Yishay Mansour

Yishay Mansour is a Professor of Computer Science at Tel Aviv University's School of Computer Science, where he has been a faculty member since 1992. He previously served as the first head of the Blavatnik School of Computer Science (2000-2002) and currently directs the Israeli Center of Research Excellence in Algorithms. He maintains a part-time research position at Google Research in Tel Aviv and has held previous positions at Microsoft Research in Israel.

Professor Mansour received his PhD from MIT in 1990, followed by a postdoctoral fellowship at Harvard and a position as Research Staff Member at IBM T.J. Watson Research Center. His research spans several key areas including machine learning (particularly reinforcement learning and multi-armed bandits), algorithmic game theory, communication networks, and theoretical computer science. He has published over 100 journal papers and 200 conference proceedings, with recent work focusing on regret minimization, differential privacy, and reinforcement learning theory.

His recent publications demonstrate a strong focus on theoretical foundations of machine learning, with particular emphasis on bandit algorithms, reinforcement learning, and game theory. The trends in his work show increasing integration of societal challenges like fairness and privacy into learning algorithms, alongside continued theoretical advances in optimization and learning guarantees.

• ACM Fellow (2014)
• ELLIS Fellow (2020)
• Asia-Pacific Artificial Intelligence Association Fellow (2022)
• AI Industry Alliance Fellow (2023)
• ERC Advanced Grant (2020)
• Multiple best paper awards including at COLT (2007), EC (2007), and EC (2025)

Professor Mansour has supervised over a dozen doctoral students and numerous master's students, with his advisees working on topics ranging from reinforcement learning algorithms to algorithmic game theory and online learning. He has served on numerous program committees including as Program Chair for STOC (2016) and COLT (1998), and is currently an associate editor for several distinguished journals. His research has been supported by multiple grants from the Israel Science Foundation, ERC Advanced Grant, and other funding bodies.

He leads the COLT-MDP lab which focuses on advancing the theory of reinforcement learning through three pillars: compact representation, efficient computation, and addressing societal challenges including fairness and privacy in learning algorithms.