About
I am a Senior Research Associate in the Department of Biostatistics & Bioinformatics at Duke University, where I work with Susan Halabi. Previously, I was a postdoctoral associate with Li Ma in Duke’s Department of Statistical Science. I received my Ph.D. in Statistical Science from the University of California, Santa Cruz, where I was advised by Athanasios Kottas.
I develop Bayesian and nonparametric methods that adapt to the structure of scientific data while keeping inference interpretable and computationally tractable. My research centers on three areas: integrating clinical trial and real-world data to improve trial efficiency while accounting for heterogeneity across patient subgroups, modeling high-dimensional, sparse omics and microbiome data, and Bayesian nonparametric modeling of temporal and spatial point processes, including self-exciting processes. These methods are motivated by collaborations in precision oncology, microbiome science, and earthquake forecasting.
Selected Papers
Kim, H. and Halabi, S. (2026). “A Two-Stage Bayesian Framework for Combining Clinical Trial and Real-World Data: Model-Based Clustering and Hierarchical Modeling.” (Under review). [PDF].
Kim, H. and Ma, L. (2026). “An Exponential Scale Mixture Model for Metatranscriptomics Data with Application to Inflammatory Bowel Disease.” (Under revision). [Preprint].
Kim, H., Siddiqui, N. Y., Karstens, L., and Ma, L. (2026). “A negative binomial latent factor model for paired microbiome sequencing data.” BMC Bioinformatics, 27, 45. [Code].
Kim, H. and Kottas, A. (2026). “Bayesian Nonparametric Marked Hawkes Processes for Earthquake Modeling.” (Under revision). [Preprint].
Kim, H. and Kottas, A. (2026). “Nonparametric Bayesian Modeling and Inference for Hawkes Processes.” (Under revision). [PDF].
Kim, H. and Kottas, A. (2022). “Erlang mixture modeling for Poisson process intensities.” Statistics and Computing, 32, 3. [Code].