Srijan Sengupta

Srijan Sengupta is an Associate Professor of Statistics at North Carolina State University (NC State) since 2020. Previously, he served as an Assistant Professor at Virginia Tech from 2016 to 2020. He holds a Ph.D. in Statistics from the University of Illinois at Urbana-Champaign (2016) and degrees from the Indian Statistical Institute (B.Stat and M.Stat with Distinction). His research focuses on statistical methodology for network data, anomaly detection, bootstrap methods, and scalable inference, with applications in healthcare analytics, epidemiology, and cybersecurity.

Education:

• Ph.D. in Statistics, University of Illinois at Urbana-Champaign (2011–2016)
• M.Stat (1st Division with Distinction), Indian Statistical Institute (2007–2009)
• B.Stat (1st Division with Distinction), Indian Statistical Institute (2004–2007)

Research Interests:

His methodological work includes statistical inference in networks, anomaly detection, bootstrap techniques, and scalable algorithms for big data. Applications span social determinants of health, healthcare analytics, space physics, epidemiology, and cybersecurity. He emphasizes interdisciplinary collaborations, particularly in patient safety event analysis and medical device safety.

Awards and Grants:

• Norton Prize for Outstanding PhD Thesis (2015)
• NIH R01 Grant ($890,055, Principal Investigator) for statistical algorithms in patient safety (2019–2022)
• Multiple grants for network inference and anomaly detection (NSF, Socially Determined Inc., Virginia Tech Foundation)

Advising and Service:

Advises over 20 students across PhD, master’s, and undergraduate research programs. Serves as an Associate Editor for Sankhya, Series B and peer reviewer for top journals. Active in university service roles at NC State and Virginia Tech, including faculty hiring committees and curriculum development.

Labs and Collaborations:

Leads research on statistical network analysis, including projects funded by NIH and NSF. Collaborates with institutions globally on topics like epidemic thresholds, cybersecurity defenses (e.g., phishing detection), and healthcare analytics.