World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
33
Citations
8789
World Ranking
12395
National Ranking
5024

Rajesh Ranganath 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 Rajesh Ranganath 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: 138 publications — 22nd percentile

22% of scientists in this discipline score the same or lower.

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

Rajesh Ranganath 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 Rajesh Ranganath 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

Rajesh Ranganath is affiliated with New York University in the United States. Their research primarily spans the fields of Computer Science and Medicine, with a substantial focus on Artificial Intelligence and Statistics and Probability. Additional subfields include Radiology, Nuclear Medicine and Imaging, Cardiology and Cardiovascular Medicine, and Computer Vision and Pattern Recognition.

The scientist's work explores several main topics including Machine Learning in Healthcare, Explainable Artificial Intelligence (XAI), Advanced Causal Inference Techniques, Statistical Methods and Inference, ECG Monitoring and Analysis, Radiomics and Machine Learning in Medical Imaging, and Adversarial Robustness in Machine Learning.

Rajesh Ranganath's recent publications cover a variety of themes relevant to these topics. Notable papers include:

  • "The role of machine learning in clinical research: transforming the future of evidence generation," 2021, Trials
  • "Reproducibility in machine learning for health research: Still a ways to go," 2021, Science Translational Medicine
  • "Deep learning models for electrocardiograms are susceptible to adversarial attack," 2020, Nature Medicine
  • "A validated, real-time prediction model for favorable outcomes in hospitalized COVID-19 patients," 2020, npj Digital Medicine
  • "Correction to: The role of machine learning in clinical research: transforming the future of evidence generation," 2021, Trials

The scientist frequently publishes in venues such as arXiv (Cornell University), PubMed, bioRxiv (Cold Spring Harbor Laboratory), Heart Rhythm, and Trials.

Rajesh Ranganath collaborates regularly with several co-authors. Frequent collaborators include:

  • Aahlad Puli
  • Neil Jethani
  • Yindalon Aphinyanaphongs
  • Mukund Sudarshan
  • Larry B. Goldstein

Best Publications

  • Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations

    Honglak Lee;Roger Grosse;Rajesh Ranganath;Andrew Y. Ng

  • Black Box Variational Inference

    Rajesh Ranganath;Sean Gerrish;David M. Blei

  • Automatic differentiation variational inference

    Alp Kucukelbir;Dustin Tran;Rajesh Ranganath;Andrew Gelman

  • ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission

    Kexin Huang;Jaan Altosaar;Rajesh Ranganath

  • Unsupervised learning of hierarchical representations with convolutional deep belief networks

    Honglak Lee;Roger Grosse;Rajesh Ranganath;Andrew Y. Ng

  • A Review of Challenges and Opportunities in Machine Learning for Health

    Marzyeh Ghassemi;Tristan Naumann;Peter Schulam;Andrew L Beam

  • The role of machine learning in clinical research: transforming the future of evidence generation

    E. Hope Weissler;E. Hope Weissler;Tristan Naumann;Tomas Andersson;Rajesh Ranganath

  • Hierarchical variational models

    Rajesh Ranganath;Dustin Tran;David M. Blei

  • Reproducibility in machine learning for health research: Still a ways to go.

    Matthew B. A. McDermott;Shirly Wang;Shirly Wang;Nikki Marinsek;Rajesh Ranganath

  • A Review of Challenges and Opportunities in Machine Learning for Health

    Marzyeh Ghassemi;Tristan Naumann;Peter Schulam;Andrew L. Beam

  • Variational Sequential Monte Carlo

    Christian Andersson Naesseth;Scott Linderman;Rajesh Ranganath;David Blei

  • Automatic variational inference in Stan

    Alp Kucukelbir;Rajesh Ranganath;Andrew Gelman;David M. Blei

  • Hierarchical Implicit Models and Likelihood-Free Variational Inference

    Dustin Tran;Rajesh Ranganath;David M. Blei

  • Deep Exponential Families

    Rajesh Ranganath;Linpeng Tang;Laurent Charlin;David M. Blei

  • Variational Gaussian Process

    Dustin Tran;Rajesh Ranganath;David M. Blei

  • Automatic Differentiation Variational Inference

    Alp Kucukelbir;Dustin Tran;Rajesh Ranganath;Andrew Gelman

  • Risk prediction for chronic kidney disease progression using heterogeneous electronic health record data and time series analysis

    Adler J. Perotte;Rajesh Ranganath;Jamie S. Hirsch;David M. Blei

  • Practical guidance on artificial intelligence for health-care data.

    Marzyeh Ghassemi;Tristan Naumann;Peter Schulam;Andrew L Beam

  • Extracting Social Meaning: Identifying Interactional Style in Spoken Conversation

    Dan Jurafsky;Rajesh Ranganath;Dan McFarland

  • Deep learning models for electrocardiograms are susceptible to adversarial attack.

    Xintian Han;Yuxuan Hu;Luca Foschini;Larry Chinitz

  • Variational Inference via $\chi$ Upper Bound Minimization

    Adji Bousso Dieng;Dustin Tran;Rajesh Ranganath;John W. Paisley

  • Bayesian Nonparametric Poisson Factorization for Recommendation Systems

    Prem Gopalan;Francisco J. R. Ruiz;Rajesh Ranganath;David M. Blei

  • An Adaptive Learning Rate for Stochastic Variational Inference

    Rajesh Ranganath;Chong Wang;Blei David;Eric Xing

  • Dynamic Poisson Factorization

    Laurent Charlin;Rajesh Ranganath;James McInerney;David M. Blei

  • It's Not You, it's Me: Detecting Flirting and its Misperception in Speed-Dates

    Rajesh Ranganath;Dan Jurafsky;Dan McFarland

  • Hierarchical Implicit Models and Likelihood-Free Variational Inference

    Dustin Tran;Rajesh Ranganath;David M. Blei

  • The Variational Gaussian Process

    Dustin Tran;Rajesh Ranganath;David M. Blei

  • Automatic Variational Inference in Stan

    Alp Kucukelbir;Rajesh Ranganath;Andrew Gelman;David M. Blei

Frequent Co-Authors

David M. Blei
David M. Blei Columbia University
Dustin Tran
Dustin Tran Google (United States)
Joan Bruna
Joan Bruna New York University
Luca Foschini
Luca Foschini University of Bologna
Noémie Elhadad
Noémie Elhadad Columbia University
Kenneth A. Norman
Kenneth A. Norman Princeton University
Andrew Gelman
Andrew Gelman Columbia University
John Paisley
John Paisley Columbia University

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