Snigdha Panigrahi

Snigdha Panigrahi is an Assistant Professor of Statistics at the University of Michigan, with a courtesy appointment in the Department of Biostatistics. She specializes in developing methods for replicable learning from complex data, focusing on quantifying uncertainties in machine learning outputs. Her work bridges selective inference, data aggregation, and non-parametric statistics, supported by NSF and NIH grants. Notable awards include the NSF CAREER Award (2023) and the Bernoulli Society’s New Researcher Award (2025).

Education: Ph.D. in Statistics from Stanford University (2013-2018), Master’s and Bachelor’s in Statistics from Indian Statistical Institute (2008-2013). Research interests include selective inference methodologies, distribution-free inferences, and scalable statistical techniques for high-dimensional data.

Her research applications span genomics, neuroimaging, and health trials. Key contributions include methods for post-selection inference in graphical models, distributed data analysis, and quantile regression. Current efforts address uncertainties in mobile health trials and malaria serology collaborations with NIH.

Awards: Recognitions include the NSF CAREER Award, Bernoulli Society Award, and election to the International Statistical Institute (2021). She serves on editorial boards for Bernoulli, Journal of Computational and Graphical Statistics, and Journal of the Royal Statistical Society: Series B.

Advising: Supervises PhD students including Judy Wu and Soham Bakshi, with over 20+ mentored undergraduates and master’s students. Leads a research team focused on adaptive data analysis and selective inference.

Grants: Includes NSF DMS-2337882 ($450K) for uncertainty quantification in ML and NIH R01 ($1.2M) for moderation analysis in micro-randomized trials. Additional funding from NSF and institutional grants.