Bayesian Methods for Trustworthy Machine Learning: Trustworthy machine learning, including robustness, algorithmic fairness, differential privacy, and interpretability; nonparametric and semiparametric Bayesian methods, including moment-condition models, latent variable models, graphical models, Bayesian additive regression trees, and spanning-tree ensembles; Bayesian hypothesis testing.
Bayesian Computation for Scalable Uncertainty Quantification: Gaussian process approximations, marginal likelihood approximations, variational inference, and scalable computation for high-dimensional Bayesian models.
Clinical Evidence Synthesis in Early-Phase Trials for Cardiometabolic Health:
Methods: Bayesian pharmacokinetic modeling, time-to-event analysis, and semiparametric dose–response modeling.
Practice: Design, conduct, analysis, and reporting of large-scale clinical trials in diabetes, obesity, and related cardiometabolic and cardiovascular diseases at Eli Lilly and Company.
Real-World Evidence Synthesis Using Electronic Health Records:
Methods: Data integration, meta-learning, transfer learning, federated learning, Bayesian causal machine learning, and disease-progression modeling.
Applications: (a) Evidence synthesis across multi-institutional scleroderma research networks in collaboration with the Johns Hopkins Scleroderma Center. (b) quantitative characterization of complex medical histories and dynamic treatment decisions in bipolar disorder in collaboration with the Johns Hopkins Precision Medicine Center of Excellence in Mood Disorders.
Statistics and Machine Learning
[19] Chakraborty, A.,Federated proportional win-fractions regression for hierarchical composite endpoints in multi-center clinical studies. (Major revision invited from Journal of Applied Statistics, 2026+)
[18] Chakraborty, A., DUST: Directional uncertainty-aware and scale-invariant transfer-learning. Neural Information Processing Systems (NeurIPS, 2026)
[17] Chakraborty, A., Mandal, A., Chakraborty, A., Probabilistically routed Bayesian additive spanning trees for learning on constrained domains. (ICML, 2026) Link
[16] Chakraborty A., Bhattacharya A., Pati D., Rejoinder to the discussion of "Robust probabilistic inference via a constrained transport metric". (Bayesian Analysis, 2026) Link
[15] Chakraborty, A., Mandal, A. Non-parametric win fractions regression for composite outcomes. (2026). (Accepted to Lifetime Data Analysis)
[14] Chakraborty A., Bhattacharya A., Pati D. Fair clustering via hierarchical fair Dirichlet prior. Older version | New version (AISTATS 2026)
[13] Chakraborty A., Chakraborty A. Provably efficient and scalable Tobit Gaussian process regression for modeling air quality data. (Equal contribution, IEEE International Conference on Acoustics, Speech and Signal Processing, 2026) Link
[12] Chakraborty A., Geometric ergodicity of a Gibbs sampler for Bayesian linear regression with tail adaptive shrinkage. (Accepted to Statistics and Probability Letters, 2026)
[11] Chakraborty A., Datta S., Differentially private Bayesian tests. (Equal contribution, Accepted to Bayesian Analysis subject, 2026) Link
[10] Chakraborty A., Chakraborty A., Meta-learning with adaptive borrowing for high dimensional moment condition model. (IEEE International Conference on Big Data, 2025)
[9] Mandal A., Chakraborty A., Flexible survival regression with variable selection for heterogeneous population. (Equal contribution, IEEE International Conference on Bioinformatics and Computational Biology (ICBCB), 2025) Link [Oral designated]
[8] Chakraborty A., Empirical likelihood with sparsely permuted data. (IEEE International Conference on Big data, 2025) Link
[7] Chakraborty A., Bhattacharya A., Pati D., Robust probabilistic inference via a constrained transport metric. (Bayesian Analysis, 2025, published with discussion) Link
[6] Chakraborty A., Datta S., Learning with sparsely permuted data: a robust Bayesian approach. (IEEE International Conference on Big Data, 2024) Link [Oral designated]
[5] Chakraborty A., Bhattacharya A., Pati D., Constrained reweighting of distributions: an optimal transport approach. (Entropy, 2024) Link
[4] Chakraborty A., Bhattacharya A., Pati D., A Gibbs posterior framework for fair clustering. (Entropy, 2024), Link
[3] Chakraborty A., Chakraborty A., Scalable Gaussian process clustering. (Equal contribution, IEEE International Conference on Acoustics, Speech and Signal Processing, 2024), Link
[2] Chakraborty A., Probabilistic spike train inference. (IEEE International Conference on Acoustics, Speech and Signal Processing, 2024) Link [Oral designated]
[1] Chakraborty A. Efficient Bayesian high-dimensional classification via random projection with application to gene expression data. (Journal of Data Science, 2023) Link
Scientific Collaborations
[2] Harris C., Chakraborty A., Mather K., Haupt A., Calle R.A., De Oliveira C.V, A phase 2 study of Tirzepatide plus Mibavademab compared with Tirzepatide alone in adult participants with obesity. (Obesity Week, 2026).
[1] Chakraborty A., Chakraborty A., Hierarchical transit compartment model to describe absorption delay in orally administered drugs. (Therapeutic Innovation and Regulatory Sciences, Springer, 2025) Link