• Two papers are accepted at NeurIPS 2020!
    (1) provably sample-efficient RL for learning $Q$-value that “overcomes” the classical lower bound via low-rank matrix estimation.
    (2) improved learning with imbalanced data by studying the value of labels in semi- and self-supervised manners.
  • Work on harnessing the low-rank structure of $Q$-value for planning & deep RL is accepted as an oral presentation (1.8%) at ICLR 2020.
  • Paper on non-asymptotic analysis of monte carlo tree search appears in ACM Sigmetrics 2020; we prove that the correct bounus term should be polynomial rather than the classical logarithmic one.
  • Paper on private sequential learning is accepted at the journal Operations Research (preliminary version: COLT2018).


I am interested in both theoretical machine learning and modern deep learning applications.

Theory: novel mathematical models and theoretical guarantees for

  • reinforcement learning algorithms
  • private active learning

Applications: theory-inspired approaches in deep learning

  • deep reinforcement learning
  • adversarial robustness
  • class-imbalanced learning

Selected Publications

(Google Scholar; theoretical work: alphabetical order)

Journal Papers and Preprints

Conference Papers

  1. Previously circulated under the title “On Reinforcement Learning Using Monte Carlo Tree Search with Supervised Learning: Non-Asymptotic Analysis.” ^

Work Experience


Quantitative Trading Intern

Tower Research Capital

Jun 2019 – Aug 2019 New York

Quantitative Research Intern

Cubist Systematic Strategies, Point72 Asset Management

Jun 2018 – Aug 2018 New York


Over the years, I have TA’ed several graduate-level machine learning and optimization courses in the Department of Electrical Engineering and Computer Science at MIT.

6.867 Machine Learning (Fall 2017 & Fall 2018)
  • graduate-level introduction to the principles, techniques, and algorithms for modern machine learning.
6.251 Introduction to Mathematical Programming (Spring 2017)
  • graduate-level introduction to linear optimization and its extensions emphasizing both methodology and the underlying mathematical structures and geometrical ideas.
6.231 Dynamic Programming and Stochastic Control (Fall 2016)
  • graduate-level introduction to sequential decision-making via Markov decision processes