World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
63
Citations
19682
World Ranking
2713
National Ranking
1348

Tom Goldstein 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 Tom Goldstein 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: 242 publications — 60th percentile

60% of scientists in this discipline score the same or lower.

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

Tom Goldstein 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 Tom Goldstein 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: 63 D-Index — 81st percentile

81% 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

  • 2017 - Fellow of Alfred P. Sloan Foundation

Overview

Tom Goldstein is affiliated with the University of Maryland, College Park in the United States. Their research primarily spans the field of Computer Science, with substantial contributions in Artificial Intelligence, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Signal Processing, and Computer Networks and Communications.

Within these fields, their work focuses on key topics including Adversarial Robustness in Machine Learning, Advanced Neural Network Applications, Anomaly Detection Techniques and Applications, Domain Adaptation and Few-Shot Learning, Topic Modeling, Natural Language Processing Techniques, and Machine Learning and Data Classification.

Goldstein has coauthored extensively with several researchers, including:

  • Micah Goldblum (81 coauthored works)
  • Jonas Geiping (50)
  • Liam Fowl (24)
  • Avi Schwarzschild (23)
  • John P. Dickerson (18)

Their frequent publication venues reflect their active engagement across prominent platforms:

  • arXiv (Cornell University) with 163 publications
  • Proceedings of the AAAI Conference on Artificial Intelligence with 4 publications
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) with 2 publications
  • Repository for Publications and Research Data (ETH Zurich) with 2 publications
  • IEEE Transactions on Pattern Analysis and Machine Intelligence with 1 publication

Goldstein's recent papers illustrate their research scope and interests:

  • "Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses," 2022, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • "A Cookbook of Self-Supervised Learning," 2023, arXiv (Cornell University)
  • "Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models," 2022, arXiv (Cornell University)
  • "A Watermark for Large Language Models," 2023, arXiv (Cornell University)
  • "SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training," 2021, arXiv (Cornell University)

They have been recognized by being named a Fellow of the Alfred P. Sloan Foundation in 2017.

Best Publications

  • The Split Bregman Method for L1-Regularized Problems

    Tom Goldstein;Stanley Osher

  • Visualizing the Loss Landscape of Neural Nets

    Hao Li;Zheng Xu;Gavin Taylor;Christoph Studer

  • Fast Alternating Direction Optimization Methods

    Tom Goldstein;Brendan O'Donoghue;Simon Setzer;Richard G. Baraniuk

  • Adversarial training for free

    Ali Shafahi;Mahyar Najibi;Mohammad Amin Ghiasi;Zheng Xu

  • Geometric Applications of the Split Bregman Method: Segmentation and Surface Reconstruction

    Tom Goldstein;Xavier Bresson;Stanley Osher

  • Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks

    Ali Shafahi;W. Ronny Huang;Mahyar Najibi;Octavian Suciu

  • FreeLB: Enhanced Adversarial Training for Natural Language Understanding

    Chen Zhu;Yu Cheng;Zhe Gan;Siqi Sun

  • Quantized Precoding for Massive MU-MIMO

    Sven Jacobsson;Giuseppe Durisi;Mikael Coldrey;Tom Goldstein

  • PhaseMax: Convex Phase Retrieval via Basis Pursuit

    Tom Goldstein;Christoph Studer

  • A Watermark for Large Language Models

    Unknown

  • Adversarially Robust Distillation

    Micah Goldblum;Liam Fowl;Soheil Feizi;Tom Goldstein

  • A Field Guide to Forward-Backward Splitting with a FASTA Implementation

    Tom Goldstein;Christoph Studer;Richard G. Baraniuk

  • Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses.

    Micah Goldblum;Dimitris Tsipras;Chulin Xie;Xinyun Chen

  • DCAN: Dual Channel-Wise Alignment Networks for Unsupervised Scene Adaptation

    Zuxuan Wu;Xintong Han;Yen-Liang Lin;Mustafa Gökhan Uzunbas

  • A Cookbook of Self-Supervised Learning

    Unknown

  • Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models

    Unknown

  • Making an Invisibility Cloak: Real World Adversarial Attacks on Object Detectors

    Zuxuan Wu;Ser-Nam Lim;Larry S. Davis;Tom Goldstein

  • Training neural networks without gradients: a scalable ADMM approach

    Gavin Taylor;Ryan Burmeister;Zheng Xu;Bharat Singh

  • Channel Charting: Locating Users Within the Radio Environment Using Channel State Information

    Christoph Studer;Said Medjkouh;Emre Gonultas;Tom Goldstein

  • Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models

    Unknown

  • Are adversarial examples inevitable

    Ali Shafahi;W. Ronny Huang;Christoph Studer;Soheil Feizi

  • Universal Adversarial Training

    Ali Shafahi;Mahyar Najibi;Zheng Xu;John P. Dickerson

  • Training Quantized Nets: A Deeper Understanding

    Hao Li;Soham De;Zheng Xu;Christoph Studer

  • Transferable Clean-Label Poisoning Attacks on Deep Neural Nets

    Chen Zhu;W. Ronny Huang;Ali Shafahi;Hengduo Li

  • Certified Data Removal from Machine Learning Models

    Chuan Guo;Tom Goldstein;Awni Hannun;Laurens van der Maaten

  • Certified Defenses for Adversarial Patches

    Ping-Yeh Chiang;Renkun Ni;Ahmed Abdelkader;Chen Zhu

Frequent Co-Authors

Giuseppe Durisi
Giuseppe Durisi Chalmers University of Technology
David W. Jacobs
David W. Jacobs University of Maryland, College Park
Richard G. Baraniuk
Richard G. Baraniuk Rice University
Larry S. Davis
Larry S. Davis University of Maryland, College Park
Zuxuan Wu
Zuxuan Wu Fudan University
Olav Tirkkonen
Olav Tirkkonen Aalto University
Mário A. T. Figueiredo
Mário A. T. Figueiredo Instituto Superior Técnico
David A. Bluemke
David A. Bluemke University of Wisconsin–Madison
Zhe Gan
Zhe Gan Microsoft (United States)

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