Snigdha Panigrahi
451 West Hall, Department of Statistics
University of Michigan
1085 South University, Ann Arbor, MI 48109.
email : psnigdha@umich.edu

I am an assistant professor of Statistics at the University of Michigan. I am primarily interested in developing methodology driven by scientific applications. My work spans across high dimensional inference, Bayesian methods, non-parametric statistics, random fields and causal inference. I completed my PhD thesis in 2018 from Stanford University under the supervision of Jonathan Taylor.


Publications and Preprints

Selective Inference

Snigdha Panigrahi and Jonathan Taylor. Scalable methods for Bayesian selective inference 2017. To appear in Electronic Journal of Statistics [arxiv] [code]

Snigdha Panigrahi, Jelena Markovic and Jonathan Taylor. An MCMC-free approach to post-selective inference. 2017. [arxiv] [code]

Xiaoying Tian Harris, Snigdha Panigrahi, Jelena Markovic, Nan Bi and Jonathan Taylor. Selective sampling after solving a convex problem. 2016. [arxiv]

Snigdha Panigrahi, Jonathan Taylor and Asaf Weinstein. Bayesian Post-Selection Inference in the Linear Model. 2016. [arxiv] [code]


Random Fields

Snigdha Panigrahi, Parthanil Roy and Yimin Xiao. Maximal Moments and Uniform Modulus of Continuity for Stable Random Fields. 2017. [arxiv]

Snigdha Panigrahi, Jonathan Taylor and Sreekar Vadlamani. Kinematic Formula for Heterogeneous Gaussian Related Fields. 2017. To appear in Stochastic Processes and their Applications [arxiv]


Machine Learning methods for Streaming Data

Snigdha Panigrahi and Nadia Fawaz. A relevance-scalability-interpretability tradeoff with temporally evolving user personas. 2017. [arxiv]


Statistical genomics

Snigdha Panigrahi, Jason Zhu and Chiara Sabbati. Selection-adjusted inference: an application to confidence intervals for cis-eQTL effect sizes. 2017. [arxiv]

Packages for Selective Inference

Bayesian selective inference [Github]

      This directory contains an MCMC sampler that provides Bayesian selective inference, that is based on a selection-adjusted posterior. The methods include implementation of inference (credible intervals and selection-adjusted MAP) post LASSO, forward stepwise regression, marginal screening, multi-stage screening algorithms.



MCMC free frequentist selective inference [Github]

      This directory contains code that provides selection-adjusted frequentist inference based on a a convex approximation for an intractable pivot. This also includes computation of the selective MLE that maximizes an approximate selection-adjusted law. Tools include inference post LASSO, marginal screening, forward stepwise regression and analysis of HIV data.

I will be teaching Stats 280, an Honors introductory course to Statistics and Data Analysis in the Fall, 2018 at the University of Michigan.

Select Talks and Presentations

A tutorial on recent advances in Selective Inference: Adaptive Data Analysis Workshop, Simons Institute for Theory of Computing, UC Berkeley. 2018

An approximation based approach for randomized conditional inference- with an application in eQTLs: Statistics Seminar, CMU Statistics and Data Science. 2018

An approximation based approach for randomized conditional inference- with an application in eQTLs: Statistics Seminar, University of Michigan. 2018

An approximation based approach for randomized conditional inference- with an application in eQTLs: Department of Biostatistics, Harvard T.H. Chan School of Public Health. 2018

An approximation based approach for randomized conditional inference- with an application in eQTLs: Statistics Seminar, Columbia University. 2018

Selection adjusted estimation of effect sizes with an application in eQTLs: Workshop in Operations Research and Data Science, The Fuqua School of Business, Duke University. 2017 [Conference]

Selection adjusted methods for effect size estimation in eQTLs: Statistics and genomics seminar, UC Berkeley. 2017 [Talk]

A Pseudo-likelihood approach to Selective Inference: Workshop on Higher-Order Asymptotics and Post-Selection Inference, Washington University, St. Louis. 2017 [Talk]

Bayesian Selective Inference in Linear Model: 10th International Conference on Multiple Comparison Procedures, UC Riverside. 2017 [Talk]

Bayesian Selective Inference in Linear Models: Workshop on Higher-Order Asymptotics and Post-Selection Inference, Washington University, St. Louis. 2016 [Poster]

Maximal Moments and Modulus of Continuity of Stable Random Fields: Extreme Value Analysis Conference, University of Michigan, Ann Arbor. 2015 [Talk]

Moments of Partial Maxima of Symmetric Stable Processes: Workshop on Heavy Tailed Distributions and Extreme Value Theory, Indian Statistical Institute, Kolkata. 2013 [Talk]