World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
53
Citations
8347
World Ranking
4923
National Ranking
193

Pascal Poupart 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 Pascal Poupart 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: 226 publications — 55th percentile

55% of scientists in this discipline score the same or lower.

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

Pascal Poupart 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 Pascal Poupart 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: 53 D-Index — 67th percentile

67% of scientists in this discipline score the same or lower.

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

Overview

Pascal Poupart is affiliated with the University of Waterloo in Canada and has contributed extensively to the field of Computer Science. Their research spans multiple subfields, predominantly focusing on Artificial Intelligence, with notable work also in Computer Vision and Pattern Recognition, Management Science and Operations Research, Automotive Engineering, and Computational Theory and Mathematics.

Their research topics cover a broad range of advanced themes including Topic Modeling, Natural Language Processing Techniques, Multimodal Machine Learning Applications, Domain Adaptation and Few-Shot Learning, Gaussian Processes and Bayesian Inference, Privacy-Preserving Technologies in Data, and Autonomous Vehicle Technology and Safety.

Pascal Poupart's publication record includes numerous papers, with selected recent works being:

  • Learning Dynamic Belief Graphs to Generalize on Text-Based Games, 2020, arXiv (Cornell University)
  • Diachronic Embedding for Temporal Knowledge Graph Completion, 2020, Proceedings of the AAAI Conference on Artificial Intelligence
  • RAIL-KD: RAndom Intermediate Layer Mapping for Knowledge Distillation, 2022, Findings of the Association for Computational Linguistics: NAACL 2022
  • Hierarchical Double Dirichlet Process Mixture of Gaussian Processes, 2021, Proceedings of the AAAI Conference on Artificial Intelligence
  • Newton-type Methods for Minimax Optimization, 2020, arXiv (Cornell University)

They frequently publish in venues such as:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • International Journal of Approximate Reasoning
  • Findings of the Association for Computational Linguistics: NAACL 2022
  • Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Pascal Poupart collaborates regularly with a number of coauthors, including:

  • Guojun Zhang
  • Mehdi Rezagholizadeh
  • Sriram Ganapathi Subramanian
  • Agustinus Kristiadi
  • Ahmad Rashid

Best Publications

  • An analytic solution to discrete Bayesian reinforcement learning

    Pascal Poupart;Nikos Vlassis;Jesse Hoey;Kevin Regan

  • Representation Learning for Dynamic Graphs: A Survey

    Seyed Mehran Kazemi;Rishab Goel;Kshitij Jain;Ivan Kobyzev

  • Exploiting structure to efficiently solve large scale partially observable markov decision processes

    Pascal Poupart

  • Point-Based Value Iteration for Continuous POMDPs

    Josep M. Porta;Nikos Vlassis;Matthijs T.J. Spaan;Pascal Poupart

  • Diachronic Embedding for Temporal Knowledge Graph Completion

    Rishab Goel;Seyed Mehran Kazemi;Marcus Brubaker;Pascal Poupart

  • Automated handwashing assistance for persons with dementia using video and a partially observable Markov decision process

    Jesse Hoey;Pascal Poupart;Axel von Bertoldi;Tammy Craig

  • Bounded Finite State Controllers

    Pascal Poupart;Craig Boutilier

  • A planning system based on Markov decision processes to guide people with dementia through activities of daily living

    J. Boger;J. Hoey;P. Poupart;C. Boutilier

  • A decision-theoretic approach to task assistance for persons with dementia

    Jennifer Boger;Pascal Poupart;Jesse Hoey;Craig Boutilier

  • Learning Rate Based Branching Heuristic for SAT Solvers

    Jia Hui Liang;Vijay Ganesh;Pascal Poupart;Krzysztof Czarnecki

  • Affective Neural Response Generation

    Nabiha Asghar;Pascal Poupart;Jesse Hoey;Xin Jiang

  • Constraint-based optimization and utility elicitation using the minimax decision criterion

    Craig Boutilier;Relu Patrascu;Pascal Poupart;Dale Schuurmans

  • Assisting persons with dementia during handwashing using a partially observable Markov decision process.

    Jesse Hoey;Axel von Bertoldi;Pascal Poupart;Alex Mihailidis

  • Self-adaptive hierarchical sentence model

    Han Zhao;Zhengdong Lu;Pascal Poupart

  • Value-Directed Compression of POMDPs

    Pascal Poupart;Craig Boutilier

  • Factored partially observable Markov decision processes for dialogue management

    J Williams;P Poupart;SJ Young

  • Solving POMDPs with continuous or large discrete observation spaces

    Jesse Hoey;Pascal Poupart

  • Bayesian reputation modeling in E-marketplaces sensitive to subjecthity, deception and change

    Kevin Regan;Pascal Poupart;Robin Cohen

  • PARTIALLY OBSERVABLE MARKOV DECISION PROCESSES WITH CONTINUOUS OBSERVATIONS FOR DIALOGUE MANAGEMENT

    Jason D. Williams;Pascal Poupart;Steve J. Young

  • VDCBPI: an Approximate Scalable Algorithm for Large POMDPs

    Pascal Poupart;Craig Boutilier

  • Time2Vec: Learning a Vector Representation of Time

    Seyed Mehran Kazemi;Rishab Goel;Sepehr Eghbali;Janahan Ramanan

  • Partially Observable Markov Decision Processes.

    Pascal Poupart

Frequent Co-Authors

Craig Boutilier
Craig Boutilier Google (United States)
Jesse Hoey
Jesse Hoey University of Waterloo
Lili Mou
Lili Mou University of Alberta
Alex Mihailidis
Alex Mihailidis University of Toronto
Eric A. Roy
Eric A. Roy University of Waterloo
Dale Schuurmans
Dale Schuurmans University of Alberta
Sandra E. Black
Sandra E. Black University of Toronto
Steve Young
Steve Young University of Cambridge
Jason D. Williams
Jason D. Williams Apple (United States)
Marc Toussaint
Marc Toussaint Technical University of Berlin

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