World's Best Scientists 2026 revealed!
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Rising Stars
2025

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Rising Stars

D-Index
41
Citations
6156
World Ranking
613
National Ranking
91

Computer Science

D-Index
32
Citations
4399
World Ranking
13170
National Ranking
5288

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Yin Tat Lee is affiliated with Microsoft in the United States. Their research spans several key areas within computer science and mathematics, focusing on complexity and algorithms, optimization techniques, and data privacy.

The scientist's main fields of study include Computer Science and Mathematics, with subfields concentrated in Artificial Intelligence, Computational Theory and Mathematics, Statistics and Probability, Numerical Analysis, and Computer Networks and Communications.

Their work frequently addresses topics such as:

  • Complexity and Algorithms in Graphs
  • Stochastic Gradient Optimization Techniques
  • Markov Chains and Monte Carlo Methods
  • Privacy-Preserving Technologies in Data
  • Advanced Optimization Algorithms Research
  • Machine Learning and Algorithms
  • Sparse and Compressive Sensing Techniques

Yin Tat Lee has published extensively, with a notable presence in venues such as arXiv (Cornell University), SIAM Journal on Computing, Journal of Privacy and Confidentiality, Journal of the ACM, and bioRxiv (Cold Spring Harbor Laboratory).

Selected recent papers include:

  • Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone, 2024, arXiv (Cornell University)
  • Solving Linear Programs in the Current Matrix Multiplication Time, 2021, Journal of the ACM
  • Numerical Composition of Differential Privacy, 2024, Journal of Privacy and Confidentiality
  • Network size and weights size for memorization with two-layers neural networks, 2020, arXiv (Cornell University)
  • Minimum Cost Flows, MDPs, and ℓ₁-Regression in Nearly Linear Time for Dense Instances, 2021, arXiv (Cornell University)

Their frequent collaborators include Ruoqi Shen, Sivakanth Gopi, Kevin Tian, Santosh Vempala, and Swati Padmanabhan, indicating regular partnerships with researchers active in related fields and topics.

Best Publications

  • Path Finding Methods for Linear Programming: Solving Linear Programs in Õ(vrank) Iterations and Faster Algorithms for Maximum Flow

    Yin Tat Lee;Aaron Sidford

  • Solving Linear Programs in the Current Matrix Multiplication Time

    Michael B. Cohen;Yin Tat Lee;Zhao Song

  • An Almost-Linear-Time Algorithm for Approximate Max Flow in Undirected Graphs, and its Multicommodity Generalizations

    Jonathan A. Kelner;Yin Tat Lee;Lorenzo Orecchia;Aaron Sidford

  • Efficient Accelerated Coordinate Descent Methods and Faster Algorithms for Solving Linear Systems

    Yin Tat Lee;Aaron Sidford

  • A Faster Cutting Plane Method and its Implications for Combinatorial and Convex Optimization

    Yin Tat Lee;Aaron Sidford;Sam Chiu-Wai Wong

  • Optimal algorithms for smooth and strongly convex distributed optimization in networks

    Kevin Seaman;Francis Bach;Sébastien Bubeck;Yin Tat Lee

  • Uniform Sampling for Matrix Approximation

    Michael B. Cohen;Yin Tat Lee;Cameron Musco;Christopher Musco

  • Solving linear programs in the current matrix multiplication time

    Michael B. Cohen;Yin Tat Lee;Zhao Song

  • Geometric median in nearly linear time

    Michael B. Cohen;Yin Tat Lee;Gary Miller;Jakub Pachocki

  • A geometric alternative to Nesterov's accelerated gradient descent

    Sébastien Bubeck;Yin Tat Lee;Mohit Singh

  • Efficient Inverse Maintenance and Faster Algorithms for Linear Programming

    Yin Tat Lee;Aaron Sidford

  • Single Pass Spectral Sparsification In Dynamic Streams

    Michael Kapralov;Yin Tat Lee;Cameron Musco;Christopher Musco

  • Sparsified Cholesky and multigrid solvers for connection laplacians

    Rasmus Kyng;Yin Tat Lee;Richard Peng;Sushant Sachdeva

  • Kernel-based methods for bandit convex optimization

    Sébastien Bubeck;Yin Tat Lee;Ronen Eldan

  • A new approach to computing maximum flows using electrical flows

    Yin Tat Lee;Satish Rao;Nikhil Srivastava

  • Optimal Algorithms for Non-Smooth Distributed Optimization in Networks

    Kevin Scaman;Francis R. Bach;Sébastien Bubeck;Laurent Massoulié

  • Constructing Linear-Sized Spectral Sparsification in Almost-Linear Time

    Yin Tat Lee;He Sun

  • Optimal Algorithms for Non-Smooth Distributed Optimization in Networks

    Kevin Scaman;Francis Bach;Sébastien Bubeck;Yin Tat Lee

  • Improved Cheeger's inequality: analysis of spectral partitioning algorithms through higher order spectral gap

    Tsz Chiu Kwok;Lap Chi Lau;Yin Tat Lee;Shayan Oveis Gharan

  • An SDP-based algorithm for linear-sized spectral sparsification

    Yin Tat Lee;He Sun

  • Eldan's Stochastic Localization and the KLS Hyperplane Conjecture: An Improved Lower Bound for Expansion

    Yin Tat Lee;Santosh Srinivas Vempala

  • Convergence rate of Riemannian Hamiltonian Monte Carlo and faster polytope volume computation

    Yin Tat Lee;Santosh S. Vempala

  • Solving tall dense linear programs in nearly linear time

    Jan van den Brand;Yin Tat Lee;Aaron Sidford;Zhao Song

  • A Faster Interior Point Method for Semidefinite Programming

    Haotian Jiang;Tarun Kathuria;Yin Tat Lee;Swati Padmanabhan

  • Minimum cost flows, MDPs, and ℓ1-regression in nearly linear time for dense instances

    Jan van den Brand;Yin Tat Lee;Yang P. Liu;Thatchaphol Saranurak

  • Single Pass Spectral Sparsification in Dynamic Streams

    Michael Kapralov;Yin Tat Lee;Cameron Musco;Christopher Musco

  • Near-optimal method for highly smooth convex optimization

    Sébastien Bubeck;Qijia Jiang;Yin Tat Lee;Yuanzhi Li

Frequent Co-Authors

Aaron Sidford
Aaron Sidford Stanford University
Sébastien Bubeck
Sébastien Bubeck Microsoft (United States)
Santosh Vempala
Santosh Vempala Georgia Institute of Technology
Zhao Song
Zhao Song Adobe Systems (United States)
Yuanzhi Li
Yuanzhi Li Carnegie Mellon University
Richard Peng
Richard Peng Carnegie Mellon University
Laurent Massoulié
Laurent Massoulié French Institute for Research in Computer Science and Automation - INRIA
Francis Bach
Francis Bach École Normale Supérieure
James R. Lee
James R. Lee University of Washington
Daniel A. Spielman
Daniel A. Spielman Yale University

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