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Rong Ge 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 Rong Ge 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+

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

Rong Ge 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 Rong Ge 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+

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

Overview

Rong Ge is affiliated with Duke University in the United States, with a body of research spanning multiple areas within computer science and engineering. Their work primarily focuses on artificial intelligence, computational mechanics, and statistical methods, with significant contributions to multiple subfields and topics related to machine learning and optimization.

Their main fields of study include:

  • Computer Science
  • Engineering

The subfields of study Rong Ge has contributed to are:

  • Artificial Intelligence
  • Computational Mechanics
  • Statistics and Probability
  • Civil and Structural Engineering
  • Biomedical Engineering

Main topics covered in their research include:

  • Sparse and Compressive Sensing Techniques
  • Stochastic Gradient Optimization Techniques
  • Statistical Methods and Inference
  • Markov Chains and Monte Carlo Methods
  • Optimization and Search Problems
  • Machine Learning and Algorithms
  • Machine Learning and Data Classification

Rong Ge's published papers reflect detailed investigations in these fields, including the following recent works:

  • "On Nonconvex Optimization for Machine Learning," 2021, Journal of the ACM
  • "Hydro-refining of coal-petroleum co-processing oil for potential clean jet fuels," 2022, Fuel
  • "Customizing ML Predictions for Online Algorithms," 2022, arXiv (Cornell University)
  • "Dissecting Hessian: Understanding Common Structure of Hessian in Neural Networks," 2020, arXiv (Cornell University)
  • "A Local Convergence Theory for Mildly Over-Parameterized Two-Layer Neural Network," 2021, arXiv (Cornell University)

The frequent publication venues where Rong Ge has shared research include:

  • arXiv (Cornell University)
  • Journal of the ACM
  • Fuel
  • Mathematical Programming
  • SSRN Electronic Journal

They have collaborated frequently with a number of researchers including:

  • Muthu Chidambaram
  • Yu Cheng
  • Ilias Diakonikolas
  • Chi Jin
  • Keerti Anand

Best Publications

  • Tensor decompositions for learning latent variable models

    Animashree Anandkumar;Rong Ge;Daniel Hsu;Sham M. Kakade

  • Escaping from saddle points: Online stochastic gradient for tensor decomposition

    Rong Ge;Furong Huang;Chi Jin;Yang Yuan

  • How to escape saddle points efficiently

    Chi Jin;Rong Ge;Praneeth Netrapalli;Sham M. Kakade

  • Generalization and Equilibrium in Generative Adversarial Nets (GANs)

    Sanjeev Arora;Rong Ge;Yingyu Liang;Tengyu Ma

  • Matrix Completion has No Spurious Local Minimum

    Rong Ge;Jason D. Lee;Tengyu Ma

  • Learning Topic Models -- Going beyond SVD

    Sanjeev Arora;Rong Ge;Ankur Moitra

  • A Practical Algorithm for Topic Modeling with Provable Guarantees

    Sanjeev Arora;Rong Ge;Yonatan Halpern;David Mimno

  • Stronger generalization bounds for deep nets via a compression approach

    Sanjeev Arora;Rong Ge;Behnam Neyshabur;Yi Zhang

  • Provable Bounds for Learning Some Deep Representations

    Sanjeev Arora;Aditya Bhaskara;Rong Ge;Tengyu Ma

  • Computing a nonnegative matrix factorization -- provably

    Sanjeev Arora;Rong Ge;Ravindran Kannan;Ankur Moitra

  • No spurious local minima in nonconvex low rank problems: a unified geometric analysis

    Rong Ge;Chi Jin;Yi Zheng

  • Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator.

    Maryam Fazel;Rong Ge;Sham M. Kakade;Mehran Mesbahi

  • New algorithms for learning in presence of errors

    Sanjeev Arora;Rong Ge

  • A Tensor Spectral Approach to Learning Mixed Membership Community Models

    Animashree Anandkumar;Rong Ge;Daniel J. Hsu;Sham M. Kakade

  • New Algorithms for Learning Incoherent and Overcomplete Dictionaries

    Sanjeev Arora;Rong Ge;Ankur Moitra

  • A tensor approach to learning mixed membership community models

    Animashree Anandkumar;Rong Ge;Daniel Hsu;Sham M. Kakade

  • Simple, Efficient, and Neural Algorithms for Sparse Coding

    Sanjeev Arora;Rong Ge;Tengyu Ma;Ankur Moitra

  • Guaranteed Non-Orthogonal Tensor Decomposition via Alternating Rank-$1$ Updates

    Animashree Anandkumar;Rong Ge;Majid Janzamin

  • Computational complexity and information asymmetry in financial products

    Sanjeev Arora;Boaz Barak;Markus Brunnermeier;Rong Ge

  • Learning one-hidden-layer neural networks with landscape design

    Rong Ge;Jason D. Lee;Tengyu Ma

Frequent Co-Authors

Sanjeev Arora
Sanjeev Arora Princeton University
Sham M. Kakade
Sham M. Kakade Harvard University
Tengyu Ma
Tengyu Ma Stanford University
Praneeth Netrapalli
Praneeth Netrapalli Google (United States)
Michael I. Jordan
Michael I. Jordan University of California, Berkeley
Anima Anandkumar
Anima Anandkumar Nvidia (United Kingdom)
Daniel Hsu
Daniel Hsu Columbia University
Jason D. Lee
Jason D. Lee Princeton University
Aaron Sidford
Aaron Sidford Stanford University

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