Research works

* denotes equal contribution.

• Marginally interpretable spatial logistic regression with bridge processes.[link] [code]
  Lee, C. J., & Dunson, D. B. (2024). arXiv preprint arXiv:2412.04744.

• Logistic-beta processes for modeling dependent random probabilities with beta marginals.[link] [code] [slide]
  Lee, C. J., Zito, A., Sang, H., & Dunson, D. B. (2024). arXiv preprint arXiv:2402.07048.

• A scalable two-stage Bayesian approach accounting for exposure measurement error in environmental epidemiology.[link] [code]
  Lee, C. J., Symanski, E., Rammah, A., Kang, D. H., Hopke, P. K, & Park, E. S. (2024). Biostatistics, kxae038. (Early Career Award, ASA Section on Statistics in Epidemiology)

• Loss-based objective and penalizing priors for model selection problems.[link]
  Lee, C. J. (2023). arXiv preprint arXiv:2311.13347.

• Rapidly mixing multiple-try Metropolis algorithms for model selection problems.[link] [code]
  Chang, H.*, Lee, C. J.*, Luo, Z. T., Sang, H., & Zhou, Q. (2022). Advances in Neural Information Processing Systems (NeurIPS) 35 (oral-designated).

• Why the rich get richer? On the balancedness of random partition models.[link] [code]
  Lee, C. J., & Sang, H. (2022). Proceedings of the 39th International Conference on Machine Learning (ICML), PMLR 162:12521 - 12541.

• T-LoHo: A Bayesian regularization model for structured sparsity and smoothness on graphs.[link] [code]
  Lee, C. J., Luo, Z. T., & Sang, H. (2021). Advances in Neural Information Processing Systems (NeurIPS) 34, 598-609.

Please also see my CV for details on ongoing projects.

I’m always open to collaboration opportunities on the field of probabilistic machine learning and spatial statistics. Feel free to contact me via email changwoo.lee (at) duke.edu.