Bodhisattva Sen

Bodhisattva Sen is a Professor of Statistics at Columbia University, New York. His research focuses on nonparametric statistics, large sample theory, optimal transportation, and statistical applications in astronomy. He completed his Ph.D. in Statistics at the University of Michigan (2008) and holds degrees from the Indian Statistical Institute, Kolkata (B.Stat., M.Stat.). His work spans shape-constrained estimation, bootstrap inference, and interdisciplinary projects in astronomy. Sen’s research emphasizes distribution-free testing, high-dimensional models, and computational methods.

Education:

• Ph.D. in Statistics, University of Michigan, Ann Arbor (2008)
• M.Stat., Indian Statistical Institute, Kolkata
• B.Stat., Indian Statistical Institute, Kolkata

Research Interests:

• Nonparametric function estimation
• Optimal transport applications in statistics
• Empirical Bayes and multiple testing
• High-dimensional statistical inference
• Statistical methods in astronomy

Key Contributions:

• Developed multivariate distribution-free tests using optimal transport
• Advanced convex regression methods in multidimensions
• Contributed to nonparametric maximum likelihood estimation in mixture models
• Explored statistical applications in stellar abundance clustering

His work bridges theoretical statistics with practical applications, emphasizing robust and computationally efficient methods. Sen has also contributed to methodological advancements in astronomy through statistical modeling of stellar data.