Hi! I'm a fourth-year graduate student in EECS at MIT. I'm very fortunate to be advised by Ankur Moitra. My current research interests are in computational statistics and reinforcement learning theory.
I am grateful to have been supported by an NDSEG Fellowship and an MIT Akamai Presidential Fellowship.
- Exploration is Harder than Prediction: Cryptographically Separating Reinforcement Learning from Supervised Learning
with Ankur Moitra and Noah Golowich
FOCS 2024 ♦ 65th Annual Symposium on Foundations of Computer Science (to appear).
- Lasso with Latents: Efficient Estimation, Covariate Rescaling, and Computational-Statistical Gaps
with Jonathan Kelner, Frederic Koehler, and Raghu Meka
COLT 2024 ♦ 37th Annual Conference on Learning Theory (to appear).
- Exploring and Learning in Sparse Linear MDPs without Computationally Intractable Oracles
with Ankur Moitra and Noah Golowich
STOC 2024 ♦ 56th Annual ACM Symposium on Theory of Computing (to appear).
- Provable Benefits of Score Matching
with Chirag Pabbaraju, Anish Sevekari, Holden Lee, Ankur Moitra, and Andrej Risteski
NeurIPS 2023 (Spotlight Presentation) ♦
37th Conference on Neural Information Processing Systems (to appear).
- Feature Adaptation for Sparse Linear Regression
with Jonathan Kelner, Frederic Koehler, and Raghu Meka
NeurIPS 2023 (Spotlight Presentation) ♦
37th Conference on Neural Information Processing Systems (to appear).
- Learning in Observable POMDPs, without Computationally Intractable Oracles
with Ankur Moitra and Noah Golowich
NeurIPS 2022 ♦ 36th Conference on Neural Information Processing Systems.
- Provably Auditing Ordinary Least Squares in Low Dimensions
with Ankur Moitra
ICLR 2023 ♦ 11th International Conference on Learning Representations.
- Distributional Hardness Against Preconditioned Lasso via Erasure-Robust Designs
with Jonathan Kelner, Frederic Koehler, and Raghu Meka
NeurIPS 2022 ♦ 36th Conference on Neural Information Processing Systems.
- Planning in Observable POMDPs in Quasipolynomial Time
with Ankur Moitra and Noah Golowich
STOC 2023 ♦ 55th Annual ACM Symposium on Theory of Computing.
- Robust Generalized Method of Moments: A Finite Sample Viewpoint
with Vasilis Syrgkanis
Preliminary version selected for Oral Presentation at MLECON Workshop (NeurIPS 2021)
NeurIPS 2022 ♦ 36th Conference on Neural Information Processing Systems.
- On the Power of Preconditioning in Sparse Linear Regression
with Jonathan Kelner, Frederic Koehler, and Raghu Meka
FOCS 2021 ♦ 62nd Annual IEEE Symposium on Foundations of Computer Science.
- Truncated Linear Regression in High Dimensions
with Costis Daskalakis and Manolis Zampetakis
NeurIPS 2020 ♦ 34th Conference on Neural Information Processing Systems.
- Constant-Expansion Suffices for Compressed Sensing with Generative Priors
with Costis Daskalakis and Manolis Zampetakis
NeurIPS 2020 (Spotlight Presentation) ♦
34th Conference on Neural Information Processing Systems.
- Regarding two conjectures on clique and biclique partitions
with John C. Urschel and Jake Wellens
The Electronic Journal of Combinatorics (2021).
- Near-Optimal Bounds for Online Caching with Machine-Learned Advice
SODA 2020 ♦ 31st ACM-SIAM Symposium on Discrete Algorithms.
- Conditional Hardness for Approximate Earth Mover Distance
APPROX 2019 ♦ 22nd Intl. Conference on Approximation Algorithms for Combinatorial Optimization Problems.
- Off-diagonal Ordered Ramsey Numbers of Matchings
The Electronic Journal of Combinatorics (2019).