Heesang Lee

Adjunct Faculty

Functional data analysis, spatial data analysis, and Bayesian inference for intractable likelihoods

Heesang Lee is a Ph.D. candidate in the Department of Statistics and Data Science at Yonsei University, South Korea. His research interests include functional data analysis, spatial data analysis, and Bayesian inference for intractable likelihoods. His recent work focuses on applying function-on-function regression models to spatial datasets and developing efficient Bayesian algorithms to approximate posterior distributions when the likelihood is computationally intractable. He currently serves as an Adjunct Instructor at George Mason University Korea, where he teaches CDS 230: Modeling and Simulation. He is based in South Korea and can be reached through his George Mason University email.

Selected Publications

- Lee, J. H., Kim, J., Lee, H., and Park, J. (2025) A Delayed Acceptance Auxiliary Variable MCMC for Spatial Models with Intractable Likelihood Function. Submitted
- Lee, H., Kim, S., Kang, B., and Park, J. (2025) A Stein Gradient Descent Approach for Doubly Intractable Distributions. submitted
- Lee, H., Oh, D., Choi, S., and Park, J. (2025) Bayesian Function-on-Function Regression for Spatial Functional Data. Bayesian Analysis. Accpeted