World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
53
Citations
11027
World Ranking
4834
National Ranking
2251

Alekh Agarwal 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 Alekh Agarwal 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: 118 publications — 14th percentile

14% of scientists in this discipline score the same or lower.

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

Alekh Agarwal 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 Alekh Agarwal 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

Alekh Agarwal is affiliated with Google in the United States and has contributed extensively to the fields of Computer Science and Decision Sciences. Their work spans several subfields, including Artificial Intelligence, Management Science and Operations Research, Information Systems, Statistical and Nonlinear Physics, and Control and Systems Engineering.

Their research topics primarily focus on advanced techniques and methodologies in machine learning and artificial intelligence. Key areas include:

  • Advanced Bandit Algorithms Research
  • Reinforcement Learning in Robotics
  • Adversarial Robustness in Machine Learning
  • Model Reduction and Neural Networks
  • Machine Learning and Algorithms
  • Stochastic Gradient Optimization Techniques
  • Evolutionary Algorithms and Applications

Alekh Agarwal has been involved in numerous publications, with a strong presence in venues such as arXiv (Cornell University), Proceedings of the AAAI Conference on Artificial Intelligence, and PLoS ONE. There are 45 publications in arXiv alone, indicating a significant engagement with open-access preprint dissemination.

Recent papers authored or co-authored by Alekh Agarwal include:

  • Safe Reinforcement Learning via Curriculum Induction, 2020, arXiv (Cornell University)
  • FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs, 2020, arXiv (Cornell University)
  • Provably Good Batch Reinforcement Learning Without Great Exploration, 2020, arXiv (Cornell University)
  • PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning, 2020, arXiv (Cornell University)
  • Federated Residual Learning, 2020, arXiv (Cornell University)

Collaboration is a significant component of their work. Frequent co-authors include Ching-An Cheng, Tong Zhang, Akshay Krishnamurthy, Christoph Dann, and Jacob Eisenstein. These collaborations highlight connections across researchers specializing in machine learning and related disciplines.

Best Publications

  • Dual Averaging for Distributed Optimization: Convergence Analysis and Network Scaling

    J. C. Duchi;A. Agarwal;M. J. Wainwright

  • Distributed delayed stochastic optimization

    Alekh Agarwal;John C. Duchi

  • Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions

    Alekh Agarwal;Sahand N. Negahban;Martin J. Wainwright

  • A Reductions Approach to Fair Classification

    Alekh Agarwal;Alina Beygelzimer;Miroslav Dudík;John Langford

  • A reliable effective terascale linear learning system

    Alekh Agarwal;Olivier Chapelle;Miroslav Dudík;John Langford

  • Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits

    Alekh Agarwal;Daniel Hsu;Satyen Kale;John Langford

  • Information-theoretic lower bounds on the oracle complexity of convex optimization

    Alekh Agarwal;Peter L. Bartlett;Pradeep Ravikumar;Martin J. Wainwright

  • Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds

    Jordan T. Ash;Chicheng Zhang;Akshay Krishnamurthy;John Langford

  • Optimal Algorithms for Online Convex Optimization with Multi-Point Bandit Feedback.

    Alekh Agarwal;Ofer Dekel;Lin Xiao

  • Fast global convergence of gradient methods for high-dimensional statistical recovery

    Alekh Agarwal;Sahand N. Negahban;Martin J. Wainwright

  • Information-Theoretic Lower Bounds on the Oracle Complexity of Stochastic Convex Optimization

    A. Agarwal;P. L. Bartlett;P. Ravikumar;M. J. Wainwright

  • Optimality and Approximation with Policy Gradient Methods in Markov Decision Processes

    Alekh Agarwal;Sham M. Kakade;Jason D. Lee;Gaurav Mahajan

  • Contextual decision processes with low Bellman rank are PAC-learnable

    Nan Jiang;Akshay Krishnamurthy;Alekh Agarwal;John Langford

  • On the Theory of Policy Gradient Methods: Optimality, Approximation, and Distribution Shift

    Alekh Agarwal;Sham M. Kakade;Jason D. Lee;Gaurav Mahajan

  • Learning to Search Better than Your Teacher

    Kai-Wei Chang;Akshay Krishnamurthy;Alekh Agarwal;Hal Daume

  • Stochastic Convex Optimization with Bandit Feedback

    Alekh Agarwal;Dean P. Foster;Daniel J. Hsu;Sham M. Kakade

  • Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization

    Alekh Agarwal;Animashree Anandkumar;Prateek Jain;Praneeth Netrapalli

  • Fast global convergence rates of gradient methods for high-dimensional statistical recovery

    Alekh Agarwal;Sahand Negahban;Martin J Wainwright

  • Information-theoretic lower bounds on the oracle complexity of convex optimization

    Alekh Agarwal;Martin J Wainwright;Peter L. Bartlett;Pradeep K. Ravikumar

  • Learning to rank networked entities

    Alekh Agarwal;Soumen Chakrabarti;Sunny Aggarwal

  • Fast convergence of regularized learning in games

    Vasilis Syrgkanis;Alekh Agarwal;Haipeng Luo;Robert E. Schapire

  • Provably efficient RL with Rich Observations via Latent State Decoding

    Simon S. Du;Akshay Krishnamurthy;Nan Jiang;Alekh Agarwal

  • Model-based RL in Contextual Decision Processes: PAC bounds and Exponential Improvements over Model-free Approaches

    Wen Sun;Nan Jiang;Akshay Krishnamurthy;Alekh Agarwal

Frequent Co-Authors

John Langford
John Langford Microsoft (United States)
Akshay Krishnamurthy
Akshay Krishnamurthy Microsoft (United States)
Miroslav Dudík
Miroslav Dudík Microsoft (United States)
Robert E. Schapire
Robert E. Schapire Microsoft (United States)
Peter L. Bartlett
Peter L. Bartlett University of California, Berkeley
John C. Duchi
John C. Duchi Stanford University
Sham M. Kakade
Sham M. Kakade Harvard University
Hal Daumé
Hal Daumé University of Maryland, College Park
Daniel Hsu
Daniel Hsu Columbia University

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