World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
75
Citations
27008
World Ranking
1395
National Ranking
725

Jeff A. Bilmes 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 Jeff A. Bilmes 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: 365 publications — 84th percentile

84% of scientists in this discipline score the same or lower.

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

Jeff A. Bilmes 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 Jeff A. Bilmes 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: 75 D-Index — 90th percentile

90% of scientists in this discipline score the same or lower.

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

Overview

Jeff A. Bilmes is affiliated with the University of Washington in the United States. Their research primarily falls within the field of Computer Science, with significant contributions across several specialized subfields.

The subfields of study in which they have been active include:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Molecular Biology
  • Spectroscopy
  • Computational Theory and Mathematics

The scientist's main topics of work reflect a diverse intersection of computational methods and biological applications. These topics include:

  • Advanced Image and Video Retrieval Techniques
  • Complexity and Algorithms in Graphs
  • Algorithms and Data Compression
  • Adversarial Robustness in Machine Learning
  • Domain Adaptation and Few-Shot Learning
  • Advanced Proteomics Techniques and Applications
  • Mass Spectrometry Techniques and Applications

Jeff A. Bilmes has published extensively, with a frequent presence in venues such as arXiv (Cornell University), Nature Communications, and the Proceedings of the AAAI Conference on Artificial Intelligence. Among their recent papers are:

  • "DIAmeter: matching peptides to data-independent acquisition mass spectrometry data," 2021, Bioinformatics
  • "PRISM: A Rich Class of Parameterized Submodular Information Measures for Guided Data Subset Selection," 2022, Proceedings of the AAAI Conference on Artificial Intelligence
  • "Submodular Combinatorial Information Measures with Applications in Machine Learning," 2020, arXiv (Cornell University)
  • "Submodularity In Machine Learning and Artificial Intelligence," 2022, arXiv (Cornell University)
  • "ChromaFold predicts the 3D contact map from single-cell chromatin accessibility," 2024, Nature Communications

Collaboration is a significant aspect of their work. Frequent co-authors include:

  • Rishabh Iyer
  • William Stafford Noble
  • Gantavya Bhatt
  • Arnav Das
  • Vianne R. Gao

Publications span both computational and biological sciences, illustrating a cross-disciplinary approach. The venues where their work appears range from preprint archives such as arXiv to high-impact journals like Nature Communications.

Best Publications

  • A gentle tutorial of the em algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models

    J. A. Bilmes

  • An integrated encyclopedia of DNA elements in the human genome

    Ian Dunham;Anshul Kundaje;Shelley F. Aldred;Patrick J. Collins

  • Deep Canonical Correlation Analysis

    Galen Andrew;Raman Arora;Jeff Bilmes;Karen Livescu

  • Integrative annotation of chromatin elements from ENCODE data

    Michael M. Hoffman;Jason Ernst;Jason Ernst;Jason Ernst;Steven P. Wilder;Anshul Kundaje;Anshul Kundaje

  • A Class of Submodular Functions for Document Summarization

    Hui Lin;Jeff Bilmes

  • Unsupervised pattern discovery in human chromatin structure through genomic segmentation

    Michael M. Hoffman;Orion J. Buske;Jie Wang;Zhiping Weng

  • Optimizing matrix multiply using PHiPAC: a portable, high-performance, ANSI C coding methodology

    Jeff Bilmes;Krste Asanovic;Chee-Whye Chin;Jim Demmel

  • On Deep Multi-View Representation Learning

    Weiran Wang;Raman Arora;Karen Livescu;Jeff Bilmes

  • Optimizing matrix multiply using PHiPAC: a portable, high-performance, ANSI C coding methodology

    Unknown

  • Multi-document Summarization via Budgeted Maximization of Submodular Functions

    Hui Lin;Jeff Bilmes

  • On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks

    Sunil Thulasidasan;Gopinath Chennupati;Jeff A. Bilmes;Tanmoy Bhattacharya

  • Factored language models and generalized parallel backoff

    Jeff A. Bilmes;Katrin Kirchhoff

  • What HMMs Can Do

    Jeff A. Bilmes

  • Transmembrane topology and signal peptide prediction using dynamic bayesian networks.

    Sheila M. Reynolds;Lukas Käll;Michael E. Riffle;Jeff A. Bilmes

  • Submodularity in Data Subset Selection and Active Learning

    Kai Wei;Rishabh Iyer;Jeff Bilmes

  • The graphical models toolkit: An open source software system for speech and time-series processing

    Jeff Bilmes;Geoffrey Zweig

  • MVA Processing of Speech Features

    Chia-Ping Chen;J.A. Bilmes

  • Submodularity beyond submodular energies: Coupling edges in graph cuts

    Stefanie Jegelka;Jeff Bilmes

  • Submodular Optimization with Submodular Cover and Submodular Knapsack Constraints

    Rishabh K Iyer;Jeff A Bilmes

  • Graphical models and automatic speech recognition

    Jeffrey A. Bilmes

  • Learning Mixtures of Submodular Functions for Image Collection Summarization

    Sebastian Tschiatschek;Rishabh K Iyer;Haochen Wei;Jeff A Bilmes

Frequent Co-Authors

William Stafford Noble
William Stafford Noble University of Washington
Katrin Kirchhoff
Katrin Kirchhoff Amazon (United States)
James A. Landay
James A. Landay Stanford University
Zhiping Weng
Zhiping Weng University of Massachusetts Chan Medical School
Karen Livescu
Karen Livescu Toyota Technological Institute at Chicago
James Demmel
James Demmel University of California, Berkeley
Dieter Fox
Dieter Fox University of Washington
Tanzeem Choudhury
Tanzeem Choudhury Cornell University
Nelson Morgan
Nelson Morgan International Computer Science Institute

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring computer science in the USA opens many doors for flexible learning and high-demand careers. For those seeking a quick start, consider short certificate programs that pay well. These certifications can boost your resume quickly and help you enter the tech workforce without a lengthy commitment.

If you’re aiming to fast-track your education, you might look into programs offering the shortest masters degree options online. These accelerated degrees are perfect for students who want to gain advanced knowledge and credentials in a condensed time frame.

For long-term career growth, pursuing one of the masters degrees that are worth it in the tech industry can make you stand out and qualify for top roles. These degrees are highly respected by employers and continue to be in great demand.

Additionally, online associate degree programs provide a solid and affordable foundation in computer science. This route is ideal for those looking to build essential skills before advancing to higher degrees or technical certifications.

Best Scientists Citing Jeff A. Bilmes

Trending Scientists

Recently Published Articles