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Computer Science

D-Index
32
Citations
4399
World Ranking
13167
National Ranking
5287

Yin Tat Lee publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Yin Tat Lee sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 164 publications — 32nd percentile

32% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Yin Tat Lee D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Yin Tat Lee sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 32 D-Index — 10th percentile

10% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

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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