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Rising Stars
2025

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Rising Stars

D-Index
39
Citations
7443
World Ranking
685
National Ranking
104

Engineering and Technology

D-Index
34
Citations
6237
World Ranking
9156
National Ranking
2568

Simon S. Du 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 Simon S. Du 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+

This scientist: 124 publications — 17th percentile

17% of scientists in this discipline score the same or lower.

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

Simon S. Du 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 Simon S. Du 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+

This scientist: 34 D-Index — 7th percentile

7% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Simon S. Du is affiliated with the University of Washington in the United States. Their research contributions are primarily situated in the field of Computer Science, with substantial work in subfields such as Artificial Intelligence, Management Science and Operations Research, Computer Networks and Communications, Computational Mechanics, and Electrical and Electronic Engineering.

Their research spans several key topics, including:

  • Reinforcement Learning in Robotics
  • Advanced Bandit Algorithms Research
  • Machine Learning and Algorithms
  • Adversarial Robustness in Machine Learning
  • Domain Adaptation and Few-Shot Learning
  • Sparse and Compressive Sensing Techniques
  • Optimization and Search Problems

Simon S. Du has published extensively, with frequent contributions to venues such as:

  • arXiv (Cornell University) - 108 publications
  • Journal of the ACM - 2 publications
  • Mathematical Programming - 1 publication
  • Information Fusion - 1 publication
  • SSRN Electronic Journal - 1 publication

Some recent papers authored or co-authored by Simon S. Du include:

  • "Understanding the acceleration phenomenon via high-resolution differential equations," 2021, Mathematical Programming
  • "How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks," 2020, arXiv (Cornell University)
  • "Few-Shot Learning via Learning the Representation, Provably," 2020, arXiv (Cornell University)
  • "On Reward-Free Reinforcement Learning with Linear Function Approximation," 2020, arXiv (Cornell University)
  • "Is Reinforcement Learning More Difficult Than Bandits? A Near-optimal Algorithm Escaping the Curse of Horizon," 2020, arXiv (Cornell University)

The scientist has collaborated frequently with several co-authors, including:

  • Jason D. Lee
  • Kevin Jamieson
  • Qiwen Cui
  • Maryam Fazel
  • Ruosong Wang

Best Publications

  • Gradient Descent Provably Optimizes Over-parameterized Neural Networks

    Simon S. Du;Xiyu Zhai;Barnabas Poczos;Aarti Singh

  • Gradient descent finds global minima of deep neural networks

    Simon S. Du;Jason D. Lee;Haochuan Li;Liwei Wang

  • Fine-grained analysis of optimization and generalization for overparameterized two-layer neural networks

    Sanjeev Arora;Simon S. Du;Wei Hu;Zhiyuan Li

  • On Exact Computation with an Infinitely Wide Neural Net

    Sanjeev Arora;Simon S. Du;Wei Hu;Zhiyuan Li

  • Understanding the acceleration phenomenon via high-resolution differential equations

    Bin Shi;Simon S. Du;Michael I. Jordan;Weijie J. Su

  • On the Power of Over-parametrization in Neural Networks with Quadratic Activation

    Simon S. Du;Jason D. Lee

  • Gradient Descent Can Take Exponential Time to Escape Saddle Points

    Simon S. Du;Chi Jin;Jason D. Lee;Michael I. Jordan

  • Gradient Descent Learns One-hidden-layer CNN: Don’t be Afraid of Spurious Local Minima

    Simon S. Du;Jason D. Lee;Yuandong Tian;Barnabas Poczos

  • How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks

    Keyulu Xu;Mozhi Zhang;Jingling Li;Simon Shaolei Du

  • Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks

    Sanjeev Arora;Simon S. Du;Zhiyuan Li;Ruslan Salakhutdinov

  • Enhanced Convolutional Neural Tangent Kernels

    Zhiyuan Li;Ruosong Wang;Dingli Yu;Simon S. Du

  • Stochastic Variance Reduction Methods for Policy Evaluation

    Simon S. Du;Jianshu Chen;Lihong Li;Lin Xiao

  • What Can Neural Networks Reason About

    Keyulu Xu;Jingling Li;Mozhi Zhang;Simon S. Du

  • Algorithmic Regularization in Learning Deep Homogeneous Models: Layers are Automatically Balanced

    Simon S. Du;Wei Hu;Jason D. Lee

  • Few-Shot Learning via Learning the Representation, Provably

    Simon Shaolei Du;Wei Hu;Sham M. Kakade;Jason D. Lee

  • Gradient Descent Can Take Exponential Time to Escape Saddle Points

    Simon S. Du;Chi Jin;Jason D. Lee;Michael I. Jordan

  • Provably efficient RL with Rich Observations via Latent State Decoding

    Simon S. Du;Akshay Krishnamurthy;Nan Jiang;Alekh Agarwal

  • Computationally Efficient Robust Sparse Estimation in High Dimensions

    Sivaraman Balakrishnan;Simon S. Du;Jerry Li;Aarti Singh

  • Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels

    Simon S. Du;Kangcheng Hou;Russ R. Salakhutdinov;Barnabas Poczos

  • Linear Convergence of the Primal-Dual Gradient Method for Convex-Concave Saddle Point Problems without Strong Convexity

    Simon S. Du;Wei Hu

  • Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning

    Simon S. Du;Sham M. Kakade;Ruosong Wang;Lin F. Yang

  • On Reward-Free Reinforcement Learning with Linear Function Approximation

    Ruosong Wang;Simon S. Du;Lin F. Yang;Ruslan Salakhutdinov

Frequent Co-Authors

Jason D. Lee
Jason D. Lee Princeton University
Aarti Singh
Aarti Singh Carnegie Mellon University
Barnabás Póczos
Barnabás Póczos Carnegie Mellon University
Sanjeev Arora
Sanjeev Arora Princeton University
Ruslan Salakhutdinov
Ruslan Salakhutdinov Carnegie Mellon University
Sham M. Kakade
Sham M. Kakade Harvard University
Yuandong Tian
Yuandong Tian Facebook (United States)
Michael I. Jordan
Michael I. Jordan University of California, Berkeley
Jiajun Wu
Jiajun Wu Stanford University
Zhao Song
Zhao Song Adobe Systems (United States)

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