ACM SIGecom: Doctoral Dissertation Award

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

Ben Brooks, Ozan Candogan, Shuchi Chawla, Yiling Chen, Rachel Cummings, Nima Haghpanah, Nicole Immorlica, Vahideh Manshadi, Renato Paes Leme, Noam Nisan, Sigal Oren, Ariel Procaccia, Aaron Roth, Inbal Talgam-Cohen, Éva Tardos, Alex Teytelboym, Michael Wellman

Doctoral Dissertation Award Winners

2023
- Gabriele Farina, Carnegie Mellon University
  - 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."
- Honorable Mention Martino Banchio, Stanford University
  - 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."
- Honorable Mention Alireza Fallah, MIT
  - 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."
2022
- Modibo Camara, Northwestern University
  - 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."
- Honorable Mention Paul Gölz, Carnegie Mellon University
  - 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."
- Honorable Mention Kangning Wang, Duke University
  - 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."
2021
- Ellen Vitercik, Carnegie Mellon University
  - 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."
- Honorable Mention Manish Raghavan, Cornell University
  - 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."
- Honorable Mention Mahsa Derakhshan, University of Maryland
  - 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."
2020
- Manolis Zampetakis, MIT
  - 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."
- Honorable Mention Nikhil Garg, Stanford University
  - 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."
- Honorable Mention Yuan Deng, Duke University
  - 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."
2019
- Hongyao Ma, Harvard University
  - 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."
- Honorable Mention Rediet Abebe, Cornell University
  - Designing Algorithms for Social Good
  - advised by Jon Kleinberg
  "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."
- Honorable Mention Eric Balkanski, Harvard University
  - 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."
2018
- Yannai A. Gonczarowski, The Hebrew University of Jerusalem
  - Aspects of Complexity and Simplicity in Economic Mechanisms
  - advised by Sergiu Hart and Noam Nisan
- Honorable Mention Nika Haghtalab, Carnegie Mellon University
  - Foundation of Machine Learning, by the People, for the People
  - advised by Avrim Blum and Ariel D. Procaccia
- Honorable Mention Haifeng Xu, University of Southern California
  - Information as a Double-Edged Sword in Strategic Interactions
  - advised by Shaddin Dughmi and Milind Tambe
2017
- Aviad Rubinstein, UC Berkeley
  - Hardness of Approximation Between P and NP
  - advised by Christos Papadimitriou
- Honorable Mention Rachel Cummings, California Institute of Technology
  - The Implications of Privacy-Aware Choice
  - advised by Katrina Ligett
- Honorable Mention Christos Tzamos, MIT
  - Mechanism Design: From Optimal Transport Theory to Revenue Optimization
  - advised by Constantinos Daskalakis
2016
- Peng Shi, MIT
  - Prediction and Optimization in School Choice
  - advised by Itai Ashlagi
- Honorable Mention Bo Waggoner, Harvard University
  - Acquiring and Aggregating Information from Strategic Sources
  - advised by Yiling Chen
- Honorable Mention James Wright, University of British Columbia
  - Modeling Human Behavior in Strategic Settings
  - advised by Kevin Leyton-Brown
2015
- Inbal Talgam-Cohen, Stanford University
  - Robust Market Design: Information and Computation
  - advised by Tim Roughgarden
2014
- S. Matthew Weinberg, MIT
  - Algorithms for Strategic Agents
  - advised by Constantinos Daskalakis
- Honorable Mention Xi (Alice) Gao, Harvard University
  - Eliciting and Aggregating Truthful and Noisy Information
  - advised by Yiling Chen
2013
- Balasubramanian Sivan, University of Wisconsin
  - Prior Robust Optimization
  - advised by Shuchi Chawla
- Honorable Mention Yang Cai, MIT
  - Mechanism Design: A New Algorithmic Framework
  - advised by Constantinos Daskalakis
- Honorable Mention Sigal Oren, Cornell University
  - An Algorithmic Approach to Analyzing Social Phenomena
  - advised by Jon Kleinberg