Research works

* denotes equal contribution.

• Scalable and robust regression models for continuous proportional data.[link] [code] [slide]
  Lee, C. J., Dahl, B. K., Ovaskainen, O., & Dunson, D. B. (2025). arXiv preprint arXiv:2504.15269. (Best long talk award at BAYSM 2025)

• 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.

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.