Junpei Komiyama
Affiliated Assistant Professor of Machine Learning
Research Interests
Professor Komiyama's research interests span machine learning, sequential decision-making, and economics. His research topics include: * Multi-armed bandits and best-arm identification, focusing on finding the best decision-making strategies. Email
Prior to joining joining MBZUAI, Professor Komiyama worked at the New York University Stern School of Business from 2019 to 2025 as an Assistant Professor of Technology, Operations, and Statistics. He worked at the University of Tokyo as a Research Associate from 2016 to 2019. Professor Komiyama received his Ph.D. in Information Science from the University of Tokyo’s Graduate School of Information Science and Technology in 2016.
- Ph.D. (U-Tokyo)
- Postdoctoral (Institute of Industrial Science, U-Tokyo)
- Junpei Komiyama, Taira Tsuchiya, and Junya Honda. “Minimax Optimal Algorithms for Fixed-Budget Best Arm Identification.” NeurIPS 2022. * Monte Carlo Tree Search
- Abe Kenshi, Junpei Komiyama, and Atsushi Iwasaki. “Anytime Capacity Expansion in Medical Residency Match by Monte Carlo Tree Search.” IJCAI 2022. * Mechanism design and market mechanisms
- Junpei Komiyama and Shunya Noda. “On Statistical Discrimination as a Failure of Social Learning: A Multiarmed Bandit Approach.” Management Science. * Reward learning, especially how machine learning systems can explore optimal decision-making.