World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
35
Citations
7427
World Ranking
11504
National Ranking
4729

Trishul Chilimbi 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 Trishul Chilimbi 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: 97 publications — 8th percentile

8% of scientists in this discipline score the same or lower.

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

Trishul Chilimbi 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 Trishul Chilimbi 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: 35 D-Index — 20th percentile

20% of scientists in this discipline score the same or lower.

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

Overview

Trishul Chilimbi is affiliated with Amazon in the United States, where their research primarily focuses on the field of computer science. Their contributions span a significant number of publications in various subfields and topics mainly related to artificial intelligence and machine learning.

Within computer science, Chilimbi's work covers the following subfields:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications
  • Information Systems
  • Materials Chemistry

Their research addresses diverse topics including:

  • Multimodal Machine Learning Applications
  • Topic Modeling
  • Natural Language Processing Techniques
  • Domain Adaptation and Few-Shot Learning
  • Advanced Image and Video Retrieval Techniques
  • Machine Learning and Extreme Learning Machines (ELM)
  • Stochastic Gradient Optimization Techniques

Chilimbi has contributed to several publications, with a notable presence in both conference proceedings and preprint archives. Recent papers include:

  • Vision-Language Pre-Training with Triple Contrastive Learning, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Multi-modal Alignment using Representation Codebook, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Vision-Language Pre-Training with Triple Contrastive Learning, 2022, arXiv (Cornell University)
  • MiCS, 2022, Proceedings of the VLDB Endowment
  • MiCS: Near-linear Scaling for Training Gigantic Model on Public Cloud, 2022, arXiv (Cornell University)

The frequent venues where their work has appeared include:

  • arXiv (Cornell University)
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Proceedings of the VLDB Endowment
  • Companion Proceedings of the Web Conference 2022

Chilimbi collaborates regularly with several researchers, with the most frequent coauthors being:

  • Belinda Zeng
  • Yi Xu
  • Son N. Tran
  • Li-Qun Chen
  • Tal Neiman

Best Publications

  • Project Adam: building an efficient and scalable deep learning training system

    Trishul Chilimbi;Yutaka Suzue;Johnson Apacible;Karthik Kalyanaraman

  • Green: a framework for supporting energy-conscious programming using controlled approximation

    Woongki Baek;Trishul M. Chilimbi

  • Cache-conscious structure layout

    Trishul M. Chilimbi;Mark D. Hill;James R. Larus

  • Vision-Language Pre-Training with Triple Contrastive Learning

    Unknown

  • SPEED: precise and efficient static estimation of program computational complexity

    Sumit Gulwani;Krishna K. Mehra;Trishul Chilimbi

  • Cache-conscious structure definition

    Trishul M. Chilimbi;Bob Davidson;James R. Larus

  • HOLMES: Effective statistical debugging via efficient path profiling

    Trishul M. Chilimbi;Ben Liblit;Krishna Mehra;Aditya V. Nori

  • Low-overhead memory leak detection using adaptive statistical profiling

    Matthias Hauswirth;Trishul M. Chilimbi

  • Dynamic hot data stream prefetching for general-purpose programs

    Trishul M. Chilimbi;Martin Hirzel

  • Web search using mobile cores: quantifying and mitigating the price of efficiency

    Vijay Janapa Reddi;Benjamin C. Lee;Trishul Chilimbi;Kushagra Vaid

  • Using generational garbage collection to implement cache-conscious data placement

    Trishul M. Chilimbi;James R. Larus

  • Efficient representations and abstractions for quantifying and exploiting data reference locality

    Trishul M. Chilimbi

  • Inferring locks for atomic sections

    Sigmund Cherem;Trishul Chilimbi;Sumit Gulwani

  • An efficient profile-analysis framework for data-layout optimizations

    Shai Rubin;Rastislav Bodík;Trishul Chilimbi

  • Data structure partitioning with garbage collection to optimize cache utilization

    Trishul M. Chilimbi;James R. Larus

  • Making pointer-based data structures cache conscious

    T.M. Chilimbi;M.D. Hill;J.R. Larus

  • Cache-conscious coallocation of hot data streams

    Trishul M. Chilimbi;Ran Shaham

  • Performance Modeling and Scalability Optimization of Distributed Deep Learning Systems

    Feng Yan;Olatunji Ruwase;Yuxiong He;Trishul Chilimbi

  • System and method for using data address sequences of a program in a software development tool

    Trishul M. Chilimbi

  • Dynamic prefetching of hot data streams

    Trishul Chilimbi;Martin Hirzel

  • SPEED: Precise and Efficient Static Estimation of Program Computational Complexity (Full Version)

    Sumit Gulwani;Krishna K. Mehra;Trishul Chilimbi

Frequent Co-Authors

James R. Larus
James R. Larus École Polytechnique Fédérale de Lausanne
Sumit Gulwani
Sumit Gulwani Microsoft (United States)
Chen Ding
Chen Ding University of Rochester
Aditya V. Nori
Aditya V. Nori Microsoft (United States)
Yuxiong He
Yuxiong He Microsoft (United States)
Martín Abadi
Martín Abadi Google (United States)
Galen C. Hunt
Galen C. Hunt Microsoft (United States)
Vijay Janapa Reddi
Vijay Janapa Reddi Harvard University
Mark D. Hill
Mark D. Hill University of Wisconsin–Madison
Todd C. Mowry
Todd C. Mowry Carnegie Mellon University

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