World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
37
Citations
4945
World Ranking
10885
National Ranking
4526

Richard Peng 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 Richard Peng 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: 167 publications — 33rd percentile

33% of scientists in this discipline score the same or lower.

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

Richard Peng 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 Richard Peng 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: 37 D-Index — 27th percentile

27% of scientists in this discipline score the same or lower.

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

Overview

Richard Peng is affiliated with the University of Waterloo in Canada, specializing in the field of Computer Science. Their research primarily focuses on areas within computational theory and mathematics, with notable work spanning subfields such as Computational Theory and Mathematics, Artificial Intelligence, Computer Networks and Communications, Statistics and Probability, and Electrical and Electronic Engineering.

The scientist's research topics encompass:

  • Complexity and Algorithms in Graphs
  • Advanced Graph Theory Research
  • Optimization and Search Problems
  • Stochastic Gradient Optimization Techniques
  • Markov Chains and Monte Carlo Methods
  • Advanced Graph Neural Networks
  • Matrix Theory and Algorithms

Richard Peng has authored numerous papers, including recent works such as:

  • Maximum Flow and Minimum-Cost Flow in Almost-Linear Time, 2022, 2022 IEEE 63rd Annual Symposium on Foundations of Computer Science (FOCS)
  • MathChat: Converse to Tackle Challenging Math Problems with LLM Agents, 2023, arXiv (Cornell University)
  • Almost-Linear-Time Algorithms for Maximum Flow and Minimum-Cost Flow, 2023, Communications of the ACM
  • Association of Circulating Fibrocytes With Fibrostenotic Small Bowel Crohn's Disease, 2021, Inflammatory Bowel Diseases
  • Graph Sparsification, Spectral Sketches, and Faster Resistance Computation via Short Cycle Decompositions, 2020, SIAM Journal on Computing

Their frequent co-authors include:

  • Yang P. Liu
  • Sushant Sachdeva
  • Rasmus Kyng
  • Maximilian Probst Gutenberg
  • Jingbang Chen

Richard Peng's publications appear predominantly in venues such as:

  • arXiv (Cornell University)
  • Communications of the ACM
  • SIAM Journal on Computing
  • 2022 IEEE 63rd Annual Symposium on Foundations of Computer Science (FOCS)
  • Inflammatory Bowel Diseases

Best Publications

  • Approaching Optimality for Solving SDD Linear Systems

    Ioannis Koutis;Gary L. Miller;Richard Peng

  • A Nearly-m log n Time Solver for SDD Linear Systems

    Ioannis Koutis;Gary L. Miller;Richard Peng

  • Maximum Flow and Minimum-Cost Flow in Almost-Linear Time

    Unknown

  • Uniform Sampling for Matrix Approximation

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

  • Approaching Optimality for Solving SDD Linear Systems

    Ioannis Koutis;Gary L. Miller;Richard Peng

  • Solving SDD linear systems in nearly mlog1/2n time

    Michael B. Cohen;Rasmus Kyng;Gary L. Miller;Jakub W. Pachocki

  • Efficient Triangle Counting in Large Graphs via Degree-Based Vertex Partitioning

    Mihail N. Kolountzakis;Gary L. Miller;Richard Peng;Charalampos E. Tsourakakis

  • A nearly-mlogn time solver for SDD linear systems

    Ioannis Koutis;Gary L. Miller;Richard Peng

  • An efficient parallel solver for SDD linear systems

    Richard Peng;Daniel A. Spielman

  • Fully Dynamic (1+ e)-Approximate Matchings

    Manoj Gupta;Richard Peng

  • Scalable Large Near-Clique Detection in Large-Scale Networks via Sampling

    Michael Mitzenmacher;Jakub Pachocki;Richard Peng;Charalampos Tsourakakis

  • Sparsified Cholesky and multigrid solvers for connection laplacians

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

  • Parallel graph decompositions using random shifts

    Gary L. Miller;Richard Peng;Shen Chen Xu

  • Approaching optimality for solving SDD systems

    Ioannis Koutis;Gary L. Miller;Richard Peng

  • Iterative Row Sampling

    Mu Li;Gary L. Miller;Richard Peng

  • Partitioning Well-Clustered Graphs: Spectral Clustering Works!

    Richard Peng;He Sun;Luca Zanetti

  • Approximate undirected maximum flows in o(mpolylog(n)) time

    Richard Peng

  • Almost-linear-time algorithms for Markov chains and new spectral primitives for directed graphs

    Michael B. Cohen;Jonathan Kelner;John Peebles;Richard Peng

  • Lp Row Sampling by Lewis Weights

    Michael B. Cohen;Richard Peng

  • Improved Parallel Algorithms for Spanners and Hopsets

    Gary L. Miller;Richard Peng;Adrian Vladu;Shen Chen Xu

  • A Deterministic Algorithm for Balanced Cut with Applications to Dynamic Connectivity, Flows, and Beyond

    Julia Chuzhoy;Yu Gao;Jason Li;Danupon Nanongkai

  • Nearly-Linear Work Parallel SDD Solvers, Low-Diameter Decomposition, and Low-Stretch Subgraphs

    Guy E. Blelloch;Anupam Gupta;Ioannis Koutis;Gary L. Miller

  • Fully Dynamic $(1+psilon)$-Approximate Matchings

    Manoj Gupta;Richard Peng

Frequent Co-Authors

Gary L. Miller
Gary L. Miller Carnegie Mellon University
Aaron Sidford
Aaron Sidford Stanford University
Yin Tat Lee
Yin Tat Lee Microsoft (United States)
Daniel A. Spielman
Daniel A. Spielman Yale University
Santosh Vempala
Santosh Vempala Georgia Institute of Technology
Guy E. Blelloch
Guy E. Blelloch Carnegie Mellon University
Jie Tang
Jie Tang Tsinghua University
Danupon Nanongkai
Danupon Nanongkai Max Planck Institute for Informatics
Anupam Gupta
Anupam Gupta Carnegie Mellon University
Monika Henzinger
Monika Henzinger Institute of Science and Technology Austria

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