The Berkeley–Hopkins Seminar Series on Learning-Enabled Multi-Agent Systems (LEMAS) is a weekly seminar that brings together researchers working at the forefront of learning and/or incentive design in multi-agent systems. We hope to create a platform for sharing recent advances, discussing key challenges, and fostering new research directions. We are based at Johns Hopkins University and UC Berkeley and currently operating in a hybrid format (talks occur both in-person and livestreamed over Zoom).
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Time: Every Friday, 10-11 a.m. Pacific Time
Zoom link for the virtual attendance: https://wse.zoom.us/j/97690026042
Organizers:
Chinmay Maheshwari (Assistant Professor, ECE, Johns Hopkins University)
Shankar Sastry (Professor, EECS, UC Berkeley)
Pan-Yang Su (Ph.D. Student, EECS, UC Berkeley)
Youtube Channel: https://www.youtube.com/@Berkeley-HopkinsLEMAS
Spring 2026 Schedule
Spring 2026 Schedule
Note: Presentation titles in parentheses provide tentative descriptions of upcoming talks, and will be replaced with actual presentation titles when available. Titles for past talks link to more information about the talk and speaker, as well as a recording and/or slide deck when available.
Date Speaker Presentation Title
February 13 Haifeng Xu The Interplay of Economic Thinking and Language Models: Vignettes and Lessons
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February 27 Gabriele Farina How Strong a Notion of Rationality Can We Learn Efficiently in Multi-Agent Settings?
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March 6 Mingyan Liu Inter-temporal Incentive and Its Application in Strategic Classification and Security Audit
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April 3 Milind Tambe
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April 10 Amanda Prorok
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April 17 Ketan Savla
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April 24 Stephanie Gil
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May 8 Xiaowu Dai
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May 15 Alireza Fallah
TalK information (Spring 2026)
TalK information (Spring 2026)
Haifeng Xu (UChicago)
Title: The Interplay of Economic Thinking and Language Models: Vignettes and Lessons
Abstract: Although large language models (LLMs) have achieved remarkable technological successes, their effective application in economic domains—critical for realizing their broader promise across society and markets—remains largely underexplored. This talk presents our recent research efforts at bridging economic thinking and LLMs, with concrete instantiations in areas such as mechanism design, information design, and forecasting. A key theme of the talk is to illustrate how this emerging technology gives rise to genuinely new research questions in foundational economic settings. We hope that explorations along this line will help move LLM research towards realizing its true economic impact in the real world.
Bio: Haifeng Xu is an assistant professor in computer science and data science at the University of Chicago, and directs the Strategic IntelliGence for Machine Agents (SIGMA) research lab which focuses on multi-agent AI and data-driven decision making. His works have been recognized by a few major awards including AI2050 Early Career Fellow, IJCAI Early Career Spotlight, a Best Paper at the Web Conference and a Best Student Paper at AAMAS. Haifeng publishes regularly at leading venues in machine learning and computational economics, and serves as area chair or senior program committee for venues such as ICML, NeurIPS, EC, AAAI, etc.
Video Link: https://youtu.be/TnYnkL1ERdo
Gabriele Farina (MIT)
Title: How Strong a Notion of Rationality Can We Learn Efficiently in Multi-Agent Settings?
Abstract: A central question in multi-agent learning is: how strong a notion of rational behavior can be guaranteed through efficient no-regret learning? Classical results show that minimizing regret leads to coarse correlated equilibria, and enriching the class of allowed deviations yields progressively stronger equilibrium concepts. But how far can this hierarchy be pushed while retaining efficient algorithms in general convex and extensive-form games?
In this talk, I present recent results that characterize the strongest notions of rationality that can be efficiently learned in this broad setting. We show that linear and low-degree polynomial correlated equilibria—strictly stronger than coarse correlated equilibria and natural relaxations of correlated equilibria—can be computed and learned efficiently even in general convex and extensive-form games.
En route to the result, we will introduce new algorithmic tools with broader applicability. First, the natural deviation sets underlying stronger notions of regret do not admit efficient separation or optimization oracles. To address this, we introduce a new algorithmic primitive, semiseparation, which enables regret minimization over convex sets that lack classical separation oracles. Second, our analysis leverages a fast computational version of von Neumann’s minimax theorem, yielding convergence rates that scale logarithmically in the desired accuracy. This tool has further applications, including to variational inequalities, fixed-point computation, and online multicalibration.
Based on joint work that has appeared at STOC’25 and ACM EC’25.
Bio: Gabriele Farina is an Assistant Professor in the Department of Electrical Engineering and Computer Science at MIT, with affiliations in LIDS and the Operations Research Center. His research combines techniques and notions of strategic behavior from game theory together with modern tools from machine learning, optimization, and statistics to construct state-of-the-art methods for learning and decision-making in multi-agent systems. He received his Ph.D. in Computer Science from Carnegie Mellon University, and his work has been recognized with several awards, including a Best Paper Award at NeurIPS'20 and an Outstanding Paper Honorable Mention at ICLR'23. His dissertation received the ACM SIGecom Doctoral Dissertation Award and one of the two ACM Dissertation Award Honorable Mentions, among others. He is an AI2050 Early Career Fellow, and the recipient of an NSF CAREER award.
Video link: https://www.youtube.com/watch?v=X5vd5iS6_WY
Title: Inter-temporal Incentive and Its Application in Strategic Classification and Security Audit
Abstract: Strategic classification problems are typically framed as a Stackelberg game, where self-interested individuals or agents manipulate their response to obtain favorable decision outcomes made by a classifier, generally turning to dishonest actions when they are less costly than genuine efforts. It is now well understood that a one-shot formulation is fundamentally unable to mitigate this, whereas repeated interactions give rise to what's known as inter-temporal incentives that can give the designer (the principal) greater ability to induce honesty. In this talk I will present two sequential models that explore the strategic principal-agent interactions in such a context. In the first case, an agent has to go through a progression of classifiers with increasing rewards but also increasing difficulty. I will characterize the agent's best response to a given progression, and derive conditions under which the principal, through judicious design of the sequence of classifiers, can incentivize an agent to reach an arbitrarily high level via solely honest means. In the second case, the agent does not have a cheating option but can choose to opt out of the classification exercise entirely. I will examine how the designer might incentivize the agent to opt in and to exert higher effort. (This is joint work with my former student Ziyuan Huang and current student Lina Alkami)
Bio: Mingyan Liu is the T.C. Chang Professor of Engineering, a professor of Electrical Engineering & Computer Science, and the Associate Dean for Academic Affairs of the College of Engineering at the University of Michigan, Ann Arbor. She received her Ph.D. Degree in electrical engineering from the University of Maryland, College Park, in 2000 and has been with the University of Michigan ever since. Her research interests are in resource allocation, sequential decision and learning theory, game theory and incentive mechanisms, with applications to large-scale networked systems, cybersecurity and cyber risk quantification. Some of her research in this space has been successfully commercialized. She is a Fellow of the IEEE and a member of the ACM.
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