Chan Park

Chan Park is an Assistant Professor in the Department of Statistics at the University of Illinois Urbana-Champaign. He holds affiliations within the College of Liberal Arts & Sciences. His research focuses on causal inference under interference, unmeasured confounding, and optimal policy learning, leveraging nonparametric theory and optimization methods. Park holds a B.S. in Statistics from Seoul National University (2015) and a Ph.D. in Statistics from the University of Wisconsin-Madison (2022). Prior to his academic roles, he worked as a statistician at the Central Bank of Korea and completed a postdoctoral fellowship at the University of Pennsylvania.

Education background includes:

• B.S. in Statistics, Seoul National University (2009-2015)
• Ph.D. in Statistics, University of Wisconsin-Madison (2017-2022)

His research interests span causal inference methodologies, statistical theory, and applications in biostatistics and epidemiology. Recent work emphasizes robust covariate adjustment in cluster-randomized experiments and network treatment effects. No notable scientific awards are listed, though his publication record demonstrates impactful contributions to statistical methodology.

Advising and grants sections remain underdeveloped in available records. Park is actively involved in collaborative research projects and maintains a digital presence via GitHub. His lab focuses on advancing statistical techniques for complex observational and experimental data structures.