Soumendra Lahiri

Soumendra Lahiri is a Professor and Stanley A. Sawyer Professor in Mathematics and Statistics at Washington University in St. Louis's Department of Statistics and Data Science. He earned his PhD from Michigan State University in 1989 and has held faculty positions at Iowa State University, Texas A&M University, and North Carolina State University before joining WashU in 2019. His research spans theoretical and applied statistics with cross-disciplinary impact.

Lahiri's research integrates higher-order asymptotic theory, resampling methods, and high-dimensional inference. He develops novel statistical methodologies for complex data structures in neuroscience, astrophysics, and econometrics. His recent work focuses on bootstrap techniques for modern data challenges like network analysis and machine learning model uncertainty.

His publication portfolio shows a consistent focus on:

• Advancing resampling methods for dependent and high-dimensional data
• Developing theoretical guarantees for machine learning algorithms
• Creating inference tools for spatial, temporal, and network-structured data
• Bridging statistical theory with applications in natural and social sciences

Honors include the endowed Stanley A. Sawyer Professorship. He maintains an active research program with recent publications in statistical methodology and interdisciplinary applications, particularly in electoral modeling, network analysis, and extreme value theory.