Moulinath Banerjee

Moulinath Banerjee was born and raised in India where he completed both his Bachelors and Masters in Statistics at the Indian Statistical Institute, Kolkata. He obtained his Ph.D. from the Statistics department at University of Washington, Seattle, in December 2000, served as lecturer there for Winter and Spring quarters, 2001, and joined University of Michigan in Fall 2001. Mouli's research interests are in the fields of non-standard problems, empirical process theory, threshold and boundary estimation, and graphical networks. His main contributions to date are in the areas of inference under shape-restrictions and investigation of non-differentiable models in low and high-dimensional settings. More recently, he has been investigating problems at the Statistics-ML interface with emphasis on data integration in a broad sense. Mouli is the recipient of the 2011 IISA Young Investigators Award, a fellow of both the IMS (Institute of Mathematical Statistics) and ASA (American Statistical Association), an IMS Medallion Lecture awardee for 2024, and Statistical Science Editor, 2023-2025. He has a broad range of interests outside of statistics including classical music, literature, history, philosophy, physics and ancestral genetics. He is, also, most emphatically, a gourmet and believes that a life without good food is a life less lived.

Courses Taught

• Statistics 412, 425, 426
• Statistics 610, 611, 612

Research Areas(s)

• Likelihood–based methods, Nonstandard problems
• Empirical Processes
• Shape–restricted estimation
• Threshold and boundary estimation problems
• Distributed Computing, Data Integration, Transfer Learning
• Statistics-ML interface