I am an Applied Mathematics PhD student at MIT, supervised by Prof. Philippe Rigollet. Prior to MIT I obtained a Master’s in Mathematics and Statistics from the University of Oxford. My recent work has focused on the theory of likelihood-free inference.
Kernel-Based Tests for Likelihood-Free Hypothesis Testing arXiv:2308.09043 (2023)
Minimax optimal testing by classification COLT (2023)
Likelihood-free hypothesis testing arXiv:2211.01126 (2022)
Fisher information lower bounds for sampling ALT (2022)
The query complexity of sampling from strongly log-concave distributions in one dimension COLT (2022)
Rejection sampling from shape-constrained distributions in sublinear time AISTATS (2022)
Gaussian discrepancy: a probabilistic relaxation of vector balancing Discrete Applied Mathematics (2022)
Averaging on the Bures-Wasserstein manifold: dimension-free convergence of gradient descent NeurIPS, Spotlight (2021)
- 18.821 — Mathematics Project Laboratory (2023 Fall)
- 18.650 — Fundamentals of Statistics (2022 Fall, 2023 Spring)
- 18.675/9.521/IDS.160 — Mathematical Statistics - A non-asymptotic approach (2022 Spring)
- 15.070J/6.265J — Discrete Probability and Stochastic Processes (2021 Spring)
- 18.675 — Theory of Probability (2020 Fall)
Academic mentor at √mathroots (2020, 2022)
√mathroots is a mathematical talent accelerator summer program for high-potential high school students from underrepresented backgrounds or underserved communities.