Edward Hughes is a Staff Research Engineer at DeepMind. His research pioneers the field of Cooperative AI, algorithms that work in partnership with each other and humans. His work lies at a crossroads linking multi-agent reinforcement learning with sociology and economics. Edward received his PhD in Theoretical Physics from Queen Mary University of London on applications of string theory to particle scattering. He read Mathematics at Cambridge University, where he graduated with Distinction.

Edward’s publications are available on Google Scholar.

Noam Brown is a Researcher at OpenAI. Before that, he worked on multi-agent artificial intelligence at Facebook AI Research, with a particular focus on imperfect-information games. He co-created Libratus and Pluribus, the first AIs to defeat top humans in two-player no-limit poker and multiplayer no-limit poker, respectively. He has received the Marvin Minsky Medal for Outstanding Achievements in AI, was named one of MIT Tech Review's 35 Innovators Under 35, and his work on Pluribus was named by Science Magazine to be one of the top 10 scientific breakthroughs of the year. Noam received his PhD from Carnegie Mellon University, where he received the School of Computer Science Distinguished Dissertation Award.

Fei Fang is Leonardo Assistant Professor at the Institute for Software Research in the School of Computer Science at Carnegie Mellon University. Before joining CMU, she was a Postdoctoral Fellow at the Center for Research on Computation and Society (CRCS) at Harvard University, hosted by David Parkes and Barbara Grosz. She received her Ph.D. from the Department of Computer Science at the University of Southern California advised by Milind Tambe (now at Harvard). Her research lies in the field of artificial intelligence and multi-agent systems, focusing on integrating machine learning with game theory. Her work has been motivated by and applied to security, sustainability, and mobility domains, contributing to the theme of AI for Social Good. She is the recipient of the IJCAI-21 Computers and Thought Award. She was named to IEEE Intelligent Systems’ “AI’s 10 to Watch” list for 2020. She received an NSF CAREER Award in 2021.

Nisarg Shah is an Associate Professor of computer science at the University of Toronto. He has been recognized as part of "Innovators Under 35" by MIT Technology Review Asia Pacific in 2022 and "AI's 10 to Watch" by IEEE Intelligent Systems in 2020. He is also the winner of the 2016 IFAAMAS Victor Lesser Distinguished Dissertation Award and the 2014-2015 Facebook PhD Fellowship. Shah conducts research at the intersection of computer science and economics, addressing issues of fairness, efficiency, elicitation, and incentives that arise when humans are affected by algorithmic decision-making. His recent work develops theoretical foundations for fairness in fields such as voting, resource allocation, and machine learning. He has co-developed two not-for-profit websites, Spliddit.org and RoboVote.org, which have helped more than 200,000 users make provably fair and optimal decisions in their everyday lives. He earned his PhD in computer science at Carnegie Mellon University and was a postdoctoral fellow at Harvard University.

Joseph Halpern received a B.Sc. in mathematics from the University of Toronto in 1975 and a Ph.D. in mathematics from Harvard in 1981. In between, he spent two years as the head of the Mathematics Department at Bawku Secondary School, in Ghana. After a year as a visiting scientist at MIT, he joined the IBM Almaden Research Center in 1982, where he remained until 1996, also serving as a consulting professor at Stanford. In 1996, he joined the Computer Science Department at Cornell University, where he is currently the Joseph C. Ford Professor and was department chair 2010-14. Halpern's major research interests are in reasoning about knowledge and uncertainty, security, distributed computation, decision theory, and game theory. Together with his former student, Yoram Moses, he pioneered the approach of applying reasoning about knowledge to analyzing distributed protocols and multi-agent systems. He has coauthored 5 patents, three books ("Reasoning About Knowledge", "Reasoning about Uncertainty", and "Actual Causality"), and over 360 technical publications.

Gillian Hadfield is the inaugural Schwartz Reisman Chair in Technology and Society, Professor of Law, Professor of Strategic Management at the University of Toronto and holds a CIFAR AI Chair at the Vector Institute for Artificial Intelligence.  She is a Schmidt Sciences AI2050 Senior Fellow. She was the inaugural Director of the Schwartz Reisman Institute for Technology and Society from 2019 through 2023. Her research is focused on the study of human and machine normative systems; safety and governance for artificial intelligence (AI); and innovative design for legal and dispute resolution systems in advanced and developing market economies.  She has also long studied the markets for law, lawyers, and dispute resolution; and contract law and theory. She teaches Contracts and Governance of AI.

Michael Dennis is a Research Scientist at DeepMind. His work has focused on the automatic generation of environments suitable for training RL agents. These works vary from co-creating the GenIE generative environment model -- trained from unlabeled internet videos -- to helping to pioneer the field of Unsupervised Environment Design (UED) for automatically generating curricula of environments. His research aims to bridge the gap between classical decision theory, and modern deep learning. Before joining Deepmind, he received his Ph.D from the Center for Human-Compatible AI at UC Berkeley.