Young Hyun Cho

I’m Young Hyun Cho (please call me Young), a Postdoctoral Fellow in the Department of Statistics at Harvard University, working with Susan A. Murphy. I received my Ph.D. in Statistics from Purdue University, where I was advised by Will Wei Sun and Jordan Awan. I received my M.S. in Statistics from Seoul National University, where I was advised by Johan Lim.

My research develops statistical foundations for modern data-driven decision making, with a focus on sequential decision making—particularly reinforcement learning and multi-agent reinforcement learning—and trustworthy learning and inference, including uncertainty quantification and privacy-preserving methods.

I am broadly interested in the two-way exchange between statistics and modern AI: how AI systems, including LLMs, can reshape statistical learning and inference, and how statistical principles can guide their training, evaluation, and deployment.

A central goal of my research—at least as of July 2026!—is to help bring reinforcement learning into real-world settings and, ultimately, everyday life. This requires addressing uncertainty throughout learning and deployment: learning from inconsistent or heterogeneous human feedback; making decisions in environments shaped by interacting agents that cooperate, compete, and adapt; connecting the engineering realities of deployment with theory; and drawing valid conclusions from noisy, adaptively collected data. Stay tuned for updates as this research agenda evolves.