Edith Elkind is a Professor of Computer Science at the University of Oxford and a Non-Tutorial Fellow at Balliol College (leaving October 2024). Her research focuses on algorithmic game theory and computational social choice. She joined Oxford in 2013 after roles at Nanyang Technological University (Singapore), Princeton University (PhD 2005), and postdoctoral positions at the University of Warwick, University of Liverpool, Hebrew University of Jerusalem, and University of Southampton. Her work addresses voting systems, cooperative games, and complexity analysis. Key contributions include studies on multiwinner elections, justified representation, and coalition formation. She has advised six PhD/Master’s students and authored/co-authored over 30 publications. Notable works include Properties of multiwinner voting rules (2017) and Justified representation in approval-based committee voting (2017), advancing fairness and efficiency in voting mechanisms. Her research spans theoretical computer science and AI, with applications in social choice theory and game dynamics. Collaborations include projects on foundational AI and computational social choice. She has contributed to foundational texts like Computational Aspects of Cooperative Game Theory (2011).
Steven Brams is a Professor of Politics at New York University's Department of Politics within the College of Arts & Science. His research focuses on game theory, social choice theory, fair division, voting systems, and international politics. He holds a B.S. from MIT (1962) and a Ph.D. from Northwestern University (1966). Brams' work emphasizes fair allocation mechanisms, including envy-free division algorithms, voting system reforms, and rule design in sports and conflict resolution. He has collaborated extensively with researchers like D. Marc Kilgour and Mehmet S. Ismail on topics such as fair shootouts in soccer, equitable chess openings, and gerrymandering solutions. Education: B.S., Massachusetts Institute of Technology, 1962 Ph.D., Northwestern University, 1966 His research explores how game-theoretic models can address real-world problems, such as improving voting systems, resolving disputes, and enhancing fairness in sports. Notable contributions include the 'Catch-Up' rule for service sports and the 'Excess Method' for multiwinner approval voting. Awards: American Association for the Advancement of Science Fellow (1992) Guggenheim Fellow (1986–1987) Russell Sage Foundation Visiting Scholar (1998–1999) Elinor Ostrom Prize (2013) Brams has advised on electoral reforms and contributed to policy discussions on fair division in international conflicts, such as the Spratly Islands dispute. His interdisciplinary work bridges mathematics, political science, and philosophy, addressing both theoretical and applied challenges in decision-making.
Tom Demeulemeester is a Research Fellow at the Operations Research and Statistics Research Group (ORSTAT) of KU Leuven , with a future appointment as Assistant Professor in Operations Research at Maastricht University starting August 2025. His work bridges operations research, combinatorial optimization, and computational social choice, focusing on fair resource allocation and algorithmic game theory. His research emphasizes fairness in integer programming, hedonic games, and one-sided matching. Recent work examines Rawlsian assignments and strategyproofness in multiwinner elections. Publications span journals like the European Journal of Operational Research and conferences such as MFCS and AAAI, with a focus on both theoretical and applied optimization. Key trends in his publications include randomization for fairness, stability in coalition formation, and axiomatic approaches to matching problems. All works are publicly accessible via repositories, with code available on GitHub. This aligns with his commitment to open science and reproducibility. Tom actively engages with academic communities through conference participation (EURO, MFCS, AAAI) and workshops on school choice. He will soon transition to Maastricht University after completing his PhD defense at KU Leuven in May 2024, titled Fairness through randomization: An operations research perspective .
Luis Sanchez Fernandez is a Full Professor at the Department of Telematics Engineering, Carlos III University of Madrid. His research focuses span Smart Cities, Semantic Web, and Distributed Systems. Contact information includes email luis.sanchez@uc3m.es and office location 4.1.F08 in Leganés. His research program integrates Blockchain Governance , Urban Mobility Analysis , and Complex Systems Modeling . Recent work examines approval-based voting mechanisms in decentralized networks and fractional transport equations for physical simulations. Publications demonstrate a strong emphasis on fair algorithm design for societal applications. Key article themes show convergence of Smart City Data Integration Multiwinner Election Algorithms Cellular Automaton Dynamics Semantic Annotation Frameworks As Deputy Director of Teaching Affairs, he leads curriculum innovation in Telematics Engineering. His educational background includes a Doctorate from Universidad de Salamanca, focusing on Wikipedia as a teaching resource in higher education.
Edith Elkind is the Ginni Rometty Professor of Computer Science at Northwestern University, located in Evanston, Illinois. Her research focuses on strategic foundations of multiagent systems, with a particular emphasis on algorithms for preference aggregation and fair division. She has contributed significantly to topics such as multiwinner voting, participatory budgeting, and temporal voting mechanisms. Research Interests: Fair Division, Voting Theory, Algorithmic Game Theory, Multiagent Systems Her recent work includes advancements in temporal fairness, budget-constrained chore allocation, and representative body selection through computational methods. Elkind's publications often bridge theoretical computer science with practical applications in public decision-making and social choice mechanisms.
Davide Grossi is an Associate Professor at the University of Groningen's Faculty of Science and Engineering, affiliated with the Bernoulli Institute for Mathematics, Computer Science, and Artificial Intelligence. He also holds an associate professorship at the University of Amsterdam's Institute for Logic, Language, and Computation. His research focuses on multi-agent systems, decision-making, game theory, social choice theory, and argumentation, with applications to digital democracy and hybrid intelligence. Grossi leads the Multi-Agent Decisions Lab (MAD-lab) and co-leads the Democratic Innovations Lab (DIL) at the University of Groningen. He has published extensively in top venues such as Artificial Intelligence , Journal of Artificial Intelligence Research , and conferences like AAMAS and IJCAI. His work bridges theoretical computer science, social choice, and legal informatics, addressing challenges in collective decision-making and democratic innovation. Research Interests: Grossi's expertise spans formal models of multi-agent systems, social choice theory, argumentation frameworks, and their applications to real-world democratic processes. He explores topics like liquid democracy, deliberative coalition formation, and the ethical implications of AI in governance. His recent work emphasizes hybrid intelligence systems that augment human decision-making with AI, ensuring transparency and fairness in automated systems. Publications: His recent articles analyze cooperation in public goods games, case-based reasoning for legal datasets, and Condorcet markets for epistemic social choice. These contributions highlight trends in AI-driven decision systems, algorithmic fairness, and computational social choice. Grossi's work on digital democracy proposes frameworks for participatory governance, while his technical papers advance methods in multi-agent learning and argumentation evaluation. Grants & Advising: Grossi leads research projects funded by the Netherlands Institute for Advanced Study (NIAS) and collaborates with institutions like the Hybrid Intelligence Center. While no specific student advisees are listed, his labs mentor graduate students in AI and social choice theory. He actively participates in interdisciplinary collaborations, integrating computer science with legal and political science perspectives. Labs & Teams: Beyond MAD-lab and DIL, he contributes to the Groningen Cognitive Systems and Materials Center (CogniGron) and the Deliberation and Argumentation Special Interest Group of Hybrid Intelligence. These groups focus on cognitive robotics, AI ethics, and deliberative mechanisms for societal challenges.
Martin Lackner is an Associate Professor in the Databases and Artificial Intelligence department at the Faculty of Informatics, Vienna University of Technology. His research focuses on computational social choice, voting theory, and algorithmic decision making, with particular expertise in multi-winner elections, approval-based voting systems, and participatory budgeting. He maintains an active research program with continuous publications in top AI and theoretical computer science venues. Lackner's research interests center on the theoretical foundations and practical implementations of fair decision-making systems. His work bridges theoretical computer science with practical applications in democratic processes, examining how computational methods can enhance fairness, representation, and efficiency in collective decision making. He has made significant contributions to understanding the computational complexity of voting rules, developing new algorithms for preference aggregation, and establishing axiomatic properties for multiwinner election methods. His recent publications demonstrate a consistent focus on fairness in long-term decision processes, with particular attention to perpetual voting systems, participatory budgeting mechanisms, and approval-based multiwinner rules. The research shows strong theoretical grounding combined with practical implementation considerations, as evidenced by his development of the abcvoting Python package for implementing approval-based voting rules. Lackner has received continuous funding for his research through multiple projects: SuDeMa (2019–2025): Algorithms for Sustainable Group Decision Making (Austrian Science Fund) FAIR (2013–2018): Fixed-Parameter Tractability in Artificial Intelligence and Reasoning (Austrian Science Fund) HINT (2012–2017): Heterogenous Information Integration (Austrian Science Fund) SEE (2012–2016): SPARQL Evaluation and Extensions (Vienna Science and Technology Fund) As an academic advisor, Lackner has supervised doctoral and master's students including J. Maly (2020) on ranking sets of objects and B. Krenn (2019) on algorithms for implicit delegation to predict preferences. His work appears in premier venues including AAAI, IJCAI, AAMAS, and the Journal of Artificial Intelligence Research, demonstrating both theoretical depth and practical relevance to democratic processes and collective decision making.
Jan Maly is an Assistant Professor at the Institute for Data, Process and Knowledge Management (DPKM) of the Vienna University of Economics and Business (WU Wien) and a postdoctoral researcher in the Database and Artificial Intelligence Group (DBAI) of TU Wien. He is currently on parental leave until September 15, 2024. His educational background includes mathematics and philosophy studies at Würzburg University, specialization in mathematical logic at Vienna University, and a PhD in computer science from TU Wien completed in 2020. Dr. Maly's research focuses on Computational Social Choice (COMSOC), Logic and Knowledge Representation, with particular emphasis on Participatory Budgeting and Fairness in Online Decision Making. His work aims to develop tools that help people make better decisions, individually or as a group, by investigating non-standard voting frameworks and computational complexity questions. His recent work addresses the critical problem that simple majority voting in online environments marginalizes minority opinions and reinforces filter bubble effects. Analysis of his publication record reveals a strong focus on developing fair voting mechanisms for participatory budgeting and online decision-making. His work bridges theoretical computational social choice with practical applications, particularly in developing algorithms that ensure proportional representation while maintaining computational feasibility. The publications demonstrate increasing sophistication in handling multi-issue decisions, perpetual voting scenarios, and complex budget allocation problems. netidee SCIENCE grant (2024, ~400,000 euros) Erwin Schrödinger Fellowship from FWF Dr. Maly has co-supervised PhD students including Simon Rey and Michael Bernreiter. His collaborative work spans multiple institutions, including the University of Amsterdam where he worked from September 2021 to April 2023 as a member of the COMSOC Group. He is also the co-founder of the European Digital Democracy Network, which organized its first conference in April 2024 bringing together over sixty scientists and practitioners.
Warut Suksompong is an Assistant Professor in the School of Computing at the National University of Singapore (NUS) and NUS Presidential Young Professor. He researches algorithmic game theory, computational social choice, and fair division, developing mechanisms for equitable resource allocation under constraints. His work formalizes fairness concepts like Weighted Envy-Freeness (WEF) and Weak WEF for indivisible items, with applications to cake cutting, tournament design, and budget aggregation. He created the 'Fast & Fair' platform implementing fair division algorithms. Recent publications explore truthfulness in resource sharing, graphical allocation constraints, and ordinal fairness guarantees. His research bridges computer science, economics, and operations research through mathematically rigorous models. Contributions: Designed picking-sequence algorithms for weighted fair division, characterized maximum Nash welfare solutions, and established asymptotic existence results for proportional allocations.
Edith Elkind is a Professor of Computing Science at the University of Oxford, affiliated with Balliol College. She joined Oxford in 2013 after previous positions at Nanyang Technological University and holds a PhD from Princeton University. Her research focuses on algorithmic game theory and computational social choice, examining how algorithms can model collective decision-making in social and economic systems. Elkind's work spans voting theory, cooperative games, and multiwinner elections. Key interests include: Design and analysis of voting systems Stability in cooperative games with overlapping coalitions Complexity of election problems Preference modeling in social choice Her recent publications demonstrate a strong emphasis on Condorcet domains, voting rule properties, and preference structures. Over 50% of her work in the past five years explores computational aspects of social choice, with increasing attention to multiagent systems and algorithmic game theory applications. She has supervised six PhD/Master's students including Jiarui Gan and Dominik Peters. Her research group collaborates internationally with institutions in Europe and Asia, focusing on game-theoretic models of collective behavior.
Subhash Suri is a Distinguished Professor in the Department of Computer Science at the University of California, Santa Barbara (UCSB). He holds academic ranks such as Fellow of ACM, AAAS, and IEEE, and is an ACM Distinguished Scientist. His research focuses on algorithms, computational geometry, sensor networks, robotics, and social networks. He directs the Applied Algorithms Lab and the Center for Geometric Computing at UCSB. Education: Ph.D. Computer Science, Johns Hopkins University, 1987 M.S. Computer Science, Johns Hopkins University, 1984 B.S. Electronics and Communication, IIT Roorkee, India, 1981 Research Interests: Prof. Suri's work emphasizes geometric and network algorithms, with applications in diverse fields. His lab develops foundational methods in computational geometry, graph theory, and algorithmic decision-making. Recent projects include dynamic geometric set cover, polychromatic TSP approximation, and shortest paths in complex environments. Awards: His honors include ACM/AAAS/IEEE Fellowships and recognition for contributions to algorithm design and analysis. Students/Grants: Advised over 20 graduate students, including notable alumni like Neeraj Kumar (Facebook) and Luca Foschini (Evidation Health). Active in NSF workshops and program committees (e.g., SoCG, WAFR, SWAT). Teaching: Recent courses include CS 130a/b (Data Structures/Algorithms), CS 190A (Algorithmic Decision Making), and CS 235 (Computational Geometry).
Krzysztof Sornat is a Lecturer at the Institute of Computer Science , Faculty of Computer Science, AGH University of Science and Technology in Kraków. His research focuses on computational aspects of voting theory, including multiwinner elections, participatory budgeting, and liquid democracy systems. His recent work analyzes approximation algorithms for submodular optimization (2024), complexity of subelection isomorphism (2024), and fine-grained hardness results in voting mechanisms (2021-2025). Publications demonstrate expertise in algorithmic fairness, preference aggregation, and parameterized complexity. Key collaborations include studies on Schulze voting methods (2021), minimax approval voting approximations (2018), and multiwinner rule comparisons (2023). Active in both theoretical algorithm design and practical implementation challenges.
Kamesh Munagala is a Professor in the Computer Science Department at Duke University's Pratt School of Engineering. His academic career spans theoretical computer science with a focus on approximation algorithms, online algorithms, and computational economics. He has made significant contributions to resource allocation, decision making, and provisioning problems across various applications including data networks, facility location, data center scheduling, ad slot allocation, ride-share scheduling, and civic budgeting. Professor Munagala's research interests span several key areas in theoretical computer science: Theoretical foundations of approximation algorithms and online algorithms Computational economics and market design Resource allocation with fairness constraints Algorithmic game theory and mechanism design Persuasion and information revelation in optimization contexts Group fairness based on proportionality and stability His recent publications demonstrate a strong focus on fairness in algorithmic decision-making, particularly in societal contexts like school assignment and participatory budgeting. He has also made significant contributions to the theory of Bayesian persuasion and information disclosure in competitive settings. His work bridges theoretical computer science with practical applications in social choice, economics, and policy-making. Notable scientific achievements include: Best paper award at WINE 2018 for 'A simple mechanism for a budget constrained buyer' Multiple publications in top theoretical computer science conferences including STOC, SODA, and FOCS Significant contributions to the understanding of fairness in resource allocation Innovative work on metric distortion in social choice Professor Munagala has advised numerous students and collaborators, with recent work involving researchers such as Govind S. Sankar, Yiheng Shen, and Kangning Wang. His research has been supported by various grants, though specific grant details aren't provided in the available information. He teaches advanced courses in algorithms, including Algorithm Design, Randomized Algorithms, and Algorithmic Game Theory, shaping the next generation of theoretical computer scientists. His work has implications for real-world systems requiring fair and efficient decision-making, from school assignment algorithms to data exchange markets and civic budgeting platforms. He is actively engaged in both theoretical advancements and practical implementations of his research.
Jie He is a PreDoc Researcher at the Department of Cyber-Physical Systems, Technische Universität Wien. His research spans computational social choice, algorithmic game theory, and formal methods in robotics and IoT systems. He works on multidisciplinary problems involving complexity analysis, fair division, and preference modeling. Current projects: EdgeAI (2022–2025), TAIGER (2023–2027), ADEX (2020–2024) Key collaborations: Research with R. Grosu, E. Bartocci, D. Nickovic Research interests focus on computational aspects of collective decision-making , including fair division, matching problems, and preference modeling. He works on both theoretical foundations (e.g., parameterized complexity) and practical applications (e.g., robotic-IoT systems). His publication history reveals deep expertise in computational complexity of social choice problems, with recent work on 3D stable roommates , fair division in graph-structured settings , and preference modeling through Euclidean and Manhattan geometries. As an advisor, he supervised diploma theses on: Optimization strategies for 5G transceivers Dynamic object detection in multi-agent systems His work appears in top conferences like ACM/IEEE DAC, ICSE, and various computational social choice venues.
Anne-Marie George is an Associate Professor at the University of Oslo, affiliated with the Scientific Computing and Machine Learning department. Her research focuses on handling user preferences through modeling, elicitation, inference, and fairness in group decision-making systems. Academic Rank: Associate Professor Department: Scientific Computing and Machine Learning Email: annemage@ifi.uio.no Her work spans computational social choice, preference learning, and algorithmic fairness, with recent publications addressing dynamic resource allocation, robust recourse in binary problems, and fair voting procedures. Collaborations with researchers like Christos Dimitrakakis highlight interdisciplinary efforts in AI ethics and decision theory. Recent trends in her publications include: Dynamic allocation fairness Preference-based decision systems Explainable AI for resource distribution Ontology engineering applications Robustness in allocation algorithms Multiwinner voting optimization
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