reaching out to the various objects in it.

Vamsi Talla University of Washington Power, Communication, Sensing Solutions for Energy Constrainted Platforms

Vamsi's dissertation proposes wireless power delivery and ultra-low power backscatter based communication and sensing solutions for the next billion devices.

Pengyu Zhang UMass Leveraging Backscatter for Ultra-Low Power Wireless Sensing Systems

Pengyu's thesis presents fundamental principles underlying the physical layer, hardware architecture, and operating system design of backscatter based sensors to enable widespread deployment of such systems.

Swarun Kumar (Runner Up) MIT Pushing the Limits of Wireless Networks

Swarun's thesis significantly improves the performance of wireless networks in presence of interference and mobility, and enhances them with accurate location.

Eligibility

• Nominations are limited to one doctoral dissertation per department, and may be from any PhD-granting university in the world.
• The thesis should have been successfully defended and the final version of the dissertation must have been accepted at the nominee's host institution between 1st April 2023 and 31st March 2024.
• The submission must be on a topic related to mobile, sensor and ubiquitous systems and networking. The determination of whether a submission is in scope for the award will be made by the SIGMOBILE Doctoral Dissertation Award Committee.
• An English-language version of the dissertation must be submitted with the nomination.
• A dissertation can be nominated for both the SIGMOBILE Doctoral Dissertation Award and the ACM Doctoral Dissertation Award.
• Self-nominations are not accepted.

Submission Deadline

April 8, 2024 (AoE)

Submission Email:

cecilia.mascolo (AT) cl.cam.ac.uk

Submission Procedure

All nomination materials must be in English, in PDF format, and submitted by email to the Committee Chair by the submission deadline. Late submissions will not be considered. Nominations must include:

• A statement from the thesis advisor summarizing the contributions and potential impact of the work (no more than two pages).
• A link to the dissertation itself (please refrain from sending large attachments by email) which can be downloaded without difficulty.
• An endorsement letter by the department chair, confirming that the thesis was successfully defended and the final document accepted within the qualifying time period (dates mentioned above).

Dissertation Award Committee

• Fadel Adib, MIT, USA
• Cecilia Mascolo (Chair), University of Cambridge, UK
• Junehwa Song, KAIST, Korea
• Lin Zhong, Yale University, USA
• Xia Zhou, Columbia University, USA

Past Committee Members

• Aruna Balasubramanian (Stony Brook University)
• Cecilia Mascolo (University of Cambridge)
• Mahadev Satyanarayanan (chair) (Carnegie Mellon University)
• Lin Zhong (Yale University)
• Xia Zhou (Columbia University)

Doctoral Dissertation Award Funding

The funding comes exclusively from ACM SIGMOBILE.

Doctoral Dissertation Award Selection Committee

The Doctoral Dissertation Award Selection Committee will consist of several SIGMOBILE members, one of whom will be appointed as the Selection Committee chair. Award committee members will be appointed by the current SIGMOBILE awards chair. The committee chair will adjudicate conflicts of interest, appointing substitutes to the committee as necessary. Committee members may remain on the committee for up to four years.

Resolution of Conflict of Interest

A member of the Doctoral Dissertation Award Committee who has a potential conflict of interest should report it to the committee chair, and the committee chair will determine whether a conflict exists. If the conflict exists, the committee chair and the SIGMOBILE award chair will replace the member of the award selection committee with another volunteer.

A potential conflict of interest occurs when a person is involved in making a decision that: could result in that person, a close associate of that person, or that person's company or institution receiving significant financial gain, such as a contract or grant, or could result in that person, or a close associate of that person, receiving significant professional recognition, such as an award or the selection of a paper, work, exhibit, or other type of submitted presentation. Some examples of instances of associations that could cause a conflict of interest are: employment at the same institution or company candidate for employment at the same institution or company received an honorarium or stipend from the institution or company within the last year co-author on book or paper in the last 48 months co-principal investigator on grant or research project actively working on project together family relationship close personal relationship graduate advisee/advisor relationship deep personal animosity.

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Doctoral Dissertation Award

The SIGecom Doctoral Dissertation Award recognizes an outstanding dissertation in the field of economics and computation. More details and nomination procedure…

• Past and Present Members of the Doctoral Dissertation Award Committee

Doctoral Dissertation Award Winners

• Game-Theoretic Decision Making in Imperfect-Information Games: Learning Dynamics, Equilibrium Computation, and Complexity
• advised by Tuomas Sandholm

"Gabriele's thesis extensively contributes to our fundamental understanding of equilibrium computation and learning in imperfect-information games, resolving recognized open problems, establishing new positive complexity results, and leading in several cases to state-of-the-art performance in theory and/or practice. Among other breakthroughs, it resolves the existence of efficient learning dynamics leading to extensive-form correlated equilibria, provides state-of-the-art regret rates for learning in multiplayer imperfect-information settings, and yields positive complexity results together with the first polynomial-time algorithm for exact sequentially-rational equilibria in large-scale games."

• Artificial Intelligence and Dynamic Markets
• advised by Andrzej Skrzypacz

"Martino's dissertation examines the interplay between Artificial Intelligence (AI) algorithms and the economic landscape of dynamic digital markets. He investigates how AI alters competitive behavior, influences optimal auction design, and affects pricing strategies, aiming to provide guidelines for understanding and fostering effective AI adoption in the marketplace."

• Algorithmic Interactions with Strategic Users: Incentives, Interplay, and Impact
• advised by Asuman Ozdaglar

"Alireza's thesis develops and studies frameworks for understanding the dynamics between a platform seeking to learn a parameter through users' data and strategic users who seek privacy or compensation for their information."

• Complexity in Economic Theory
• advised by Eddie Dekel and Jason Hartline

"Modibo's thesis beautifully combines Economics and Computer Science. Modibo adds an axiom of computational tractability to decision theory and finds that decisions are rationalizable if and only if preferences are suitably separable and, in general, complex decision problems can be better approximated by decisions that are inconsistent with the paradigm of expected utility."

• Social Choice for Social Good: Proposals for Democratic Innovation from Computer Science
• advised by Ariel Procaccia

"Paul's thesis boosts democratic innovation and resource allocation, building on ideas and tools from computer science, economics, political science and operations research. He has led the development of algorithms and platforms that are regularly used to resettle refugees and to fairly select major citizens' assemblies around the world."

• Approximations for Economic Efficiency and Fairness
• advised by Kamesh Munagala

"Kangning's thesis cleverly applies the approximation lens to make significant contributions to a broad set of topics: computational social choice, algorithmic fairness, and algorithmic pricing. In particular, it settles a long lasting problem in bilateral trade, showing a constant ratio between first-best and second-best in bilateral trade."

• Automated Algorithm and Mechanism Configuration
• advised by Maria-Florina Balcan and Tuomas Sandholm

"Ellen's thesis makes extensive contributions toward establishing the rigorous foundations of automated algorithm configuration, where data-driven machine learning and optimization are used to fine tune an algorithm's parameters for application-specific performance guarantees. Her ground-breaking thesis provides among the first provable generalization guarantees for automated algorithm configuration. An important application domain of automated algorithmic configuration analyzed in depth in Ellen's thesis is automated mechanism design where the goal is to design a mechanism from data to optimize for the mechanism's performance, such as revenue. Ellen's thesis includes additional applications of automated algorithmic configuration such as integer programming (linear and quadratic), and computational biology."

• The Societal Impacts of Algorithmic Decision-Making
• advised by Jon Kleinberg

"Manish's thesis consists of a set of highly visible and influential papers on topics concerning the societal impacts of algorithmic decision-making. Most notably, his work establishes the fundamental and unavoidable trade-offs between a number of intuitive notions of fairness in prediction-based decision-making. Manish's results, together with a closely related independent discovery, have substantially shaped the research directions in the field of algorithmic fairness. In addition to theory, Manish used qualitative research methods to perform deep, application-focused analyses of the consequences of algorithmic decision making in algorithmic hiring and consumer finance."

• Algorithms for Markets: Matching and Pricing
• advised by Mohammad Hajiaghayi

"Mahsa's thesis designs new models and algorithms for auctions and matching markets. Her work analyzes and improves markets using a mix of tools from operations research, economics, and computer science. Her improvements are in terms of both computational performance and economic value. Mahsa's thesis solves significant open problems, including achieving the first (1-ε) approximation to the stochastic matching problem, a problem with direct application to kidney exchange."

• Statistics in High Dimensions without IID Samples: Truncated Statistics and Minimax Optimization
• advised by Constantinos Daskalakis

"Manolis's thesis includes stellar theoretical contributions to learning from data that are subject to strategic manipulations (e.g., showing that finding a local approximate min-max equilibria is PPAD-complete) and from data that are truncated or censored. Manolis's work brilliantly exploits connections to high-dimensional probability, harmonic analysis and optimization to provide innovative tools for handling truncated or censored data in high-dimensional settings. This outstanding thesis is likely to have a major impact on game theory, econometrics, machine learning, and statistics."

• Designing Marketplaces and Civic Engagement Platforms
• advised by Ramesh Johari and Ashish Goel

"Nikhil's thesis offers wide-ranging methodological contributions to platform design with applications to surge pricing, rating systems, and preference elicitation for participatory budgeting. Nikhil's work combines elegant theoretical modelling with impressive experimental and empirical analysis. His deep engagement with real-world platforms makes his work a wonderful example of applied research on the intersection of operations research, economics, and computer science."

• Dynamic Mechanism Design in Complex Environments
• advised by Vincent Conitzer

"Yuan's thesis pushes the frontiers of dynamic mechanism design in a way that is notable for its combination of technical strength and practical motivation. In his thesis, Yuan develops a new framework for designing robust dynamic mechanisms in complex environments which makes major advances in bridging the theory and practice. His thesis also contains an ingenious statistical test which leads to a metric quantifying the extent to which a mechanism is dynamically incentive compatible. This is an important step towards making dynamic mechanisms transparent."

• Mechanism Design for Coordinating Behavior
• advised by David C. Parkes

"An impressive combination of theory and practice for complex mechanism design settings — this thesis identifies key problems, develops sophisticated mathematical models to tackle them and ultimately influences the practice in the industry via direct collaborations. Hongyao's work on Spatio-temporal pricing provides key operational insight that has been adopted by ridesharing platforms and is considered one of the best papers in ridesharing by experts in the field. Her work on optimizing trade-offs between participants and societal value also addresses a key problem faced by many companies allocating scarce resources and the solutions identified are both principle and practical."

• Designing Algorithms for Social Good

"This thesis introduces important new themes to the algorithmic economics community: how can algorithms play a role in increasing access to opportunity for historically underserved populations. Rediet's work on measuring and designing interventions generated practical impact through collaborations with NGOs and the public health community. The work in the thesis also offers the foundations of the emerging area of Mechanism Design for Social Good."

• The Adaptive Complexity of Submodular Optimization
• advised by Yaron Singer

"This thesis makes substantial progress in an old and well-studied area of submodular optimization where only very technically hard problems remain. The thesis contains a breakthrough result that provides exponential speedup in parallel runtime of submodular maximization. The new results in Eric's thesis open exciting new areas of inquiry in the intersection of optimization and learning theory."

• Aspects of Complexity and Simplicity in Economic Mechanisms
• advised by Sergiu Hart and Noam Nisan
• Foundation of Machine Learning, by the People, for the People
• advised by Avrim Blum and Ariel D. Procaccia
• Information as a Double-Edged Sword in Strategic Interactions
• advised by Shaddin Dughmi and Milind Tambe
• Hardness of Approximation Between P and NP
• advised by Christos Papadimitriou
• The Implications of Privacy-Aware Choice
• advised by Katrina Ligett
• Mechanism Design: From Optimal Transport Theory to Revenue Optimization
• Prediction and Optimization in School Choice
• advised by Itai Ashlagi
• Acquiring and Aggregating Information from Strategic Sources
• advised by Yiling Chen
• Modeling Human Behavior in Strategic Settings
• advised by Kevin Leyton-Brown
• Robust Market Design: Information and Computation
• advised by Tim Roughgarden
• Algorithms for Strategic Agents
• Eliciting and Aggregating Truthful and Noisy Information
• Prior Robust Optimization
• advised by Shuchi Chawla
• Mechanism Design: A New Algorithmic Framework
• An Algorithmic Approach to Analyzing Social Phenomena

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Doctoral Dissertation Award

The SIGecom Doctoral Dissertation Award recognizes an outstanding dissertation in the field of economics and computation. The award is conferred annually at the ACM Conference on Economics and Computation and includes a certificate, complimentary conference registration, and an honorarium of $1,500. A certificate may further be given to up to two runners-up. No award may be conferred if the nominations are judged not to meet the standards for the award.

To be eligible, a dissertation must be on a topic related to economics and computation and must have been defended successfully during the calendar year preceding the year of the award presentation.

The next SIGecom Doctoral Dissertation Award will be given for dissertations defended in 2023. Nominations are due by February 29th, 2024 (Anywhere on Earth), and must be submitted by email to [email protected] with the name of the nominee in the subject. A dissertation may be nominated simultaneously for both the SIGecom Doctoral Dissertation Award and the ACM Doctoral Dissertation Award.

Nominations may be made by any member of SIGecom and will typically come from the dissertation supervisor. Self-nomination is not allowed. Nominations for the award must include the following , preferably having items 1, 2, 4, and 5 in a single PDF file:

• Suggested citation if the dissertation is selected. This should consist of at most 2 sentences describing the key contributions of the thesis.
• A two-page summary of the dissertation, written by the nominee, including bibliographic data and links to publicly accessible versions of published papers based primarily on the dissertation.
• An English-language version of the dissertation in a separate file.
• An endorsement letter of no more than two pages by the nominator, arguing the merit of the dissertation, potential impact, and justification of the nomination. This document should also certify the dissertation defense date.
• The names, email addresses, and affiliations of two additional endorsers.

The additional endorsement letters themselves should be sent directly by email ( [email protected] ), by the same deadline. These endorsements should be no longer than 500 words and should specify the relationship of the endorser to the nominee, contributions of the dissertation, and its potential impact on the field.

It is expected that a nominated candidate, if selected for the award, will attend the next ACM Conference on Economics and Computation to accept the award and give a presentation on the dissertation work. The award includes complimentary registration but does not cover travel or accommodation expenses to attend the conference.

• The 2023 Doctoral Dissertation Award Committee
• Ben Brooks , University of Chicago
• Rachel Cummings , Columbia University
• Nima Haghpanah (chair) , Penn State University

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CS Faculty Earned Outstanding Dissertation Award

Assistant Professor Yu Meng , who joined the UVA Department of Computer Science in January of 2024, received the ACM SIGKDD 2024 Dissertation Award for his Ph.D. thesis, "Efficient and Effective Learning of Text Representations". 

This award recognizes outstanding research by doctoral candidates in data science, data mining, and knowledge discovery and is considered the highest honor in the data mining field for Ph.D. research. 

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Doctoral Dissertation Award

Purpose of the award.

This award recognizes the most outstanding doctoral dissertation/thesis in information science the preceding twelve months.

Eligibility

The nominee must meet the following qualifications:

• For this award, “information science” is considered to include all the communication activities and information science and technology fields noted in the first paragraph of Article II, “Purpose,” of the ASIS&T Constitution and Bylaws, published on the ASIS&T website.
• Authors must have completed their doctorates in the time period between April 1, 2023 and March 31, 2024.
• Author must be a current member in good standing of ASIS&T.
• Prior award winners, current members of the Awards & Honors Committee and current members of the Board of Directors are not eligible.

Submissions will be judged on the following criteria:

• Importance of the topic to theory development and/or practical applications in information science
• Soundness of methodology
• Organization and clarity of the presentation
• Quality of data (when applicable)

Nominations Process

Nominations must be submitted by 11:59 pm US Pacific Time on the deadline date via the  designated online portal.

Nominations must include the following only:

• Name, affiliation, and contact information of the dissertation author;
• The final, submitted version of the entire dissertation
• A letter of endorsement from the nominee’s dissertation advisor

A jury of five members (including the Jury Chair) and one alternate shall be appointed by the Awards & Honors Committee at the recommendation of the Jury Chair. Jury members must:

• Be a member in good standing of ASIS&T;
• Not be a member of any other ASIS&T committee;
• Not submit a nomination for the award in the year of their jury service; and
• Declare any conflict of interest related to any nominee for the award and recuse themselves from the jury should the conflict be deemed significant by the Jury Chair (in which case the alternate would be appointed).

Selection Process

• The jury will utilize both asynchronous scoring and synchronous discussion to arrive at a final decision as to who wins any award.
• Through the evaluation platform or system, jury members will rate each nominee on a scale of 10 (highest) to 1 (lowest) on each of the five criteria listed above. Jury members should also submit a brief synopsis summarizing the most exemplary accomplishments of that nominee.
• Only one winner will be selected in a given year. In the event of a tie that cannot be resolved through synchronous discussion, the jury chair will identify an additional jury member to break the tie. This new jury member will evaluate only the tied nominations and will rate them using the same criteria used by the other jurors.
• In the event that the Jury decides that none of the submitted dissertations should be recognized, no award may be given that year.
• The jury chair shall use the qualitative feedback from the jury as well as their own judgment to write a ~ 250-word citation stating the rationale for making the award.

Nature of the Award

The Doctoral Dissertation Award was established in 1974 and is administered by the Awards & Honors Committee and sponsored by the Association. The award consists of a US$500 prize, a certificate, and up to US$500 towards travel or other expenses of the award recipient, contingent upon the recipient’s attending the ASIS&T annual meeting. The winner may be given the opportunity to present a summary of the doctoral research at the ASIS&T Annual Meeting. If runners-up are designated, they will receive letters of “honorable mention,” signed by the ASIS&T President, but shall not share in the award.

Presentation of the Award

The award shall be presented at the ASIS&T Annual Meeting.

Important Dates

• Jury Appointment: March 15
• Submission Deadline: April 1
• Selection Deadline: May 15

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Fredric m. jablin doctoral dissertation award.

This annual award was established in 1999 and later renamed to honor and celebrate the life of Jepson professor Dr. Fredric M. Jablin (1952-2004). It is presented in partnership by the Jepson School of Leadership Studies and the International Leadership Association in recognition of outstanding new scholarship in the field. In keeping with the Jepson School's mission to further scholarship and educate others for and about leadership through curricula, events, and programs, this award is presented to a scholar whose doctoral dissertation research - on any topic and from any discipline - demonstrates substantial insights and implications for the study of leadership. Submissions are due on or before June 15 . Applicants submit a letter of interest, a three- to five-page abstract of a substantive doctoral dissertation chapter (specifically, the chapter that best represents the author's dissertation), a brief biography and verification of the dissertation defense date (e.g., a letter from the dissertation advisor). The award committee will contact finalists by the end of June to request a full dissertation chapter.

Recipients are recognized and present their dissertations at the annual International Leadership Association's global conference, receive a $1,000 prize from the Jepson School of Leadership Studies, a one-year membership to the ILA, complimentary conference registration, and travel expenses.

Dr. Michael A. Goldfien received the  2023 Fredric M. Jablin Doctoral Dissertation Award  award for his doctoral dissertation, “Essays on Leadership, Domestic Politics, and Diplomacy.”

• News & Stories

News in Brief: EHD Alumna, Researcher Wins Outstanding Dissertation Award

Now a researcher at Youth-Nex, Meredith Franco was awarded the 2024 APA Division 16 Outstanding Dissertation Award for her work as an EHD doctoral student.

Leslie Booren

August 30, 2024

Meredith P. Franco, currently a research scientist at Youth-Nex, was recently recognized for her outstanding dissertation while a Ph.D. student at the UVA School of Education and Human Development. A 2023 graduate of the clinical and school psychology program, Franco’s dissertation, “Culturally Responsive Practice in PK-12 Classrooms: Identification and Validation of Discrete Indicators,” was honored by the American Psychological Association (APA) Division 16 this month.

The honor is given to an individual who recently completed a dissertation that merits special recognition and has the potential to contribute to the science and practice of school psychology. An article stemming from Franco’s dissertation work was published in the high-impact journal, Review of Educational Research, and is now freely available .

“We are grateful that the APA and the review committee selected Meredith for this prestigious award,” said Professor Catherine Bradshaw, senior associate dean for research and co-chair of Franco’s dissertation committee. “Her dissertation work is exceptional in many ways. Meredith is skillful in conceptualizing and addressing key challenges in the field by employing rigorous quantitative methodologies with a critical lens.”

In her dissertation, Franco encourages school researchers and practitioners to recognize the positive impact of culturally responsive practice (CRP) on classroom climate and how to understand different measurement approaches for CRP-related interventions. She also encourages them to find ways to create new, innovative measures that can be used across developmental stages and contexts.

Now a Nationally Certified School Psychologist and researcher, Franco’s scholarship continues to explore how teachers’ use of culturally responsive educational practices can promote student equity and, ultimately, inform the design of just classrooms, schools, and systems.

“I am incredibly thankful to the Division 16 Award Committee for this recognition.” Franco said. “The encouragement I received from my EHD mentors—including professors Catherine Bradshaw, Jessika Bottiani, and Jason Downer—to take on ambitious projects and aim for publishing in high-impact journals has laid the groundwork for my path as an early career researcher.”

Franco and other Division 16 award winners were recognized at the annual APA Convention this summer on August 10 in Seattle, WA.  

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Research center or department, featured faculty.

• Meredith Powers Franco
• Catherine P. Bradshaw

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• Teaching & Learning

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Student Contributions ACM India Doctoral Dissertation Award

Best doctoral dissertation in computing from an academic institution in India

• ACM India Doctoral Dissertation Award

About ACM India Doctoral Dissertation Award

The ACM India Doctoral Dissertation Award was established in 2011. This award recognizes the best doctoral dissertation from degree-awarding institutions based in India for each academic year, from July 1 to June 30 in the current cycle. The ACM India Doctoral Dissertation Award is accompanied by a prize of ₹2,00,000. An honorable mention award, if any, is accompanied by a prize of ₹1,00,000. The dissertation(s) will be published in the ACM Digital Library. Tata Consultancy Services Limited (TCS) is the founding sponsor of the ACM India Doctoral Dissertation Award.

Recent India Doctoral Dissertation Award News

Pranjal dutta chosen as the recipient of the acm india 2023 doctoral dissertation award.

The ACM India 2023 Doctoral Dissertation Award goes to Pranjal Dutta for his dissertation titled “A Tale of Hardness, De-randomization and De-bordering in Complexity Theory.” Pranjal’s dissertation makes breakthrough contributions in the study of hardness, approximation and derandomization of algebraic circuits, and develops new mathematical tools, laying the foundation for future developments in algebraic complexity theory. Pranjal’s dissertation work was done at Chennai Mathematical Institute under the supervision of Prof. Nitin Saxena.

Pranjal Dutta’s dissertation makes multiple contributions to algebraic complexity theory. In an early result, he shows that the existence of a polynomial with a slightly largish special representation would resolve the long-standing VP vs VNP problem, and also provide non-trivial algorithms for polynomial identity testing (PIT) – effectively attacking two flagship problems in algebraic complexity theory. In the context of PIT, his dissertation provides a nearly polynomial time algorithm for derandomization of a special class of depth 4 algebraic circuits, where the top fanin is bounded and the bottom gates compute low degree polynomials. This is among the strongest results in PIT known so far. Pranjal also provides a deep understanding of the power of closure of algebraic circuits -- an important problem in the Geometric Complexity Theory approach to the P vs NP problem. Specifically, he shows that the closure (or border) of (bounded fanin) depth 3 circuits can be captured by algebraic branching programs, making it one of the first such "de-bordering" results in this area. Additionally, his work also establishes an efficient algorithm for PIT for this class, and shows a surprising exponential-gap hierarchy-theorem for depth-3 constant-top-fanin circuits.

The Honorable Mention for 2023 goes to Jogendra Nath Kundu for his dissertation titled “Self-supervised Domain Adaptation Framework for Computer Vision Tasks.” Jogendra’s dissertation makes significant and timely contributions to unsupervised domain adaptation and self-supervised learning techniques for structured prediction based vision tasks, advancing the practical deployment of intelligent machines in real-world scenarios. His dissertation work was done at Indian Institute of Science, Bangalore under the supervision of Prof. Venkatesh Babu Radhakrishnan.

Jogendra Nath Kundu’s doctoral dissertation makes three important contributions. First, he considers image-like dense-prediction tasks, where he addresses shortcomings in existing classification-based domain adaptation algorithms. The content-preserving mechanisms introduced by him via cyclic consistency objectives have been shown to yield state-of-the-art performance. Next, he introduces source-side procurement stage learning, a novel approach to source-free adaptation, which significantly enhances adaptability in scenarios with restricted data-sharing. Finally, Jogendra’s thesis applies domain adaptation concepts to the complex task of 3D human pose estimation. He shows how this can be achieved by incorporation of effective prior-enforcing mechanisms alongside development of novel self-supervised techniques leveraging inter-entity relations. The insights in Jogendra’s dissertation have the potential to revolutionize the deployment of intelligent vision systems across diverse industries, from healthcare to virtual and augmented reality.

The ACM India Doctoral Dissertation Award was established in 2011. This award recognizes the best doctoral dissertation in Computer Science and related disciplines from a degree-awarding institution based in India for each academic year, running from July 1 of one year to June 30 of the following year. The ACM India Doctoral Dissertation Award is accompanied by a prize of ₹2,00,000. An Honorable Mention award, given to nomination(s), if any, that missed the award by a narrow margin, is accompanied by a prize of ₹1,00,000, that is shared among the recipient(s). The winning dissertation(s) will be published in the ACM Digital Library. T ata Consultancy Services Limited (TCS)    is the founding sponsor of these awards. Please see the ACM India Doctoral Dissertation Award page  for additional information on current and past winners.

Please join us in congratulating Pranjal Dutta and Jogendra Nath Kundu for their significant achievements.

Siddharth Sandipkumar Bhandari Chosen as Recipient of ACM India 2022 Doctoral Dissertation Award

Siddharth Sandipkumar Bhandari is the recipient of the ACM India 2022 Doctoral Dissertation Award for his dissertation titled “Exact Sampling and List Decoding” that develops new techniques and tools in sampling graph colourings and contributes to improved analyses for understanding list-decodability of codes. Siddharth’s doctoral dissertation work was done at Tata Institute of Fundamental Research, Mumbai under the supervision of Prahladh Harsha.

Siddharth’s dissertation makes fundamental contributions to two different areas of theoretical computer science: (a) sampling colourings of graphs and (b) list-decoding error-correcting codes. Sampling a random k-colouring of a given graph is a classic problem in theoretical computer science and statistical physics. The problem of perfect sampling is to construct a polynomial time randomised algorithm that generates a perfect sample from the uniform distribution of k-colourings. Siddharth’s work improves on a two-decade old algorithm for solving this problem using novel techniques that are likely to have wider applicability. In the area of list-decoding, Siddharth’s dissertation focuses on three problems. The first problem is the zero-error capacity of the q/(q 1) channel. Prior work on perfect hashing shows that as the list-size is reduced, the channel capacity decreases from an inverse-polynomial form to an inverse-exponential form, and it has been an open problem to understand where this transition occurs. By showing that the channel follows a coupon-collector like behaviour, Siddharth’s work demonstrates that there is an almost sharp transition near O(q log q). The other two results in this part are related to list-decodability of algebraic codes and lead to a better understanding of the list-decoding of these codes all the way up to capacity.

Pritish Mohapatra shares the Honorable Mention for his dissertation titled “Optimization for and by Machine Learning” that makes significant contributions towards the design of efficient optimization methods for machine learning, including key results for optimizing ranking metrics used in information retrieval systems. Pritish’s doctoral dissertation work was done at International Institute of Information Technology, Hyderabad under the supervision of C. V. Jawahar.

Pritish’s dissertation addresses the problem of making optimization of complicated loss functions efficient and practical. Specifically, he designs a practically efficient optimization algorithm for a category of non-decomposable ranking metrics that greatly improve the feasibility of using such sophisticated metrics for learning in information retrieval tasks. Pritish also provides a concrete theoretical analysis that proves the superiority of the proposed algorithm for optimization, providing a fine balance between the theoretical soundness and practical utility of the algorithms. Pritish’s work also makes an interesting contribution in the use of the classical optimization technique of partial-linearization for efficient learning of large-scale classification models. This holds special significance for learning of models with structured output spaces. Finally, Pritish’s dissertation contributes to the problem of using learning for optimization. He proposes a novel framework that uses a learnable model for doing rounding for combinatorial optimization algorithms. This is based on a key insight that randomized rounding procedures can be visualized as sampling from latent variable models.

Sruthi Sekar shares the Honorable Mention for her dissertation titled “Near-Optimal Non-Malleable Codes and Leakage Resilient Secret Sharing Schemes” that makes fundamental contributions in our understanding of cryptographic primitives such as non-malleable codes, secret sharing schemes and randomness extractors. Sruthi’s doctoral dissertation work was done at Indian Institute of Science, Bangalore under the supervision of Bhavana Kanukurthi.

The goal of non-malleable codes (NMCs) is to enable encoding of messages so that an adversary cannot tamper it into the encoding of a related message. An open problem in this area is to build 1/2-rate NMCs in 2-split state model where a codeword consists of two independently tamperable states. Sruthi’s dissertation makes fundamental contributions to this area. Specifically, she builds on her earlier work to introduce new two-state non-malleable codes for random messages with rate 1/2. This work also introduces a new primitive called "Non-malleable randomness encoders". Her dissertation also shows how to build a near-optimal rate 1/3, 2-state NMC, thereby taking us a step closer to solving the main open problem in this area. In addition, Sruthi’s dissertation introduces a new pseudorandomness primitive called "Adaptive Extractors" and shows their applicability to building constant-rate leakage resilient secret sharing schemes.

Deepika Yadav shares the Honorable Mention for her dissertation titled “Supporting Ongoing Training of Community Health Workers through Mobile-based Solutions in Rural India” that combines the knowledge of Computer System research and HCI to produce systems that are deployed in fields. Her work resulted in deployment of a mobile based training platform for ASHA workers that has been used to train hundreds of ASHA workers. Deepika’s doctoral dissertation work was done at Indraprastha Institute of Information Technology Delhi under the supervision of Pushpendra Singh.

Deepika’s dissertation develops a low-cost mobile-based training platform called “Sangosthi” that allows a geographically distributed group of community health workers (CHWs) to connect over a conference call and receive training in a structured manner. The developed system uses a hybrid architecture to use Interactive Voice Response for facilitating online audio training sessions, enabling CHWs to access training from anywhere through their feature phones. Her work contributes to (i) testing the feasibility and efficacy of a low-cost technology intervention through a controlled field experiment (ii) unpacking the training needs of CHWs in the field and mapping it back to the existing reference material through a large-scale deployment on 500 CHWs, (iii) investigating the potential for peer-to-peer learning models to address the challenge of experts’ availability through a controlled field experiment, and (iv) exploring the potential for automated techniques in this domain by proposing a semi-automated natural language processing approach for curating generated learning content and exposing CHWs and women to Chatbot-based education for the first time. By using a range of mixed methods and field experiments, Deepika’s dissertation expands the focus of HCI4D and mHealth research on CHW competence development in low-resource settings.

The ACM India Doctoral Dissertation Award was established in 2011. This award recognizes the best doctoral dissertation from a degree-awarding institution based in India for each academic year, running from August 1 of one year to July 31 of the following year. The ACM India Doctoral Dissertation Award is accompanied by a prize of ₹2,00,000. An Honorable Mention award is accompanied by a prize of ₹1,00,000 which is shared amongst the recipients. The dissertation(s) will be published in the ACM Digital Library. Financial support for both the award and honorable mention is provided by Tata Consultancy Services (TCS). Please see the ACM India Doctoral Dissertation Award page for additional information on current and past winners.

ACM India 2023 Doctoral Dissertation Award

Pranjal Dutta   is the recipient of ACM India 2023 Doctoral Dissertation Award for his dissertation titled “A Tale of Hardness, De-randomization and De-bordering in Complexity Theory.” The Honorable Mention goes to  Jogendra Nath Kundu  for his dissertation titled “Self-supervised Domain Adaptation Framework for Computer Vision Tasks.”

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ACM India Awards - Nominations Deadline Nearing

Reminder, the deadline for ACM India Doctoral Dissertation Award is just around the corner: 31 August 2024 . Please rush your nominations for this coveted award. Also, remember that the deadline for the other three ACM India awards (Early Career Researcher, Outstanding Contribution to Computing Education and Outstanding Contributions in Computing by a Woman) is 15 September 2024 . You can find more details for all the awards via: https://india.acm.org/awards . The nomination forms for all the awards are directly available via: https://cmt3.research.microsoft.com/A   CMIndiaAwards2024 .