World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
53
Citations
27146
World Ranking
4679
National Ranking
2172

Ameet Talwalkar 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 Ameet Talwalkar 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: 128 publications — 18th percentile

18% of scientists in this discipline score the same or lower.

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

Ameet Talwalkar 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 Ameet Talwalkar 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

Ameet Talwalkar is affiliated with Carnegie Mellon University in the United States, with a focus on research in computer science. Their work spans multiple subfields including artificial intelligence, computer vision and pattern recognition, management science and operations research, genetics, and computer science applications.

The primary fields of study for Talwalkar revolve around computer science, encompassing 117 publications. Their research topics cover a range of areas with particular emphasis on machine learning and data classification, explainable artificial intelligence (XAI), topic modeling, domain adaptation and few-shot learning, adversarial robustness in machine learning, advanced neural network applications, and privacy-preserving technologies in data.

The scientist's publication record includes numerous papers, with research often appearing in well-known venues. Frequent publication platforms include:

  • arXiv (Cornell University)
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • British Journal of Surgery
  • Queue

Some of the recent research papers authored or co-authored by Talwalkar are:

  • Applying interpretable machine learning in computational biology-pitfalls, recommendations and opportunities for new developments, 2024, Nature Methods
  • Interpretable machine learning, 2022, Communications of the ACM
  • Inferring population structure in biobank-scale genomic data, 2022, The American Journal of Human Genetics
  • Interpretable Machine Learning, 2021, Queue
  • A Field Guide to Federated Optimization, 2021, arXiv (Cornell University)

The collaborator network of Talwalkar consists of frequent co-authors, reflecting interdisciplinary partnerships and sustained research collaborations. Key frequent co-authors include:

  • Valerie Chen
  • Mikhail Khodak
  • Gregory Plumb
  • Joon Sik Kim
  • Virginia Smith

Best Publications

  • Federated Learning: Challenges, Methods, and Future Directions

    Tian Li;Anit Kumar Sahu;Ameet Talwalkar;Virginia Smith

  • Foundations of Machine Learning

    Mehryar Mohri;Afshin Rostamizadeh;Afshin Rostamizadeh;Ameet Talwalkar;Ameet Talwalkar

  • Federated Optimization in Heterogeneous Networks

    Tian Li;Anit Kumar Sahu;Manzil Zaheer;Maziar Sanjabi

  • Hyperband: a novel bandit-based approach to hyperparameter optimization

    Lisha Li;Kevin Jamieson;Giulia DeSalvo;Afshin Rostamizadeh

  • MLlib: machine learning in apache spark

    Xiangrui Meng;Joseph Bradley;Burak Yavuz;Evan Sparks

  • A large-scale evaluation of computational protein function prediction

    Predrag Radivojac;Wyatt T Clark;Tal Ronnen Oron;Alexandra M Schnoes

  • Federated multi-task learning

    Virginia Smith;Chao-Kai Chiang;Maziar Sanjabi;Ameet Talwalkar

  • Random Search and Reproducibility for Neural Architecture Search

    Liam Li;Ameet Talwalkar

  • Interpretable Machine Learning

    Unknown

  • LEAF: A Benchmark for Federated Settings

    Sebastian Caldas;Peter Wu;Tian Li;Jakub Konecný

  • A scalable bootstrap for massive data

    Ariel Kleiner;Ameet Talwalkar;Purnamrita Sarkar;Michael I. Jordan

  • MLbase: A Distributed Machine-learning System

    Tim Kraska;Ameet Talwalkar;John C. Duchi;Rean Griffith

  • Non-stochastic Best Arm Identification and Hyperparameter Optimization

    Kevin G. Jamieson;Ameet Talwalkar

  • Sampling methods for the Nyström method

    Sanjiv Kumar;Mehryar Mohri;Ameet Talwalkar

  • The Foundations of Machine Learning

    Mehryar Mohri;Afshin Rostamizadeh;Ameet Talwalkar

  • Large-scale manifold learning

    A. Talwalkar;S. Kumar;H. Rowley

  • Expanding the Reach of Federated Learning by Reducing Client Resource Requirements

    Sebastian Caldas;Jakub Konecný;H. Brendan McMahan;Ameet Talwalkar

  • Divide-and-Conquer Matrix Factorization

    Lester Mackey;Ameet Talwalkar;Michael I. Jordan

  • On the Convergence of Federated Optimization in Heterogeneous Networks.

    Anit Kumar Sahu;Tian Li;Maziar Sanjabi;Manzil Zaheer

  • Joint Link Prediction and Attribute Inference Using a Social-Attribute Network

    Neil Zhenqiang Gong;Ameet Talwalkar;Lester Mackey;Ling Huang

  • A System for Massively Parallel Hyperparameter Tuning

    Liam Li;Kevin G. Jamieson;Afshin Rostamizadeh;Ekaterina Gonina

  • Adaptive Gradient-Based Meta-Learning Methods

    Mikhail Khodak;Maria-Florina F. Balcan;Ameet S. Talwalkar

  • A System for Massively Parallel Hyperparameter Tuning

    Liam Li;Kevin Jamieson;Afshin Rostamizadeh;Ekaterina Gonina

Frequent Co-Authors

Michael I. Jordan
Michael I. Jordan University of California, Berkeley
Afshin Rostamizadeh
Afshin Rostamizadeh Google (United States)
Mehryar Mohri
Mehryar Mohri Google (United States)
Michael J. Franklin
Michael J. Franklin University of Chicago
Sanjiv Kumar
Sanjiv Kumar Google (United States)
Maria-Florina Balcan
Maria-Florina Balcan Carnegie Mellon University
David A. Patterson
David A. Patterson University of California, Berkeley
Eric P. Xing
Eric P. Xing Mohamed bin Zayed University of Artificial Intelligence
Matei Zaharia
Matei Zaharia University of California, Berkeley

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