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D-Index
40
Citations
8582
World Ranking
645
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96

Computer Science

D-Index
40
Citations
6066
World Ranking
9370
National Ranking
3973

Dustin Tran 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 Dustin Tran 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: 250 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: 560 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: 424 scientists 242–251 publications: 408 scientists 252–261 publications: 378 scientists 262–271 publications: 300 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: 83 publications — 4th percentile

4% of scientists in this discipline score the same or lower.

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

Dustin Tran 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 Dustin Tran sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 984 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 969 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 765 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 517 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: 40 D-Index — 37th percentile

37% 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

  • 2025 - Research.com Rising Stars Award

Overview

Dustin Tran is affiliated with Google in the United States and has a research focus predominantly within computer science. Their work spans several subfields including artificial intelligence, computer vision and pattern recognition, control and systems engineering, computational mechanics, and emergency medicine.

The primary research topics addressed by Dustin Tran include adversarial robustness in machine learning, domain adaptation and few-shot learning, advanced neural network applications, anomaly detection techniques and applications, topic modeling, machine learning and data classification, and Gaussian processes and Bayesian inference.

Dustin Tran has authored numerous publications, with many appearing in the venue arXiv (Cornell University) where they have 23 documented papers. Other publication venues include the Annual Review of Statistics and Its Application, Annals of Vascular Surgery, and Entropy.

  • "Gemini: A Family of Highly Capable Multimodal Models" (2023) published in arXiv (Cornell University)
  • "Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness" (2020) published in arXiv (Cornell University)
  • "BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning" (2020) published in arXiv (Cornell University)
  • "Scaling Vision Transformers to 22 Billion Parameters" (2023) published in arXiv (Cornell University)
  • "Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness" (2020) published in arXiv (Cornell University)

Dustin Tran collaborates frequently with several coauthors including Balaji Lakshminarayanan, Jasper Snoek, Rodolphe Jenatton, Yeming Wen, and Michael W. Dusenberry, indicating a collaborative research approach within the machine learning community.

Best Publications

  • Automatic differentiation variational inference

    Alp Kucukelbir;Dustin Tran;Rajesh Ranganath;Andrew Gelman

  • Edward: A library for probabilistic modeling, inference, and criticism

    Dustin Tran;Alp Kucukelbir;Adji B. Dieng;Maja R. Rudolph

  • Scaling Vision Transformers to 22 Billion Parameters

    Unknown

  • Hierarchical variational models

    Rajesh Ranganath;Dustin Tran;David M. Blei

  • Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches

    Yeming Wen;Paul Vicol;Jimmy Ba;Dustin Tran

  • Larger language models do in-context learning differently

    Unknown

  • BatchEnsemble: an Alternative Approach to Efficient Ensemble and Lifelong Learning

    Yeming Wen;Dustin Tran;Jimmy Ba

  • Image Transformer

    Niki Parmar;Ashish Vaswani;Jakob Uszkoreit;Łukasz Kaiser

  • Mesh-TensorFlow: Deep Learning for Supercomputers

    Noam Shazeer;Youlong Cheng;Niki J. Parmar;Dustin Tran

  • Deep Probabilistic Programming

    Dustin Tran;Matthew D. Hoffman;Rif A. Saurous;Eugene Brevdo

  • TensorFlow Distributions

    Joshua V. Dillon;Ian Langmore;Dustin Tran;Eugene Brevdo

  • Hierarchical Implicit Models and Likelihood-Free Variational Inference

    Dustin Tran;Rajesh Ranganath;David M. Blei

  • Measuring Calibration in Deep Learning

    Jeremy Nixon;Mike Dusenberry;Ghassen Jerfel;Timothy Nguyen

  • Variational Gaussian Process

    Dustin Tran;Rajesh Ranganath;David M. Blei

  • Hyperparameter Ensembles for Robustness and Uncertainty Quantification

    Florian Wenzel;Jasper Snoek;Dustin Tran;Rodolphe Jenatton

  • Analyzing the role of model uncertainty for electronic health records

    Michael W. Dusenberry;Dustin Tran;Edward Choi;Jonas Kemp

  • Variational Inference via $\chi$ Upper Bound Minimization

    Adji Bousso Dieng;Dustin Tran;Rajesh Ranganath;John W. Paisley

  • Plex: Towards Reliability using Pretrained Large Model Extensions

    Unknown

  • Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness

    Jeremiah Zhe Liu;Zi Lin;Shreyas Padhy;Dustin Tran

  • Training independent subnetworks for robust prediction

    Marton Havasi;Rodolphe Jenatton;Stanislav Fort;Jeremiah Zhe Liu

  • The Variational Gaussian Process

    Dustin Tran;Rajesh Ranganath;David M. Blei

  • Operator variational inference

    Rajesh Ranganath;Jaan Altosaar;Dustin Tran;David M. Blei

  • Deep and Hierarchical Implicit Models.

    Dustin Tran;Rajesh Ranganath;David M. Blei

  • NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transport

    Dustin Tran;Ian Langmore;Josh Dillon;Matthew D. Hoffman

Frequent Co-Authors

David M. Blei
David M. Blei Columbia University
Rajesh Ranganath
Rajesh Ranganath New York University
Balaji Lakshminarayanan
Balaji Lakshminarayanan Google (United States)
Jasper Snoek
Jasper Snoek Google (United States)
Aki Vehtari
Aki Vehtari Aalto University
Edoardo M. Airoldi
Edoardo M. Airoldi Temple University
Matthew D. Hoffman
Matthew D. Hoffman Google (United States)
Maria G. Castro
Maria G. Castro University of Michigan–Ann Arbor
Pedro R. Lowenstein
Pedro R. Lowenstein University of Michigan–Ann Arbor
Katherine A. Heller
Katherine A. Heller Google (United States)

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