Preprints

• Proximal Point Reductions in Sampling and Algorithms for Logconcave Finite Sums
  with Yin Tat Lee and Ruoqi Shen
  Manuscript, 2020.
• Semi-Streaming Bipartite Matching in Fewer Passes and Less Space
  with Yujia Jin and Aaron Sidford
  Manuscript, 2020.
• Relative Lipschitzness in Extragradient Methods and a Direct Recipe for Acceleration
  with Michael B. Cohen and Aaron Sidford
  Manuscript, 2020.
• Well-Conditioned Methods for Ill-Conditioned Systems: Linear Regression with Semi-Random Noise
  with Jerry Li, Aaron Sidford, and Huishuai Zhang
  arXiv preprint, 2020.
• Robust Sub-Gaussian Principal Component Analysis and Width-Independent Schatten Packing
  with Arun Jambulapati and Jerry Li
  arXiv preprint, 2020.
• Composite Logconcave Sampling with a Restricted Gaussian Oracle
  with Yin Tat Lee and Ruoqi Shen
  arXiv preprint, 2020.
• Acceleration with a Ball Optimization Oracle
  with Yair Carmon, Arun Jambulapati, Qijia Jiang, Yujia Jin, Yin Tat Lee, and Aaron Sidford
  arXiv preprint, 2020.

Publications

• Coordinate Methods for Matrix Games
  with Yair Carmon, Yujia Jin, and Aaron Sidford
  Foundations of Computer Science (FOCS), 2020.
• Logsmooth Gradient Concentration and Tighter Runtimes for Metropolized Hamiltonian Monte Carlo
  with Yin Tat Lee and Ruoqi Shen
  Conference on Learning Theory (COLT), 2020.
• Positive Semidefinite Programming: Mixed, Parallel, and Width-Independent
  with Arun Jambulapati, Yin Tat Lee, Jerry Li, and Swati Padmanabhan
  Symposium on Theory of Computing (STOC), 2020.
• Variance Reduction for Matrix Games
  with Yair Carmon, Yujia Jin, and Aaron Sidford
  Neural Information Processing Systems (NeurIPS), 2019. Oral presentation (top 0.5% of submissions).
• A Direct Õ(1/ϵ) Iteration Parallel Algorithm for Optimal Transport
  with Arun Jambulapati and Aaron Sidford
  Neural Information Processing Systems (NeurIPS), 2019.
• A Rank-1 Sketch for Matrix Multiplicative Weights
  with Yair Carmon, John C. Duchi, and Aaron Sidford
  Conference on Learning Theory (COLT), 2019.
• Coordinate Methods for Accelerating ℓ∞ Regression and Faster Approximate Maximum Flow
  with Aaron Sidford
  Foundations of Computer Science (FOCS), 2018. Invited to the FOCS SICOMP Special Issue.
• CoVeR: Learning Covariate-Specific Vector Representations with Tensor Decompositions
  with Teng Zhang and James Zou
  International Conference on Machine Learning (ICML), 2018.
• Learning Populations of Parameters
  with Weihao Kong and Gregory Valiant
  Neural Information Processing Systems (NeurIPS), 2017. Here is a humorous informational video about the paper.

Earlier published work

• A novel k-mer set memory (KSM) motif representation improves regulatory variant prediction
  with Yuchun Guo, Haoyang Zeng, Xiaoyun Guo, and David K. Gifford
  Genome Research, 2018.
• Predicting gene expression in massively parallel reporter assays: a comparative study
  with Anat Kreimer, Haoyang Zeng, et al.
  Human Mutation, 2017.
• K-mer Set Memory (KSM) Motif Representation Enables Accurate Prediction of the Impact of Regulatory Variants
  with Yuchun Guo, Haoyang Zeng, and David K. Gifford
  International Conference on Research in Computational Molecular Biology (RECOMB), 2017.
• On the Power Dominating Sets of Hypercubes
  with Nathaniel Dean, Alexandra Ilic, Ignacio Ramirez, and Jian Shen
  International Conference on Computational Science and Engineering (CSE), 2011.

Teaching and professional service

At Stanford, I was a graduate teaching assistant for MS&E 213/CS 269O: Introduction to Optimization Theory and MS&E 313/CS 269G: Almost Linear Time Graph Algorithms. At MIT, I was a teaching assistant for 6.006: Introduction to Algorithms. In the 2018-19 school year, I ran the Stanford Optimization for Algorithm Design reading group. I was the Stanford Theory Lunch organizer for Spring 2020.

Reviewer for: STOC 2019, COLT 2019, STOC 2020, ICALP 2020, ICML 2020, NeurIPS 2020, SODA 2021

Outreach

I am passionate about outreach, both educational and for diversity and community service purposes, and am fortunate to have been a member of many awesome groups, which have taught me a lot and helped shape my perspectives. At Stanford, I am a member of the Algorithms and Friends initiative, and previously was a research instructor for AI4ALL (formerly SAILORS), and a teacher for Stanford Splash. At MIT, I was a teacher for CETI and MIT Splash, a counselor for Camp Kesem and the Freshman Arts Program, an orientation leader, and Brotherhood Development Director of Zeta Beta Tau fraternity. In high school, I attended the Honors Summer Math Camp, which was my first foray into the world of research.