World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
33
Citations
5288
World Ranking
12587
National Ranking
5102

Abhradeep Thakurta 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 Abhradeep Thakurta 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: 114 publications — 13th percentile

13% of scientists in this discipline score the same or lower.

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

Abhradeep Thakurta 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 Abhradeep Thakurta 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: 33 D-Index — 13th percentile

13% of scientists in this discipline score the same or lower.

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

Overview

Abhradeep Thakurta is a researcher affiliated with Google in the United States. Their work primarily centers on the field of computer science, with a significant focus on artificial intelligence. Their research portfolio spans 122 publications, with 108 dedicated to artificial intelligence, supported by contributions to related subfields such as statistics and probability, sociology and political science, computer networks and communications, and computational theory and mathematics.

The main topics in Thakurta's research include privacy-preserving technologies in data, stochastic gradient optimization techniques, adversarial robustness in machine learning, cryptography and data security, random matrices and applications, complexity and algorithms in graphs, and mobile crowdsensing and crowdsourcing.

Thakurta has contributed extensively to publications across a variety of venues. They have 55 publications in the arXiv repository, reflecting a strong presence in preprint dissemination. Other notable venues include Leibniz-Zentrum für Informatik (Schloss Dagstuhl), Harvard Dataverse, Journal of Artificial Intelligence Research, and the Proceedings of the AAAI Conference on Artificial Intelligence.

Several recent notable papers authored by or involving Thakurta are:

  • How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy, 2023, Journal of Artificial Intelligence Research
  • Tempered Sigmoid Activations for Deep Learning with Differential Privacy, 2021, Proceedings of the AAAI Conference on Artificial Intelligence
  • Encode, Shuffle, Analyze Privacy Revisited: Formalizations and Empirical Evaluation, 2020, arXiv (Cornell University)
  • Tempered Sigmoid Activations for Deep Learning with Differential Privacy, 2020, arXiv (Cornell University)
  • Advancing Differential Privacy: Where We Are Now and Future Directions for Real-World Deployment, 2024, Harvard Data Science Review

Frequent co-authors collaborating with Thakurta include Arun Ganesh, Shuang Song, Thomas Steinke, Om Thakkar, and Steve Chien. These collaborations suggest an active research network and joint efforts in advancing topics related to differential privacy, machine learning, and data privacy.

Best Publications

  • Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds

    Raef Bassily;Adam Smith;Abhradeep Thakurta

  • Private Convex Empirical Risk Minimization and High-dimensional Regression

    Daniel Kifer;Adam Smith;Abhradeep Thakurta

  • GUPT: privacy preserving data analysis made easy

    Prashanth Mohan;Abhradeep Thakurta;Elaine Shi;Dawn Song

  • Discovering frequent patterns in sensitive data

    Raghav Bhaskar;Srivatsan Laxman;Adam Smith;Abhradeep Thakurta

  • Analyze gauss: optimal bounds for privacy-preserving principal component analysis

    Cynthia Dwork;Kunal Talwar;Abhradeep Thakurta;Li Zhang

  • Differentially Private Online Learning

    Prateek Jain;Pravesh Kothari;Abhradeep Thakurta

  • Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity

    Úlfar Erlingsson;Vitaly Feldman;Ilya Mironov;Ananth Raghunathan

  • Amplification by shuffling: from local to central differential privacy via anonymity

    Úlfar Erlingsson;Vitaly Feldman;Ilya Mironov;Ananth Raghunathan

  • How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy

    Unknown

  • Is Interaction Necessary for Distributed Private Learning

    Adam Smith;Abhradeep Thakurta;Jalaj Upadhyay

  • Differentially Private Feature Selection via Stability Arguments, and the Robustness of the Lasso

    Abhradeep Guha Thakurta;Adam Smith

  • Towards Practical Differentially Private Convex Optimization

    Roger Iyengar;Joseph P. Near;Dawn Song;Om Thakkar

  • Nearly-optimal private LASSO

    Kunal Talwar;Abhradeep Thakurta;Li Zhang

  • Near) Dimension Independent Risk Bounds for Differentially Private Learning

    Prateek Jain;Abhradeep Guha Thakurta

  • Tempered Sigmoid Activations for Deep Learning with Differential Privacy.

    Nicolas Papernot;Abhradeep Thakurta;Shuang Song;Steve Chien

  • Privacy Amplification by Iteration

    Vitaly Feldman;Ilya Mironov;Kunal Talwar;Abhradeep Thakurta

  • Differentially Private Learning with Kernels

    Prateek Jain;Abhradeep Thakurta

  • Noiseless database privacy

    Raghav Bhaskar;Abhishek Bhowmick;Vipul Goyal;Srivatsan Laxman

  • Practical Locally Private Heavy Hitters

    Raef Bassily;Kobbi Nissim;Uri Stemmer;Abhradeep Guha Thakurta

  • Tempered Sigmoid Activations for Deep Learning with Differential Privacy.

    Nicolas Papernot;Abhradeep Thakurta;Shuang Song;Steve Chien

  • Differentially Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds

    Raef Bassily;Adam Smith;Abhradeep Thakurta

  • Private Stochastic Convex Optimization with Optimal Rates

    Raef Bassily;Vitaly Feldman;Kunal Talwar;Abhradeep Guha Thakurta

  • (Nearly) Optimal Algorithms for Private Online Learning in Full-information and Bandit Settings

    Abhradeep Guha Thakurta;Adam Smith

  • Privacy Amplification by Iteration

    Vitaly Feldman;Ilya Mironov;Kunal Talwar;Abhradeep Thakurta

  • Analyze Gauss: optimal bounds for privacy-preserving PCA

    Cynthia Dwork;Kunal Talwar;Abhradeep Thakurta;Li Zhang

Frequent Co-Authors

Adam Smith
Adam Smith Boston University
Kunal Talwar
Kunal Talwar Apple (United States)
Prateek Jain
Prateek Jain Google (United States)
Ilya Mironov
Ilya Mironov Google (United States)
Vitaly Feldman
Vitaly Feldman Apple (United States)
Sanjam Garg
Sanjam Garg University of California, Berkeley
Somesh Jha
Somesh Jha University of Wisconsin–Madison
Li Zhang
Li Zhang Google (United States)
Nicholas Carlini
Nicholas Carlini Google (United States)

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