About Me
I am an Associate Professor of Statistics at North Carolina State University, where I have been working since Aug 2020. I received my Ph.D. in Statistics from the University of Illinois at Urbana-Champaign in Jul 2016. From Aug 2016 to Aug 2020, I was a tenure-track Assistant Professor of Statistics at Virginia Tech.
The primary theme of my research is developing formal inferential algorithms for network data and applying such algorithms to epidemiology, social sciences, and environmental health. I am also working on developing a statistical science of patient safety, focusing on adverse medical events due to human errors, medical devices, drug reactions, and radiation therapy. See my CV below for more on my work and background.
Email: ssengup2 ''at'' ncsu.edu
Google Scholar: https://scholar.google.com/citations?user=MXM2IiUAAAAJ
Twitter: @SrijanSengupta7
CV_Mar2024.pdfRecent news and updates
Upcoming talk (02/29) on core-periphery inference in networks at the Indian Statistical Institute, Kolkata.
Upcoming talk (03/15) on core-periphery inference in networks at the Department of Mathematics, Indian Institute of Technology Bombay.
Upcoming talk (03/16) on core-periphery inference in networks at the IEOR Foundation Day at Indian Institute of Technology Bombay.
Congratulations to Eric Yanchenko for successfully defending his PhD dissertation! Eric will start an Assistant Professor position (tenure-track) at Akita International University in Akita, Japan, in April 2024 after a short post-doctoral stint at NC State.
Our work on a hypothesis test for community structure in networks has now been published in Network Science. Researchers theorize that many real-world networks exhibit community structure where within-community edges are more likely than between-community edges. While numerous methods exist to cluster nodes into different communities, less work has addressed this question: given some network, does it exhibit statistically meaningful community structure? We answer this question in a principled manner by framing it as a statistical hypothesis test in terms of a general and model-agnostic community structure parameter. Leveraging this parameter, we propose a simple and interpretable test statistic used to formulate two separate hypothesis testing frameworks. The first is an asymptotic test against a baseline value of the parameter while the second tests against a baseline model using bootstrap-based thresholds. We prove theoretical properties of these tests and demonstrate how the proposed method yields rich insights into real-world datasets.
Our work on scalable community detection has now been published in Statistica Sinica. This is joint work with Yuguo Chen and our co-advised Ph.D. student, Sayan Chakrabarty. This paper is part of a broader research project on scalable network inference.
We received a one-year ($250k) computing supplement from the NIH for our R01 grant on learning gut-brain interactions. This supplement will help us set up an innovative computing framework for next-generation statistical algorithms related to biomedical data.