Welcome! I am an Assistant Professor in the Department of Statistics and Actuarial Science at the University of Waterloo. I received my PhD in Statistics from the University of Michigan, where I was fortunate to be advised by Prof. Yuekai Sun and Prof. Moulinath Banerjee. Before that, I completed my undergraduate and master’s studies at the Indian Statistical Institute, Kolkata, with a focus on mathematics and statistics.
My research lies at the intersection of statistics and machine learning, with a focus on the statistical foundations of modern machine learning methods. Currently, I am interested in developing and analyzing statistical models and algorithms for transfer learning and learning under distribution shift.
Some recent work in this area includes:
Chakraborty, A., & Maity, S. (2026). The Statistical Cost of Adaptation in Multi-Source Transfer Learning. [preprint]
Cheng, M., Maity, S., Tian, Q., & Li, P. (2025). Transfer Learning under Group-Label Shift: A Semiparametric Exponential Tilting Approach. [preprint]
My previous research has spanned a variety of topics in machine learning, including algorithmic fairness, routing and weak-to-strong generalization in large language models, and related areas.
To learn more about my research interests, please visit the RESEARCH and PUBLICATIONS pages. You can also find information about my teaching, CV, and contact details throughout this site.
Prospective Trainees: I am currently seeking an MMath student to begin in Fall 2026, with an interest in conducting research as part of their Masters research paper and the possibility of continuing into our PhD program. I am also planning to recruit a PhD student starting in Fall 2027. If you are interested in working with me, please get in touch to discuss potential opportunities.
Get in touch: Email: ‘‘smaity at uwaterloo dot ca’’; Office: M3-4227, University of Waterloo