World's Best Scientists 2026 revealed!
Mohammad Ghavamzadeh

Mohammad Ghavamzadeh

D-Index & Metrics

Computer Science

D-Index
57
Citations
10541
World Ranking
3897
National Ranking
1846

Mohammad Ghavamzadeh 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 Mohammad Ghavamzadeh 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: 176 publications — 37th percentile

37% of scientists in this discipline score the same or lower.

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

Mohammad Ghavamzadeh 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 Mohammad Ghavamzadeh 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: 57 D-Index — 74th percentile

74% of scientists in this discipline score the same or lower.

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

Overview

Mohammad Ghavamzadeh is a researcher affiliated with Amazon in the United States. Their academic work primarily spans the field of Computer Science, with a focus on Artificial Intelligence, Management Science and Operations Research, and subareas such as Computer Networks and Communications, Computational Theory and Mathematics, and Computer Vision and Pattern Recognition.

Their research topics cover a range of subjects, including:

  • Advanced Bandit Algorithms Research
  • Reinforcement Learning in Robotics
  • Machine Learning and Algorithms
  • Topic Modeling
  • Machine Learning and Data Classification
  • Data Stream Mining Techniques
  • Optimization and Search Problems

Ghavamzadeh has an extensive publication record, with many papers appearing in prominent venues. Their frequent publication outlets include:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Information Fusion
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • IEEE Journal on Selected Areas in Information Theory

Notable recent papers authored by Ghavamzadeh include:

  • A review of uncertainty quantification in deep learning: Techniques, applications and challenges (2021, Information Fusion)
  • Finite-Sample Analysis of Proximal Gradient TD Algorithms (2020, arXiv (Cornell University))
  • Aligning Text-to-Image Models using Human Feedback (2023, arXiv (Cornell University))
  • Mirror Descent Policy Optimization (2020, arXiv (Cornell University))
  • DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models (2023, arXiv (Cornell University))

Throughout their career, Ghavamzadeh has collaborated frequently with several researchers, forming recurring co-authorship links. These include:

  • Branislav Kveton (9 collaborations)
  • Yinlam Chow (9 collaborations)
  • Craig Boutilier (9 collaborations)
  • Marek Petrik (7 collaborations)
  • Alessandro Lazaric (5 collaborations)

Best Publications

  • A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges

    Moloud Abdar;Farhad Pourpanah;Sadiq Hussain;Dana Rezazadegan

  • Natural actor-critic algorithms

    Shalabh Bhatnagar;Richard S. Sutton;Mohammad Ghavamzadeh;Mark Lee

  • Bayesian Reinforcement Learning: A Survey

    Mohammad Ghavamzadeh;Shie Mannor;Joelle Pineau;Aviv Tamar

  • Best Arm Identification: A Unified Approach to Fixed Budget and Fixed Confidence

    Victor Gabillon;Mohammad Ghavamzadeh;Alessandro Lazaric

  • A Lyapunov-based Approach to Safe Reinforcement Learning

    Yinlam Chow;Ofir Nachum;Edgar A. Duéñez-Guzmán;Mohammad Ghavamzadeh

  • Risk-Constrained Reinforcement Learning with Percentile Risk Criteria

    Yinlam Chow;Mohammad Ghavamzadeh;Lucas Janson;Marco Pavone

  • High confidence off-policy evaluation

    Philip S. Thomas;Georgios Theocharous;Mohammad Ghavamzadeh

  • Incremental Natural Actor-Critic Algorithms

    Shalabh Bhatnagar;Mohammad Ghavamzadeh;Mark Lee;Richard S Sutton

  • Benchmarking Batch Deep Reinforcement Learning Algorithms.

    Scott Fujimoto;Edoardo Conti;Mohammad Ghavamzadeh;Joelle Pineau

  • Hierarchical multi-agent reinforcement learning

    Rajbala Makar;Sridhar Mahadevan;Mohammad Ghavamzadeh

  • Lyapunov-based Safe Policy Optimization for Continuous Control

    Yinlam Chow;Ofir Nachum;Aleksandra Faust;Edgar Duenez-Guzman

  • Hierarchical multi-agent reinforcement learning

    Mohammad Ghavamzadeh;Sridhar Mahadevan;Rajbala Makar

  • High Confidence Policy Improvement

    Philip Thomas;Georgios Theocharous;Mohammad Ghavamzadeh

  • Regularized Policy Iteration

    Amir M. Farahmand;Mohammad Ghavamzadeh;Shie Mannor;Csaba Szepesvári

  • More Robust Doubly Robust Off-policy Evaluation

    Mehrdad Farajtabar;Yinlam Chow;Mohammad Ghavamzadeh

  • Algorithms for CVaR Optimization in MDPs

    Yinlam Chow;Mohammad Ghavamzadeh

  • Personalized ad recommendation systems for life-time value optimization with guarantees

    Georgios Theocharous;Philip S. Thomas;Mohammad Ghavamzadeh

  • Ad Recommendation Systems for Life-Time Value Optimization

    Georgios Theocharous;Philip S. Thomas;Mohammad Ghavamzadeh

  • Bayesian Multi-Task Reinforcement Learning

    Alessandro Lazaric;Mohammad Ghavamzadeh

  • Finite-sample analysis of proximal gradient TD algorithms

    Bo Liu;Ji Liu;Mohammad Ghavamzadeh;Sridhar Mahadevan

  • Speedy Q-Learning

    Mohammad Ghavamzadeh;Hilbert J. Kappen;Mohammad G. Azar;Rémi Munos

  • Approximate modified policy iteration and its application to the game of Tetris

    Bruno Scherrer;Mohammad Ghavamzadeh;Victor Gabillon;Boris Lesner

  • Convex Optimization: Algorithms and Complexity

    Mohammed Ghavamzadeh;Shie Mannor;Joelle Pineau;Aviv Tamar

Frequent Co-Authors

Alessandro Lazaric
Alessandro Lazaric Facebook (United States)
Sridhar Mahadevan
Sridhar Mahadevan University of Massachusetts Amherst
Shie Mannor
Shie Mannor Technion – Israel Institute of Technology
Branislav Kveton
Branislav Kveton Adobe Systems (United States)
Csaba Szepesvári
Csaba Szepesvári University of Alberta
Rémi Munos
Rémi Munos French Institute for Research in Computer Science and Automation - INRIA
Craig Boutilier
Craig Boutilier Google (United States)
Aviv Tamar
Aviv Tamar Technion – Israel Institute of Technology
Ji Liu
Ji Liu Facebook (United States)
Tara Javidi
Tara Javidi University of California, San Diego

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