Debarghya Mukherjee

Debarghya Mukherjee is an Assistant Professor at the Department of Mathematics and Statistics, Boston University, with a joint appointment in the College of Arts & Sciences and the Computational Data Science (CDS) program. His research focuses on high-dimensional statistics, statistical machine learning, and algorithmic fairness. He holds a Ph.D. from the University of Michigan and completed postdoctoral work at Princeton University under Prof. Jianqing Fan.

• Education:
  • Ph.D. in Statistics, University of Michigan (2017-2022)
  • M.Stat. and B.Stat., Indian Statistical Institute, Kolkata (2012-2017)

His research explores the intersection of statistical theory and modern machine learning challenges, including fairness, domain adaptation, and non-standard asymptotics. He has contributed to methodologies for high-dimensional models, spatial/temporal data dependency, and optimal knowledge transfer across domains. His work emphasizes theoretical rigor and practical applicability in fields like economics and biology.

Recent publications address topics such as prediction interval aggregation under domain shifts, fairness-aware machine learning, and the theoretical foundations of deep neural networks. His teaching includes courses on generalized linear models and introductory statistics.

He has no explicitly listed scientific awards but maintains active collaborations with leading institutions and researchers in statistics and machine learning.