Jeongyeol Kwon ( joined UW-Madison in September 2022. He is doing a postdoc with Robert Nowak. He completed his PhD at The University of Texas at Austin in August 2022, advised by Constantine Caramanis. 

Jeongyeol is broadly interested in theoretical aspects of machine learning and optimization. During his Ph.D., Jeongyeol has focused on fundamental questions arising from statistical inference and sequential decision making in the presence of latent variables. In his earlier PhD years, he worked on the analysis of the Expectation-Maximization algorithm and showed its convergence and statistical optimality properties. More recently, he has been more involved in reinforcement learning theory with partial observations inspired by real-world examples. He plans to enlarge his scope to more diverse research topics including stochastic optimization and more practical approaches for RL and other related problems.