Modern data science and machine learning present new challenges that calls for rigorous theoretical frameworks to analyze complex, high-dimensional data and to ensure reliable inference from data driven methods. My research aims to devise innovative statistical methodologies that provide robust inferential guarantees, with applications in causal inference and contemporary data-science. My current research interests can be organized around the following themes:
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Explore randomization as a tool to facilitate flexible and tractable inference for a wide-range of data-driven methods
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Develop novel statistical frameworks to advance theoretical insight into the complexities arising in today’s AI & ML systems
Publications & Preprints
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Inference with Randomized Regression Trees
Authors: Soham Bakshi, Yiling Huang, Snigdha Panigrahi, Walter Dempsey
Year: 2024
Link: arXiv:2412.20535 -
Selective Inference for Time-varying Effect Moderation
Authors: Soham Bakshi, Walter Dempsey, Snigdha Panigrahi
Year: 2024
Link: arXiv:2412.20535 -
Bayes Classifier Cannot Be Learned from Noisy Responses with Unknown Noise Rates
Authors: Soham Bakshi, Subha Maity
ICLR 2023, Tiny Papers
Link: OpenReview -
How to Approximate Irrational Numbers Nicely?
Authors: Tirthankar Bhattacharyya, Soham Bakshi, Arka Das
Year: 2022
Link: arXiv:2206.12579 -
Set of Points of Continuity and Maximally Discontinuous Extensions
Author: Soham Bakshi
Journal: Resonance, Volume 27, Issue 1, Pages 131–142
Year: 2022
Link: Springer
Selected Talks
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Selective Inference for Time-Varying Moderated Effects (slides)
Statistical Analysis of Multi-Outcome Data (SAM) 2024, Salzburg, Austria -
Non-Identifiability of Bayes Classifier (slides)
Joint Statistical Meet (JSM) 2023, Torronto, Canada -
Geomnetry in Statistics (slides)
Math Club 2022, Indian Statistical Institute, Bangaluru Centre, India -
Hopf-Rinow’s theorem and Geodesic Completeness (slides)
Visiting Students Research Program (VSRP) 2021, School of Mathematics, TIFR, India