World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
42
Citations
7502
World Ranking
8379
National Ranking
334

Scott Sanner 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 Scott Sanner 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: 231 publications — 57th percentile

57% of scientists in this discipline score the same or lower.

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

Scott Sanner 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 Scott Sanner 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: 42 D-Index — 43rd percentile

43% of scientists in this discipline score the same or lower.

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

Overview

Scott Sanner is affiliated with the University of Toronto in Canada and has a significant presence in the field of computer science. Their research primarily spans artificial intelligence, computer vision and pattern recognition, control and systems engineering, building and construction, and signal processing.

The scientist's recent notable papers include:

  • Online continual learning in image classification: An empirical survey, 2021, Neurocomputing
  • Online Class-Incremental Continual Learning with Adversarial Shapley Value, 2021, Proceedings of the AAAI Conference on Artificial Intelligence
  • A Deep Learning-Based Cyberattack Detection System for Transmission Protective Relays, 2020, IEEE Transactions on Smart Grid
  • TransCAM: Transformer attention-based CAM refinement for Weakly supervised semantic segmentation, 2023, Journal of Visual Communication and Image Representation
  • The Lifecyle of a Youtube Video: Phases, Content and Popularity, 2021, Proceedings of the International AAAI Conference on Web and Social Media

Frequent coauthors in their work include Zheda Mai, Baher Abdulhai, Michael Gimelfarb, Ayal Taitler, and Jihwan Jeong, indicating collaborative work across various projects and research themes.

Scott Sanner has published extensively in venues such as arXiv (Cornell University), with 48 publications, followed by the Proceedings of the AAAI Conference on Artificial Intelligence, ACM Transactions on the Web, Proceedings of the International Conference on Automated Planning and Scheduling, and Neurocomputing.

Their main field of study is computer science, with 159 publications in this area. Within this domain, the subfields with the highest representation are artificial intelligence, computer vision and pattern recognition, control and systems engineering, building and construction, and signal processing.

Their research focuses on several key topics including:

  • Topic Modeling
  • Multimodal Machine Learning Applications
  • Domain Adaptation and Few-Shot Learning
  • Reinforcement Learning in Robotics
  • Machine Learning and Algorithms
  • Recommender Systems and Techniques
  • Traffic control and management

Best Publications

  • AutoRec: Autoencoders Meet Collaborative Filtering

    Suvash Sedhain;Aditya Krishna Menon;Scott Sanner;Lexing Xie

  • Improving LDA topic models for microblogs via tweet pooling and automatic labeling

    Rishabh Mehrotra;Scott Sanner;Wray Buntine;Lexing Xie

  • Online Continual Learning in Image Classification: An Empirical Survey

    Zheda Mai;Ruiwen Li;Jihwan Jeong;David Quispe

  • Deep Learning with Microfluidics for Biotechnology.

    Jason Riordon;Dušan Sovilj;Scott Sanner;David Sinton

  • Towards object mapping in non-stationary environments with mobile robots

    R. Biswas;B. Limketkai;S. Sanner;S. Thrun

  • Expecting to be HIP: Hawkes Intensity Processes for Social Media Popularity

    Marian-Andrei Rizoiu;Lexing Xie;Scott Sanner;Manuel Cebrian

  • Online Class-Incremental Continual Learning with Adversarial Shapley Value

    Dongsub Shim;Zheda Mai;Jihwan Jeong;Scott Sanner

  • Supervised Contrastive Replay: Revisiting the Nearest Class Mean Classifier in Online Class-Incremental Continual Learning

    Zheda Mai;Ruiwen Li;Hyunwoo Kim;Scott Sanner

  • The 2014 International Planning Competition: Progress and Trends

    Mauro Vallati;Lukás Chrpa;Marek Grzes;Thomas Leo McCluskey

  • Social collaborative filtering for cold-start recommendations

    Suvash Sedhain;Scott Sanner;Darius Braziunas;Lexing Xie

  • A Survey of the Seventh International Planning Competition

    Amanda Coles;Andrew Coles;Angel García Olaya;Sergio Jiménez

  • Practical solution techniques for first-order MDPs

    Scott Sanner;Craig Boutilier

  • Algorithms for Direct 01 Loss Optimization in Binary Classification

    Tan Nguyen;Scott Sanner

  • Deep learning-based selection of human sperm with high DNA integrity.

    Christopher McCallum;Jason Riordon;Yihe Wang;Tian Kong

  • Affine algebraic decision diagrams (AADDs) and their application to structured probabilistic inference

    Scott Sanner;David McAllester

  • Efficient solutions to factored MDPs with imprecise transition probabilities

    Karina Valdivia Delgado;Scott Sanner;Leliane Nunes de Barros

  • Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferences

    Unknown

  • Gaussian Process Preference Elicitation

    Shengbo Guo;Scott Sanner;Edwin Bonilla

  • Symbolic dynamic programming for first-order POMDPs

    Scott Sanner;Kristian Kersting

  • New objective functions for social collaborative filtering

    Joseph Noel;Scott Sanner;Khoi-Nguyen Tran;Peter Christen

  • Efficient solutions to factored MDPs with imprecise transition probabilities

    Karina Valdivia Delgado;Scott Sanner;Leliane Nunes de Barros;Fabio G. Cozman

  • Comparison of machine learning models for occupancy prediction in residential buildings using connected thermostat data

    Brent Huchuk;Scott Sanner;William O'Brien

  • Real-time Multiattribute Bayesian Preference Elicitation with Pairwise Comparison Queries

    Shengbo Guo;Scott Sanner

Frequent Co-Authors

Lexing Xie
Lexing Xie Australian National University
Kristian Kersting
Kristian Kersting Technical University of Darmstadt
Craig Boutilier
Craig Boutilier Google (United States)
William O'Brien
William O'Brien Carleton University
Manuel Cebrian
Manuel Cebrian Carlos III University of Madrid
Aditya Krishna Menon
Aditya Krishna Menon Google (United States)
Pascal Van Hentenryck
Pascal Van Hentenryck Georgia Institute of Technology
Peter Christen
Peter Christen Australian National University
Mark Chignell
Mark Chignell University of Toronto
Deepa Kundur
Deepa Kundur University of Toronto

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