Team

• Anming Gu, Ph.D. student.
• Syamantak Kumar, Ph.D. student, co-advised with Purnamrita Sarkar.
• Chutong Yang, Ph.D. student.
• Yusong Zhu, Ph.D. student, co-advised with Eric Price.
• Jonathan Li, M.S. alumnus.
• Jennifer Mickel, undergraduate alumnus.
• Saloni Modi, undergraduate alumnus.

Teaching

• CS 395T: Continuous Algorithms (Spring 2025)
• CS 331: Algorithms and Complexity (Fall 2024)
• CS 395T: Continuous Algorithms (Spring 2024)

Thesis

• Iterative Methods for Structured Algorithmic Data Science
  Stanford University, 2022.
  Arthur Samuel Award for Best Doctoral Thesis in Computer Science.

Preprints

• The Power of Iterative Filtering for Supervised Learning with (Heavy) Contamination
  with Adam R. Klivans, Konstantinos Stavrapoulos, and Arsen Vasilyan
  arXiv preprint, 2025.
• Private Geometric Median in Nearly-Linear Time
  with Syamantak Kumar, Daogao Liu, and Chutong Yang
  arXiv preprint, 2025.
• Radial Isotropic Position via an Implicit Newton's Method
  with Arun Jambulapati and Jonathan Li
  arXiv preprint, 2025.
• More of the Same: Persistent Representational Harms Under Increased Representation
  with Jennifer Mickel, Maria De-Arteaga, and Leqi Liu
  arXiv preprint, 2025.

Publications

• Spike-and-Slab Posterior Sampling in High Dimensions
  with Syamantak Kumar, Purnamrita Sarkar, and Yusong Zhu
  Conference on Learning Theory (COLT), 2025.
• Omnipredicting Single-Index Models with Multi-Index Models
  with Lunjia Hu and Chutong Yang
  Symposium on Theory of Computing (STOC), 2025.
• Eulerian Graph Sparsification by Effective Resistance Decomposition
  with Arun Jambulapati, Sushant Sachdeva, Aaron Sidford, and Yibin Zhao
  Symposium on Discrete Algorithms (SODA), 2025.
• Learning Noisy Halfspaces with a Margin: Massart is No Harder than Random
  with Gautam Chandrasekaran, Vasilis Kontonis, and Konstantinos Stavropoulos
  Neural Information Processing Systems (NeurIPS), 2024.
  Spotlight presentation (top 2% of submissions).
• Semi-Random Matrix Completion via Flow-Based Adaptive Reweighting
  with Jonathan A. Kelner, Jerry Li, Allen Liu, and Aaron Sidford
  Neural Information Processing Systems (NeurIPS), 2024.
• Testing Calibration in Nearly-Linear Time
  with Lunjia Hu, Arun Jambulapati, and Chutong Yang
  Neural Information Processing Systems (NeurIPS), 2024.
• Private Stochastic Convex Optimization with Heavy Tails: Near-Optimality from Simple Reductions
  with Hilal Asi and Daogao Liu
  Neural Information Processing Systems (NeurIPS), 2024.
• Closing the Computational-Query Depth Gap in Parallel Stochastic Convex Optimization
  with Arun Jambulapati and Aaron Sidford
  Conference on Learning Theory (COLT), 2024.
• Black-Box k-to-1 PCA Reductions: Theory and Applications
  with Arun Jambulapati, Syamantak Kumar, Jerry Li, Shourya Pandey, and Ankit Pensia
  Conference on Learning Theory (COLT), 2024.
• Linear-Sized Sparsifiers via Near-Linear Time Discrepancy Theory
  with Arun Jambulapati and Victor Reis
  Symposium on Discrete Algorithms (SODA), 2024.
• A CS guide to the quantum singular value transformation
  with Ewin Tang
  Symposium on Simplicity in Algorithms (SOSA), 2024.
• Revisiting Area Convexity: Faster Box-Simplex Games and Spectrahedral Generalizations
  with Arun Jambulapati
  Neural Information Processing Systems (NeurIPS), 2023.
• Structured Semidefinite Programming for Recovering Structured Preconditioners
  with Arun Jambulapati, Jerry Li, Christopher Musco, Kirankumar Shiragur, and Aaron Sidford
  Neural Information Processing Systems (NeurIPS), 2023.
• Matrix Completion in Almost-Verification Time
  with Jonathan A. Kelner, Jerry Li, Allen Liu, and Aaron Sidford
  Foundations of Computer Science (FOCS), 2023.
• ReSQueing Parallel and Private Stochastic Convex Optimization
  with Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, and Aaron Sidford
  Foundations of Computer Science (FOCS), 2023.
• Algorithmic Aspects of the Log-Laplace Transform and a Non-Euclidean Proximal Sampler
  with Sivakanth Gopi, Yin Tat Lee, Daogao Liu, and Ruoqi Shen
  Conference on Learning Theory (COLT), 2023.
• Semi-Random Sparse Recovery in Nearly-Linear Time
  with Jonathan A. Kelner, Jerry Li, Allen Liu, and Aaron Sidford
  Conference on Learning Theory (COLT), 2023.
• Quadratic Speedups in Parallel Sampling from Determinantal Distributions
  with Nima Anari, Callum Burgess, and Thuy-Duong Vuong
  Symposium on Parallelism in Algorithms and Architectures (SPAA), 2023.
• Quantum Speedups for Zero-Sum Games via Improved Dynamic Gibbs Sampling
  with Adam Bouland, Yosheb Getachew, Yujia Jin, and Aaron Sidford
  International Conference on Machine Learning (ICML), 2023.
• Private Convex Optimization in General Norms
  with Sivakanth Gopi, Yin Tat Lee, Daogao Liu, and Ruoqi Shen
  Symposium on Discrete Algorithms (SODA), 2023.
• Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods
  with Yujia Jin and Aaron Sidford
  Conference on Learning Theory (COLT), 2022.
• Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching
  with Arun Jambulapati, Yujia Jin, and Aaron Sidford
  International Colloquium on Automata, Languages, and Programming (ICALP), 2022.
• Clustering Mixture Models in Almost-Linear Time via List-Decodable Mean Estimation
  with Ilias Diakonikolas, Daniel M. Kane, Daniel Kongsgaard, and Jerry Li
  Symposium on Theory of Computing (STOC), 2022.
• Semi-Streaming Bipartite Matching in Fewer Passes and Optimal Space
  with Sepehr Assadi, Arun Jambulapati, Yujia Jin and Aaron Sidford
  Symposium on Discrete Algorithms (SODA), 2022.
• Lower Bounds on Metropolized Sampling Methods for Well-Conditioned Distributions
  with Yin Tat Lee and Ruoqi Shen
  Neural Information Processing Systems (NeurIPS), 2021.
  Oral presentation (top 1% of submissions).
• List-Decodable Mean Estimation in Nearly-PCA Time
  with Ilias Diakonikolas, Daniel M. Kane, Daniel Kongsgaard, and Jerry Li
  Neural Information Processing Systems (NeurIPS), 2021.
  Spotlight presentation (top 3% of submissions).
• Robust Regression Revisited: Acceleration and Improved Estimation Rates
  with Arun Jambulapati, Jerry Li, and Tselil Schramm
  Neural Information Processing Systems (NeurIPS), 2021.
• Structured Logconcave Sampling with a Restricted Gaussian Oracle
  with Yin Tat Lee and Ruoqi Shen
  Conference on Learning Theory (COLT), 2021.
• Relative Lipschitzness in Extragradient Methods and a Direct Recipe for Acceleration
  with Michael B. Cohen and Aaron Sidford
  Innovations in Theoretical Computer Science (ITCS), 2021.
• Acceleration with a Ball Optimization Oracle
  with Yair Carmon, Arun Jambulapati, Qijia Jiang, Yujia Jin, Yin Tat Lee, and Aaron Sidford
  Neural Information Processing Systems (NeurIPS), 2020.
  Oral presentation (top 1% of submissions).
• Robust Sub-Gaussian Principal Component Analysis and Width-Independent Schatten Packing
  with Arun Jambulapati and Jerry Li
  Neural Information Processing Systems (NeurIPS), 2020.
  Spotlight presentation (top 3% of submissions).
• 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.
• 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.

Other

• Positive Semidefinite Programming: Mixed, Parallel, and Width-Independent
  with Arun Jambulapati, Yin Tat Lee, Jerry Li, and Swati Padmanabhan
  Manuscript.
  A preliminary version was presented at Symposium on Theory of Computing (STOC), 2020. The authors later discovered an error in Section 5, which we currently do not know how to fix.

Professional service

At Stanford, I was a graduate teaching assistant for MS&E 213/CS 269O: Introduction to Optimization Theory, MS&E 313/CS 269G: Almost Linear Time Graph Algorithms, and CS 168: The Modern Algorithmic Toolbox. At MIT, I was a teaching assistant for 6.006: Introduction to Algorithms. In 2018-19, I ran the Stanford Optimization for Algorithm Design reading group. I was the Stanford Theory Lunch organizer for Spring 2020. Together with Sinho Chewi, I co-organized the Complexity of Sampling reading group at the Simons Institute in Fall 2021.

Program committee member: COLT 2023, STOC 2024, COLT 2024, ALT 2025, COLT 2025, ITCS 2026

Workshop organizer: SIAM Conference on Optimization 2023, INFORMS Optimization Society Conference 2024

Conference reviewer: STOC, COLT, ICALP, ICML, NeurIPS, SODA, STACS, FOCS, SOSA, ITCS

Journal reviewer: Bernoulli, Mathematical Programming, Journal of Machine Learning Research, Transactions on Information Theory, SIAM Journal on Optimization, Journal of the ACM, The Computer Journal, Journal of Optimization Theory and Applications, Transactions on Quantum Computing, Probability Theory and Related Fields, Information and Computation, Mathematics of Operations Research, Annals of Statistics, Applied Mathematics and Optimization

Outreach

I am passionate about STEM education, diversity, and outreach, and am fortunate to have participated in many awesome groups, which have taught me a lot and shaped my perspectives. At Stanford, I was an organizer of Algorithms and Friends, a mentor for CURIS, a research instructor for AI4ALL, 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, Brotherhood Development Director of Zeta Beta Tau fraternity, and a coach for the Texas ARML team. In high school, I attended and was a counselor for the Honors Summer Math Camp, my first foray into the world of research.

Fun

I enjoy dabbling in guitar and piano (and various quirkier instruments), playing word games, running and biking (slowly), and watching semi-educational YouTube videos. I am an avid fan of the San Antonio Spurs, Philadelphia Eagles, and Texas Longhorns. I am from beautiful Austin, Texas, home of the world's best barbecue. Due to George Qi's visionary diplomacy, I am also a lord.

Like my great-grand-advisor, I have a favorite inequality.