World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
86
Citations
29258
World Ranking
771
National Ranking
413

Christopher Ré 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 Christopher Ré 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: 314 publications — 76th percentile

76% of scientists in this discipline score the same or lower.

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

Christopher Ré 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 Christopher Ré 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: 86 D-Index — 95th percentile

95% 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

  • 2015 - Fellow of the MacArthur Foundation
  • 2013 - Fellow of Alfred P. Sloan Foundation

Overview

Christopher Ré is affiliated with Stanford University in the United States. Their research primarily focuses on the domain of Computer Science, with a notable emphasis on Artificial Intelligence and related interdisciplinary areas.

Their work spans several subfields of study, which include:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Radiology, Nuclear Medicine and Imaging
  • Molecular Biology
  • Management Science and Operations Research

Major topics covered across their publications are:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Domain Adaptation and Few-Shot Learning
  • Advanced Neural Network Applications
  • Machine Learning and Data Classification
  • Multimodal Machine Learning Applications
  • Advanced Graph Neural Networks

Christopher Ré has published extensively in research venues such as:

  • arXiv (Cornell University)
  • Proceedings of the VLDB Endowment
  • Communications of the ACM
  • Findings of the Association for Computational Linguistics: ACL 2022
  • Zenodo (CERN European Organization for Nuclear Research)

Frequent collaborators include:

  • Daniel Y. Fu
  • Tri Dao
  • Atri Rudra
  • Simran Arora
  • Avanika Narayan

Representative recent papers highlight topics related to foundation models, attention mechanisms, and sequence modeling. Some of these are:

  • On the Opportunities and Risks of Foundation Models (2021), arXiv (Cornell University)
  • Efficiently Modeling Long Sequences with Structured State Spaces (2021), arXiv (Cornell University)
  • FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness (2022), arXiv (Cornell University)
  • HiPPO: Recurrent Memory with Optimal Polynomial Projections (2020), arXiv (Cornell University)
  • Hungry Hungry Hippos: Towards Language Modeling with State Space Models (2022), arXiv (Cornell University)

Christopher Ré has been recognized by major awards, specifically:

  • Fellow of the MacArthur Foundation, 2015
  • Fellow of Alfred P. Sloan Foundation, 2013

Best Publications

  • Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent

    Benjamin Recht;Christopher Re;Stephen Wright;Feng Niu

  • On the Opportunities and Risks of Foundation Models.

    Rishi Bommasani;Drew A. Hudson;Ehsan Adeli;Russ Altman

  • HOGWILD!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent

    Feng Niu;Benjamin Recht;Christopher Re;Stephen J. Wright

  • Snorkel: rapid training data creation with weak supervision

    Alexander Ratner;Stephen H. Bach;Henry Ehrenberg;Jason Fries

  • Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features.

    Kun-Hsing Yu;Ce Zhang;Gerald J. Berry;Russ B. Altman

  • Data Programming: Creating Large Training Sets, Quickly

    Alexander J. Ratner;Christopher M. De Sa;Sen Wu;Daniel Selsam

  • HoloClean: holistic data repairs with probabilistic inference

    Theodoros Rekatsinas;Xu Chu;Ihab F. Ilyas;Christopher Ré

  • The MADlib analytics library: or MAD skills, the SQL

    Joseph M. Hellerstein;Christoper Ré;Florian Schoppmann;Daisy Zhe Wang

  • Efficient Top-k Query Evaluation on Probabilistic Data

    C. Re;N. Dalvi;D. Suciu

  • Efficiently Modeling Long Sequences with Structured State Spaces

    Albert Gu;Karan Goel;Christopher Ré

  • Hyperbolic Graph Convolutional Neural Networks.

    Ines Chami;Rex Ying;Christopher Ré;Jure Leskovec

  • Parallel stochastic gradient algorithms for large-scale matrix completion

    Benjamin Recht;Christopher Ré

  • An asynchronous parallel stochastic coordinate descent algorithm

    Ji Liu;Stephen J. Wright;Christopher Ré;Victor Bittorf

  • Learning to Compose Domain-Specific Transformations for Data Augmentation.

    Alexander J Ratner;Henry R Ehrenberg;Zeshan Hussain;Jared Dunnmon

  • Hidden stratification causes clinically meaningful failures in machine learning for medical imaging

    Luke Oakden-Rayner;Jared Dunnmon;Gustavo Carneiro;Christopher Re

  • Low-Dimensional Hyperbolic Knowledge Graph Embeddings

    Ines Chami;Adva Wolf;Da-Cheng Juan;Frederic Sala

  • Using Social Media to Measure Labor Market Flows

    Dolan Antenucci;Michael Cafarella;Margaret Levenstein;Christopher Ré

  • Representation Tradeoffs for Hyperbolic Embeddings.

    Christopher De Sa;Albert Gu;Christopher Ré;Frederic Sala

  • Incremental knowledge base construction using DeepDive

    Jaeho Shin;Sen Wu;Feiran Wang;Christopher De Sa

  • Event queries on correlated probabilistic streams

    Christopher Ré;Julie Letchner;Magdalena Balazinksa;Dan Suciu

  • MYSTIQ: a system for finding more answers by using probabilities

    Jihad Boulos;Nilesh Dalvi;Bhushan Mandhani;Shobhit Mathur

  • Probabilistic databases: diamonds in the dirt

    Nilesh Dalvi;Christopher Ré;Dan Suciu

  • HiPPO: Recurrent Memory with Optimal Polynomial Projections

    Albert Gu;Tri Dao;Stefano Ermon;Atri Rudra

Frequent Co-Authors

Ce Zhang
Ce Zhang ETH Zurich
Dan Suciu
Dan Suciu University of Washington
Atri Rudra
Atri Rudra University at Buffalo, State University of New York
Kunle Olukotun
Kunle Olukotun Stanford University
Hung Q. Ngo
Hung Q. Ngo University at Buffalo, State University of New York
Daniel L. Rubin
Daniel L. Rubin Stanford University
Benjamin Recht
Benjamin Recht University of California, Berkeley
Magdalena Balazinska
Magdalena Balazinska University of Washington
Michael Snyder
Michael Snyder Stanford University

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