Noam Brown is a Research Scientist at Facebook AI Research working on multi-agent artificial intelligence, 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.

Vincent Conitzer is Professor of Computer Science (with affiliate/courtesy appointments in Machine Learning, Philosophy, and the Tepper School of Business) at Carnegie Mellon University, where he directs the Foundations of Cooperative AI Lab (FOCAL). He is also Head of Technical AI Engagement at the Institute for Ethics in AI, and Professor of Computer Science and Philosophy, at the University of Oxford. Previous to joining CMU, Conitzer was the Kimberly J. Jenkins Distinguished University Professor of New Technologies and Professor of Computer Science, Professor of Economics, and Professor of Philosophy at Duke University. He received Ph.D. (2006) and M.S. (2003) degrees in Computer Science from Carnegie Mellon University, and an A.B. (2001) degree in Applied Mathematics from Harvard University.

Yali Du is a Lecturer (Assistant Professor) at King’s College London. Her research aim is to enable machines to exhibit cooperative and trusted behaviour in intelligent decision making tasks.  Her research interest lies in reinforcement learning and evaluation, multi-agent cooperation, and social agency (e.g.  human-involved learning, safety and ethics). She was chosen for AAAI New Faculty Highlights program (2023). Prior to her current position, Yali received her PhD from the University of Technology Sydney and worked as a postdoc at University College London.

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Jakob Foerster is an Associate Professor at the department of engineering science at the University of Oxford. During his PhD at Oxford he helped bring deep multi-agent reinforcement learning to the forefront of AI research and interned at Google Brain, OpenAI, and DeepMind. After his PhD he worked as a research scientist at Facebook AI Research in California, where he continued doing foundational work. He was the lead organizer of the first Emergent Communication workshop at NeurIPS in 2017, which he has helped organize ever since and was awarded a prestigious CIFAR AI chair in 2019.

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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.

Natasha Jaques is a Senior Research Scientist at Google Brain. Her research focuses on Social Reinforcement Learning in multi-agent and human-AI interactions. Natasha completed her PhD at the MIT Media Lab, where her thesis received the Outstanding PhD Dissertation Award from the Association for the Advancement of Affective Computing, and completed a postdoc at UC Berkeley. Her work has received Best Demo at NeurIPS, an honourable mention for Best Paper at ICML, Best of Collection in the IEEE Transactions on Affective Computing, and received several best paper awards at NeurIPS and AAAI workshops. She has interned at DeepMind, Google Brain, and was an OpenAI Scholars mentor. Her work has been featured in Science Magazine, MIT Technology Review, Quartz, IEEE Spectrum, Boston Magazine, and on CBC radio. Natasha earned her Masters degree from the University of British Columbia, and undergraduate degrees in Computer Science and Psychology from the University of Regina.

Joel Z. Leibo is a research scientist at DeepMind. He obtained his PhD in 2013 from MIT where he worked on the computational neuroscience of face recognition with Tomaso Poggio. Nowadays, Joel's research is aimed at the following questions: How can we get deep reinforcement learning agents to perform complex cognitive behaviors like cooperating with one another in groups? How should we evaluate the performance of deep reinforcement learning agents? How can we model processes like cumulative culture that gave rise to unique aspects of human intelligence?

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.