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., Zhang, A. R., & Dunson, D. B. (2024). Blessing of dimension in Bayesian inference on covariance matrices. arXiv preprint arXiv:2404.03805. (Link)

Publications

  1. Chattopadhyay, S., Engel, S. M., & Dunson, D. (2024, Annals of Applied Statistics, in print). Inferring Synergistic and Antagonistic Interactions in Mixtures of Exposures. arXiv preprint arXiv:2210.09279. (Link)
  2. 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. et al. (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)
  3. Chattopadhyay, S., Chakraborty, A., & Dunson, D. B. (2023). Nearest Neighbor Dirichlet Mixtures. Journal of Machine Learning Research. (Link)
  4. 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)