World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
60
Citations
11296
World Ranking
3297
National Ranking
1597

Jeff Schneider 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 Jeff Schneider 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: 240 publications — 59th percentile

59% of scientists in this discipline score the same or lower.

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

Jeff Schneider 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 Jeff Schneider 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: 60 D-Index — 78th percentile

78% of scientists in this discipline score the same or lower.

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

Overview

Jeff Schneider is affiliated with Carnegie Mellon University in the United States. Their research is concentrated in the field of Computer Science, with a strong focus on several specialized subfields including Artificial Intelligence, Management Science and Operations Research, Computer Networks and Communications, Control and Systems Engineering, and Computer Vision and Pattern Recognition.

Their work engages with a variety of main topics such as Reinforcement Learning in Robotics, Machine Learning and Algorithms, Optimization and Search Problems, Adversarial Robustness in Machine Learning, Auction Theory and Applications, Data Stream Mining Techniques, and Autonomous Vehicle Technology and Safety.

Recent publications by Jeff Schneider include:

  • "When is Deep Learning the Best Approach to Knowledge Tracing?" (2020) published in Zenodo (CERN European Organization for Nuclear Research)
  • "Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantification" (2021) published in arXiv (Cornell University)
  • "Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification" (2020) published in arXiv (Cornell University)
  • "Multi-Agent Active Search: A Reinforcement Learning Approach" (2021) published in IEEE Robotics and Automation Letters

Jeff Schneider frequently publishes in venues such as arXiv (Cornell University), where they have 47 publications, IEEE Robotics and Automation Letters with 2 publications, Nuclear Fusion with 2 publications, Zenodo (CERN European Organization for Nuclear Research), and the 2021 60th IEEE Conference on Decision and Control (CDC).

They have collaborated regularly with several coauthors, including Willie Neiswanger, Ian Char, Youngseog Chung, Viraj Mehta, and Ramina Ghods.

Best Publications

  • Temporal Collaborative Filtering with Bayesian Probabilistic Tensor Factorization

    Liang Xiong;Xi Chen;Tzu-Kuo Huang;Jeff G. Schneider

  • Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks

    Henggang Cui;Vladan Radosavljevic;Fang-Chieh Chou;Tsung-Han Lin

  • Neural Architecture Search with Bayesian Optimisation and Optimal Transport

    Kirthevasan Kandasamy;Willie Neiswanger;Jeff Schneider;Barnabas Poczos

  • Autonomous helicopter control using reinforcement learning policy search methods

    J.A. Bagnell;J.G. Schneider

  • Efficiently learning the accuracy of labeling sources for selective sampling

    Pinar Donmez;Jaime G. Carbonell;Jeff Schneider

  • Detecting anomalous records in categorical datasets

    Kaustav Das;Jeff Schneider

  • Approximate Solutions for Partially Observable Stochastic Games with Common Payoffs

    Rosemary Emery-Montemerlo;Geoff Gordon;Jeff Schneider;Sebastian Thrun

  • High Dimensional Bayesian Optimisation and Bandits via Additive Models

    Kirthevasan Kandasamy;Jeff Schneider;Barnabas Poczos

  • Uncertainty-aware Short-term Motion Prediction of Traffic Actors for Autonomous Driving

    Nemanja Djuric;Vladan Radosavljevic;Henggang Cui;Thi Nguyen

  • Controlling the False-Discovery Rate in Astrophysical Data Analysis

    Christopher J. Miller;Christopher Genovese;Robert C. Nichol;Larry Wasserman

  • Policy Search by Dynamic Programming

    J. A. Bagnell;Sham M Kakade;Jeff G. Schneider;Andrew Y. Ng

  • Distributed Value Functions

    Jeff G. Schneider;Weng-Keen Wong;Andrew W. Moore;Martin A. Riedmiller

  • Covariant policy search

    J. Andrew Bagnell;Jeff Schneider

  • Multi-Label Output Codes using Canonical Correlation Analysis

    Yi Zhang;Jeff G. Schneider

  • Deep Learning with Sets and Point Clouds

    Siamak Ravanbakhsh;Jeff G. Schneider;Barnabás Póczos

  • Parallelised Bayesian Optimisation via Thompson Sampling

    Kirthevasan Kandasamy;Akshay Krishnamurthy;Jeff Schneider;Barnabás Póczos

  • Anomaly pattern detection in categorical datasets

    Kaustav Das;Jeff Schneider;Daniel B. Neill

  • Multi-fidelity Bayesian optimisation with continuous approximations

    Kirthevasan Kandasamy;Gautam Dasarathy;Jeff Schneider;Barnabás Póczos

  • Efficient Locally Weighted Polynomial Regression Predictions

    Andrew W. Moore;Jeff Schneider;Kan Deng

  • Automatic construction of active appearance models as an image coding problem

    S. Baker;I. Matthews;J. Schneider

  • On the error of random fourier features

    Dougal J. Sutherland;Jeff Schneider

Frequent Co-Authors

Barnabás Póczos
Barnabás Póczos Carnegie Mellon University
Andrew W. Moore
Andrew W. Moore Carnegie Mellon University
Robert C. Nichol
Robert C. Nichol University of Surrey
Eric P. Xing
Eric P. Xing Mohamed bin Zayed University of Artificial Intelligence
Larry Wasserman
Larry Wasserman Carnegie Mellon University
John M. Dolan
John M. Dolan Carnegie Mellon University
Christopher J. Miller
Christopher J. Miller University of California, Davis
Alexander S. Szalay
Alexander S. Szalay Johns Hopkins University
Howie Choset
Howie Choset Carnegie Mellon University
Andrew M. Hopkins
Andrew M. Hopkins Macquarie University

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