World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
33
Citations
7690
World Ranking
12422
National Ranking
5037

Sashank J. Reddi 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 Sashank J. Reddi 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: 74 publications — 2nd percentile

2% of scientists in this discipline score the same or lower.

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

Sashank J. Reddi 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 Sashank J. Reddi 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

Sashank J. Reddi is affiliated with Google in the United States and has contributed extensively to the field of computer science, particularly in artificial intelligence and machine learning. Their research output comprises a significant number of publications focusing on optimization techniques, privacy, and advanced neural application methods.

The scientist's recent publications include:

  • "A Field Guide to Federated Optimization", 2021, arXiv (Cornell University)
  • "Adaptive Federated Optimization", 2020, arXiv (Cornell University)
  • "Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning", 2020, arXiv (Cornell University)
  • "$O(n)$ Connections are Expressive Enough: Universal Approximability of Sparse Transformers", 2020, arXiv (Cornell University)
  • "Why distillation helps: a statistical perspective", 2020, arXiv (Cornell University)

Their frequent collaborators include:

  • Sanjiv Kumar
  • Ankit Singh Rawat
  • Aditya Krishna Menon
  • Nikunj Saunshi
  • Srinadh Bhojanapalli

Prominent publication venues for Sashank J. Reddi primarily include arXiv (Cornell University), evidencing a focus on accessible and cutting-edge research dissemination.

The main fields of study represented in their work are:

  • Computer Science

Their subfields of study encompass:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Computational Mechanics
  • Information Systems
  • Signal Processing

Core topics emphasized in their research are:

  • Stochastic Gradient Optimization Techniques
  • Privacy-Preserving Technologies in Data
  • Domain Adaptation and Few-Shot Learning
  • Sparse and Compressive Sensing Techniques
  • Machine Learning and Extreme Learning Machines (ELM)
  • Advanced Neural Network Applications
  • Text and Document Classification Technologies

Best Publications

  • On the Convergence of Adam and Beyond

    Sashank J. Reddi;Satyen Kale;Sanjiv Kumar

  • SCAFFOLD: Stochastic Controlled Averaging for Federated Learning

    Sai Praneeth Reddy Karimireddy;Satyen Kale;Mehryar Mohri;Sashank Jakkam Reddi

  • Large Batch Optimization for Deep Learning: Training BERT in 76 minutes

    Yang You;Jing Li;Sashank Reddi;Jonathan Hseu

  • Stochastic variance reduction for nonconvex optimization

    Sashank J. Reddi;Ahmed Hefny;Suvrit Sra;Barnabás Póczós

  • Adaptive Methods for Nonconvex Optimization

    Manzil Zaheer;Sashank J. Reddi;Devendra Singh Sachan;Satyen Kale

  • SCAFFOLD: Stochastic Controlled Averaging for On-Device Federated Learning.

    Sai Praneeth Karimireddy;Satyen Kale;Mehryar Mohri;Sashank J. Reddi

  • Adaptive Federated Optimization

    Sashank Reddi;Zachary Charles;Manzil Zaheer;Zachary Garrett

  • Proximal stochastic methods for nonsmooth nonconvex finite-sum optimization

    Sashank J. Reddi;Suvrit Sra;Barnabas Poczos;Alexander J. Smola

  • Riemannian SVRG: fast stochastic optimization on riemannian manifolds

    Hongyi Zhang;Sashank J. Reddi;Suvrit Sra

  • A Field Guide to Federated Optimization

    Jianyu Wang;Zachary Charles;Zheng Xu;Gauri Joshi

  • On the decreasing power of kernel and distance based nonparametric hypothesis tests in high dimensions

    Aaditya Ramdas;Sashank J. Reddi;Barnabás Póczos;Aarti Singh

  • On variance reduction in stochastic gradient descent and its asynchronous variants

    Sashank J. Reddi;Ahmed Hefny;Suvrit Sra;Barnabás Pöczos

  • Adaptive Federated Optimization

    Sashank J. Reddi;Zachary Charles;Manzil Zaheer;Zachary Garrett

  • AIDE: Fast and Communication Efficient Distributed Optimization

    Sashank J. Reddi;Jakub Konecný;Peter Richtárik;Barnabás Póczos

  • Stochastic Frank-Wolfe methods for nonconvex optimization

    Sashank J. Reddi;Suvrit Sra;Barnabas Poczos;Alex Smola

  • Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning

    Sai Praneeth Karimireddy;Martin Jaggi;Satyen Kale;Mehryar Mohri

  • Are Transformers universal approximators of sequence-to-sequence functions?

    Chulhee Yun;Srinadh Bhojanapalli;Ankit Singh Rawat;Sashank Reddi

  • Why are Adaptive Methods Good for Attention Models

    Jingzhao Zhang;Sai Praneeth Karimireddy;Andreas Veit;Seungyeon Kim

  • Can gradient clipping mitigate label noise

    Aditya Krishna Menon;Ankit Singh Rawat;Sashank J. Reddi;Sanjiv Kumar

  • Variance Reduction in Stochastic Gradient Langevin Dynamics

    Kumar Avinava Dubey;Sashank J. Reddi;Sinead A. Williamson;Barnabas Poczos

  • AdaCliP: Adaptive Clipping for Private SGD.

    Venkatadheeraj Pichapati;Ananda Theertha Suresh;Felix X. Yu;Sashank J. Reddi

  • Fast stochastic optimization on Riemannian manifolds.

    Hongyi Zhang;Sashank J. Reddi;Suvrit Sra

Frequent Co-Authors

Sanjiv Kumar
Sanjiv Kumar Google (United States)
Barnabás Póczos
Barnabás Póczos Carnegie Mellon University
Alexander J. Smola
Alexander J. Smola Amazon (United States)
Satyen Kale
Satyen Kale Google (United States)
Aditya Krishna Menon
Aditya Krishna Menon Google (United States)
Felix X. Yu
Felix X. Yu Google (United States)
Aarti Singh
Aarti Singh Carnegie Mellon University
Larry Wasserman
Larry Wasserman Carnegie Mellon University
Mehryar Mohri
Mehryar Mohri Google (United States)

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