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Rising Stars
2025

D-Index & Metrics

Rising Stars

D-Index
42
Citations
6210
World Ranking
570
National Ranking
84

Aaron Sidford publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Aaron Sidford sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

The last bar groups every scientist with 804 publications or more.

Aaron Sidford D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Aaron Sidford sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

The last bar groups every scientist with 107 D-Index or more.

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Aaron Sidford is a researcher affiliated with Stanford University in the United States, specializing in computer science. Their work encompasses multiple subfields within computer science, with a strong focus on computational theory and mathematics as well as artificial intelligence. Other notable subfields in their research portfolio include computational mechanics, computer networks and communications, and statistics and probability.

Their research topics cover a range of areas, highlighting complexity and algorithms in graphs, sparse and compressive sensing techniques, stochastic gradient optimization techniques, and optimization and search problems. Additional topics include advanced graph theory research, Markov chains and Monte Carlo methods, and advanced optimization algorithms research.

Aaron Sidford has authored numerous academic papers published primarily in venues that include arXiv (Cornell University), Leibniz-Zentrum für Informatik (Schloss Dagstuhl), SIAM Journal on Computing, Operations Research Letters, and Theory of Computing. The distribution of their publications across these venues indicates active engagement with both preprint archives and specialized academic journals.

  • Towards optimal running times for optimal transport, 2023, Operations Research Letters
  • Large-Scale Methods for Distributionally Robust Optimization, 2020, arXiv (Cornell University)
  • Fully-Dynamic Graph Sparsifiers Against an Adaptive Adversary, 2022, arXiv (Cornell University)
  • Relative Lipschitzness in Extragradient Methods and a Direct Recipe for Acceleration, 2021, Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
  • Minimum Cost Flows, MDPs, and ℓ₁-Regression in Nearly Linear Time for Dense Instances, 2021, arXiv (Cornell University)

Their frequent co-authors include Arun Jambulapati, Jan van den Brand, Yang P. Liu, Yujia Jin, and Kevin Tian. Collaboration with these researchers appears extensive, reflecting joint efforts on topics aligned with Sidford's research interests.

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

  • Accelerated Methods for Non-Convex Optimization

    Yair Carmon;John C. Duchi;Oliver Hinder;Aaron Sidford

  • 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

  • A simple, combinatorial algorithm for solving SDD systems in nearly-linear time

    Jonathan A. Kelner;Lorenzo Orecchia;Aaron Sidford;Zeyuan Allen Zhu

  • 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

  • Uniform Sampling for Matrix Approximation

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

  • Lower bounds for finding stationary points I

    Yair Carmon;John C. Duchi;Oliver Hinder;Aaron Sidford

  • Geometric median in nearly linear time

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

  • Efficient Inverse Maintenance and Faster Algorithms for Linear Programming

    Yin Tat Lee;Aaron Sidford

  • Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization

    Roy Frostig;Rong Ge;Sham Kakade;Aaron Sidford

  • Lower bounds for finding stationary points II: first-order methods

    Yair Carmon;John C. Duchi;Oliver Hinder;Aaron Sidford

  • Single Pass Spectral Sparsification In Dynamic Streams

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

  • Near-Optimal Time and Sample Complexities for Solving Markov Decision Processes with a Generative Model

    Aaron Sidford;Mengdi Wang;Xian Wu;Lin F. Yang

  • Streaming PCA: Matching matrix bernstein and near-optimal finite sample guarantees for oja's algorithm

    Prateek Jain;Chi Jin;Sham M. Kakade;Praneeth Netrapalli

  • Competing with the Empirical Risk Minimizer in a Single Pass

    Roy Frostig;Rong Ge;Sham M. Kakade;Aaron Sidford

  • Parallelizing Stochastic Gradient Descent for Least Squares Regression: Mini-batching, Averaging, and Model Misspecification

    Prateek Jain;Sham M. Kakade;Rahul Kidambi;Praneeth Netrapalli

  • Variance reduced value iteration and faster algorithms for solving Markov decision processes

    Aaron Sidford;Mengdi Wang;Xian Wu;Yinyu Ye

  • Accelerating Stochastic Gradient Descent for Least Squares Regression

    Prateek Jain;Sham M. Kakade;Rahul Kidambi;Praneeth Netrapalli

  • 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

Frequent Co-Authors

Yin Tat Lee
Yin Tat Lee Microsoft (United States)
John C. Duchi
John C. Duchi Stanford University
Zhao Song
Zhao Song Adobe Systems (United States)
Yinyu Ye
Yinyu Ye Stanford University
Yuanzhi Li
Yuanzhi Li Carnegie Mellon University
Shuzhong Zhang
Shuzhong Zhang University of Minnesota

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