Research

Research Interests

My research focuses on Variational Inference, Machine learning, Normalizing Flows, Neural Network, Bayesian Statistics, Spatial Statistics, and Functional Data Analysis.

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Publications

1. A Scalable Variational Bayes Approach for Fitting Non-Conjugate Spatial Generalized Linear Mixed Models via Basis Expansions
   Lee, J.H., and Lee, B.S. (2027)
   Computational Statistics & Data Analysis. [Code]

2. A Scalable Variational Bayes Approach to Fit High-Dimensional Spatial Generalized Linear Mixed Models
   Lee, J.H., and Lee, B.S. (2026)
   Technometrics, 68(1), 146–158. [Code]

3. Bayesian Methods for Quality Tolerance Limit (QTL) Monitoring
   Poythress, J.C., Lee, J.H., Takeda, K., and Liu, J. (2024)
   Pharmaceutical Statistics, 23(6), 1166-1180.

Under Review

1. Bayesian Hierarchical Dose-Response Model Averaging in Small Clinical Trials for Decision-Making.
   Wang, Y., Lee, J.H., Yamaguchi, Y., and Han, C. (2026) Under review.

Manuscripts in Preparation

1. A Scalable Variational Bayes Approach for Modeling Multivariate Spatial Models.
   Lee, J.H., and Bhadra, A. (2026)

2. Deep Spatial Function-on-Scalar Regression with Robust Nonparametric Testing.
   Lee, H.S., Kim, J.S., Park, J., Lee, B.S., and Lee, J.H. (2026)

3. A Scalable Variational Bayes Method for a Robust High-dimensional Bayesian Spatial Quantile Regression.
   Lee, J.H., Lee, S., and Kim, S.D. (2026)

4. Dynamic Stochastic General Equilibrium Models with Variational Inference Normalizing Flows.
   Hong, S., Lee, J.H. (2026)