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Pallavi Basu
Overview
I am an Assistant Professor in the Operations Management area, teaching concepts and approaches in Statistics at ISB. My research interests include applications of statistics in finance, marketing, and other disciplines, high-dimensional statistical inference, large-scale multiple testing, and topics on causal inference. I am a member of the American Statistical Association, Institute of Mathematical Statistics, and International Indian Statistical Association. I received my Ph.D. (Business Administration and Statistics) from USC Marshall School of Business. I was a post-doctoral fellow at Tel Aviv University. I completed my undergrad and master's studies in Statistics (specializing in mathematical statistics and probability) from the Indian Statistical Institute (ISI, Kolkata). The Mathematical Research Impact Centric Support from the SERB, Government of India, funds part of my current research (2023-2026).
Experience
Academic
- 2018 - Present: Assistant Professor, Indian School of Business
- 2023 - 2023: Visiting Researcher, Stanford GSB, Economics & Statistics
- 2018 - 2018: Visiting Researcher, Wharton Statistics and Data Science
Professional
- 2010 - 2011: Quant. Research Associate, Bank of America
- 2009 - 2010: Risk Analyst, McKinsey & Company
Research & Publications
Published Papers
Basu, Pallavi., Fu, Luella., Saretto, Alessio., Sun, Wenguang. (Accepted) "Empirical Bayes Control of the False Discovery Exceedance", Journal of Business and Economic Statistics [Ranked A* in Mathematical Sciences] Basu, Pallavi. (2022) "Comments on Assumption-lean inference for generalised linear model parameters", Journal of the Royal Statistical Society: Series B (Statistical Methodology) [Ranked A* in Mathematical Sciences] Demirkaya, Emre., Feng, Yang., Basu, Pallavi., Lv, Jinchi. (2021) "Large-scale model selection in misspecified generalized linear models", Biometrika [Ranked A* in Mathematical Sciences] Basu, Pallavi., Small, Dylan. (2020) "Constructing a More Closely Matched Control Group in a Difference-in-Differences Analysis: Its Effect on History Interacting with Group Bias", Observational Studies [New journal, currently IMS affiliated] Tao Feng., Basu, Pallavi., Wenguang Sun., Hsun Teresa Ku., Wendy J. Mack. (2019) "Optimal Design for High–Throughput Screening via False Discovery Rate Control", Statistics in Medicine [Ranked A in Mathematical Sciences] Basu, Pallavi., Cai T Tony., Das Kiranmoy., Sun Wenguang. (2018) "Weighted False Discovery Rate Control in Large- Scale Multiple Testing", Journal of the American Statistical Association, Theory and Methods [Ranked A* in Mathematical Sciences]