Constantinos Daskalakis

Constantinos Daskalakis is a Professor of Electrical Engineering and Computer Science at MIT. He works on Computation Theory and its interface with Game Theory, Economics, Probability Theory, Machine Learning and Statistics. He has resolved long-standing open problems about the computational complexity of Nash equilibrium, and the mathematical structure and computational complexity of multi-item auctions. His current work focuses on high-dimensional statistics and learning from biased, dependent, or strategic data. He has been honored with the ACM Doctoral Dissertation Award, the Kalai Prize from the Game Theory Society, the Sloan Fellowship in Computer Science, the SIAM Outstanding Paper Prize, the Microsoft Research Faculty Fellowship, the Simons Investigator Award, the Rolf Nevanlinna Prize from the International Mathematical Union, the ACM Grace Murray Hopper Award, and the Bodossaki Foundation Distinguished Young Scientists Award.

Sarit Kraus

Sarit Kraus is a Professor of Computer Science at Bar-Ilan University. Her research is focused on intelligent agents and multiagent systems (including people and robots). Kraus was awarded the IJCAI Computers and Thought Award, ACM SIGART Agents Research award, ACM Athena Lecturer, the EMET prize and was twice the winner of the IFAAMAS influential paper award. She is AAAI, ECCAI and ACM fellow and a recipient of the advanced ERC grant. She is a member of The Israel Academy of Sciences and Humanities.

Elad Schneidman

Elad Schneidman is the Joseph and Bessie Feinberg Professor in the Department of Neurobiology at the Weizmann Institute of Science. He has been a Hurvitz complexity science foundation fellow, a recipient of the Peter and Patricia Gruber Award, and a visiting professor at NYU. His research focuses on questions at the intersection of neuroscience, biological networks, machine learning, and collective behavior. Using tools from statistical physics, machine learning, and information theory, he studies the nature of information representation and processing by large populations of neurons, collective behavior in groups of animals and of artificial agents, the design principles of biological networks, and statistical learning in primates and humans. credentials and the unique perspectives they will bring to the conference.

Lillian Ratliff

Lillian Ratliff is an Assistant Professor in the Department of Electrical Engineering at the University of Washington (UW), and an Adjunct Professor in the Allen School of Computer Science and Engineering at UW. Her research interests lie at the intersection of game theory, learning, and optimization. She draws on theory from these areas to develop analysis tools for studying algorithmic competition, cooperation and collusion and synthesis tools for designing algorithms with performance guarantees. In addition, she is interested in developing new theoretical models of human decision-making in consideration of behavioral factors in societal-scale systems (e.g., intelligent infrastructure, platform-based markets and e-commerce, etc.) and computational schemes to shape the outcome of competitive interactions. She is the recipient of an NSF Graduate Research Fellowship (2009), NSF CISE Research Initiation Initiative award (2017), and an NSF CAREER award (2019), the ONR Young Investigator award (2020) and the Dhanani Endowed Faculty Fellowship (2020).

Kaiqing Zhang

Kaiqing Zhang is currently a postdoctoral scholar affiliated with LIDS and CSAIL at Massachusetts Institute of Technology (MIT). He works jointly with Prof. Asu Ozdaglar, Prof. Russ Tedrake, and Prof. Constantinos Daskalakis. He received his Ph.D. from the Department of Electrical and Computer Engineering (ECE) at the University of Illinois at Urbana-Champaign (UIUC), fortunately advised by Prof. Tamer Başar. He has also received two M.S. degrees in ECE and Applied Math from UIUC, and B.E. from Tsinghua University. His research interests lie broadly in control theory, game theory, reinforcement learning, robotics, and their intersections.