معرفی
Joonha Park is an Assistant Professor in the Department of Mathematics at the University of Kansas (since 2020), affiliated with the College of Liberal Arts & Sciences. His research focuses on computational statistics, including sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC) methods, with applications to public health, Bayesian analysis, and spatio-temporal systems. He holds a Ph.D. in Statistics from the University of Michigan (2018), an M.A. from KAIST (2012), and a B.S. from MIT (2007).
Education:
- Ph.D. in Statistics, University of Michigan, 2018
- M.A. in Mathematical Sciences, KAIST, 2012
- B.S. in Mathematics, MIT, 2007
Research Interests: Computational methods for statistical inference, spatiotemporal modeling, Bayesian statistics, and applications to public health. His work emphasizes developing scalable algorithms for implicitly defined models and high-dimensional systems.
Publications: Recent work includes cross-cultural studies on well-being, computational inference methods (e.g., the sbi R package), and epidemiological modeling. His research bridges statistical methodology and real-world applications, often involving international collaborations.
Labs/Teams: Active in computational statistics research groups, contributing to open-source tools like sbi and athmc. Collaborates with interdisciplinary teams on public health data analysis and spatiotemporal dynamics.
