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| Position | Lecturer in Statistical Science |
| Phone (external) | |
| Phone (internal) | |
| Email(*) | p.chakravarti |
| Personal webpage | https://purvashac.github.io/ |
| Themes | Computational Statistics and Machine Learning, General Theory and Methodology, Environmental Statistics, Multivariate and High Dimensional Data; Statistical Methods in High-Energy Particle Physics |
* @ucl.ac.uk
Biographical Details
Purvasha Chakravarti has been a Lecturer (Assistant Professor) in Statistical Science since June 2022 at UCL. Previously, she was a Chapman Fellow in Mathematics in the Statistics Section of the Department of Mathematics at Imperial College London. She received a Ph.D. in Statistics from the Department of Statistics & Data Science at Carnegie Mellon University, under the supervision of Professor Larry Wasserman. She completed her Bachelors and Masters in Statistics from the Indian Statistical Institute Kolkata.
Research Interests
Purvasha's research focuses on developing scalable and interpretable machine learning methods, with statistical significance guarantees, to analyze high-dimensional data. Specifically, her current research includes:
1. Inference for Clustering: developing and analyzing, both theoretically and empirically, high-dimensional clustering algorithms with significance guarantees.
2. Signal Detection in Particle Physics: developing tests that can detect new signals in particle physics data sets in model-independent and model-dependent settings.
3. Interpretability of High-dimensional Classifiers: developing active subspace search to model the surface of the classifier, identify directions with the most variability, and identify relationships between
features that influence the classifier.
Selected publications
Purvasha's full publication list can be found at https://scholar.google.com/citations?hl=en&user=VHcPDQoAAAAJ.