I am interested in developing scalable, structured, and interpretable Bayesian methods, with a focus on Bayesian nonparametrics. These often involve building probabilistic models, with particular interests being:

(1) incorporating prior information relevant to a specific problem of interest,

(2) bypassing existing computational bottlenecks, and

(3) obtaining provable theoretical guarantees.

As a part of the global OHDSI community, I am passionate about delivering reliable clinical evidence from massive observational healthcare data-driven studies.

Preprints

  1. Chattopadhyay, S., Bu, F., Schuemie, M. J., …, Suchard, M. A. (2024). Comparative performance of the concurrent comparator design with existing vaccine safety surveillance approaches on real-world observational health data. (preprint available upon request)
  2. Chattopadhyay, S., Zhang, A. R., & Dunson, D. B. (2024). Blessing of dimension in Bayesian inference on covariance matrices. arXiv preprint arXiv:2404.03805. (Link)

Publications

  1. Morales, D. R., Bu, F., Viernes, B., DuVall, S. L., Matheny, M. E., Simon, K. R., Falconer, T., Richter, L. R., Ostropolets, A., Lau, W. C. Y., Man, K. K. C, Chattopadhyay, S., …, Suchard, M. A. (2025). Risk of Thyroid Tumors With GLP-1 Receptor Agonists: A Retrospective Cohort Study. Diabetes Care, dc250154.
  2. Chattopadhyay, S., Engel, S. M., & Dunson, D. (2025). Inferring synergistic and antagonistic interactions in mixtures of exposures. The Annals of Applied Statistics, 19(1), 169-190.
  3. Khera, R., Aminorroaya, A., Dhingra, L.S., Thangaraj, P.M., Camargos, A.P., Bu, F., Ding, X., Nishimura, A., Anand, T.V., Arshad, F. and Blacketer, C., Chai, Y., Chattopadhyay, S., …, Suchard, M. A. (2024). Comparative Effectiveness of Second-line Antihyperglycemic Agents for Cardiovascular Outcomes: A Large-scale, Multinational, Federated Analysis of the LEGEND-T2DM Study. Journal of the American College of Cardiology, 84(10), 904-917. (Link)
  4. Chattopadhyay, S., Chakraborty, A., & Dunson, D. B. (2023). Nearest Neighbor Dirichlet Mixtures. Journal of Machine Learning Research. (Link)
  5. Maitre, L., Guimbaud, J. B., Warembourg, C., Güil-Oumrait, N., Petrone, P. M., Chadeau-Hyam, M., … & Exposome Data Challenge Participant Consortium. (2022). State-of-the-art methods for exposure-health studies: results from the exposome data challenge event. Environment International. (Link)