Current Research Interests

Transfer learning. Transfer learning improves prediction and inference in data-scarce target domains by borrowing information from related source domains. It has emerged as a central topic in machine learning and statistics over the past decade. My research focuses on developing and understanding the statistical performance of transfer-learning models and methods for a variety of statistical tasks. Selected examples of this work are listed below.

  • Chakraborty, A., & Maity, S. (2026). The Statistical Cost of Adaptation in Multi-Source Transfer Learning. (Submitted to JMLR) Preprint
  • Cheng, M., Maity, S., Tian, Q., & Li, P. (2025). Transfer Learning under Group-Label Shift: A Semiparametric Exponential Tilting Approach. (Major revision in Scandinavian Journal of Statistics) Preprint
  • Xu, M., Maity, S., & Dubin, J. (2025). Diagnosis-based mortality prediction for intensive care unit patients via transfer learning. (To be submitted to Canadian Journal of Statistics) Preprint
  • Maity, S., Dutta, D., Terhorst, J., Sun, Y., & Banerjee, M. (2024). A linear adjustment-based approach to posterior drift in transfer learning. Biometrika, 111(1), 31-50. Paper
  • Maity, S., Yurochkin, M., Banerjee, M., & Sun, Y. (2022). Understanding new tasks through the lens of training data via exponential tilting. ICLR. Paper
  • Maity, S., Sun, Y., & Banerjee, M. (2022). Minimax optimal approaches to the label shift problem in non-parametric settings. Journal of Machine Learning Research, 23(346), 1-45. Paper

Distribution shift. More broadly, I am interested in statistical methods for learning from data subject to distribution shift. Beyond transfer learning, this includes integrative analysis, domain adaptation, performative prediction, and algorithmic fairness. Selected works in these areas are listed below.

  • Somerstep, S., Ritov, Y. A., Yurochkin, M., Maity, S., & Sun, Y. (2025). Limitations of refinement methods for weak to strong generalization. COLM. Paper
  • Bracale, D., Maity, S., Polo, F. M., Somerstep, S., Banerjee, M., & Sun, Y. (2025). Microfoundation inference for strategic prediction. AISTAT. Paper
  • Maity, S., Mukherjee, D., Banerjee, M., & Sun, Y. (2022). Predictor-corrector algorithms for stochastic optimization under gradual distribution shift. ICLR. Paper
  • Ngweta, L., Maity, S., Gittens, A., Sun, Y., & Yurochkin, M. (2023). Simple disentanglement of style and content in visual representations. ICML. Paper
  • Maity, S., Sun, Y., & Banerjee, M. (2022). Meta-analysis of heterogeneous data: integrative sparse regression in high-dimensions. Journal of Machine Learning Research, 23(198), 1-50. Paper
  • Maity, S., Mukherjee, D., Yurochkin, M., & Sun, Y. (2021). Does enforcing fairness mitigate biases caused by subpopulation shift?. NeurIPS. Paper

Research Trainees

Past Research Trainees

  • Taoyue Chen, MMath Student