World's Best Scientists 2026 revealed!
Finale Doshi-Velez

Finale Doshi-Velez

D-Index & Metrics

Computer Science

D-Index
50
Citations
12910
World Ranking
5531
National Ranking
2527

Finale Doshi-Velez 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 Finale Doshi-Velez 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: 215 publications — 52nd percentile

52% of scientists in this discipline score the same or lower.

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

Finale Doshi-Velez 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 Finale Doshi-Velez 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: 50 D-Index — 62nd percentile

62% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2018 - Fellow of Alfred P. Sloan Foundation

Overview

Finale Doshi-Velez is affiliated with Harvard University in the United States. Their research primarily spans the field of Computer Science, with a focus on Artificial Intelligence. They have produced 137 publications in this domain, emphasizing specialized subfields such as Epidemiology, Pharmacology, Economics and Econometrics, and Management Science and Operations Research.

Their work engages a range of topics including Explainable Artificial Intelligence (XAI), Machine Learning in Healthcare, Adversarial Robustness in Machine Learning, Sepsis Diagnosis and Treatment, Treatment of Major Depression, Reinforcement Learning in Robotics, and Anomaly Detection Techniques and Applications.

Frequent collaborators include Weiwei Pan, Sonali Parbhoo, Susan A. Murphy, Roy H. Perlis, and Yaniv Yacoby, with co-authorship counts of 22, 17, 15, 11, and 11 respectively.

The scientist's publications are often featured in venues such as arXiv (Cornell University), PubMed, Proceedings of the AAAI Conference on Artificial Intelligence, Journal of Affective Disorders, and The Lancet Digital Health. They have notably contributed 73 papers to arXiv, followed by 8 in PubMed, 4 in AAAI conferences, 3 in the Journal of Affective Disorders, and 2 in The Lancet Digital Health.

Recent papers include:

  • "The myth of generalisability in clinical research and machine learning in health care" (2020, The Lancet Digital Health)
  • "How machine-learning recommendations influence clinician treatment selections: the example of antidepressant selection" (2021, Translational Psychiatry)
  • "Ethical and regulatory challenges of large language models in medicine" (2024, The Lancet Digital Health)
  • "Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report" (2022, arXiv (Cornell University))
  • "Artificial intelligence education: An evidence-based medicine approach for consumers, translators, and developers" (2023, Cell Reports Medicine)

Finale Doshi-Velez was awarded the status of Fellow of the Alfred P. Sloan Foundation in 2018.

Best Publications

  • Towards A Rigorous Science of Interpretable Machine Learning

    Finale Doshi-Velez;Been Kim

  • Do no harm: a roadmap for responsible machine learning for health care.

    Jenna Wiens;Suchi Saria;Mark Sendak;Marzyeh Ghassemi

  • Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing Their Input Gradients

    Andrew Slavin Ross;Finale Doshi-Velez

  • Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations.

    Andrew Slavin Ross;Michael C. Hughes;Finale Doshi-Velez

  • The myth of generalisability in clinical research and machine learning in health care.

    Joseph Futoma;Morgan Simons;Trishan Panch;Finale Doshi-Velez

  • Guidelines for reinforcement learning in healthcare

    Omer Gottesman;Fredrik Johansson;Matthieu Komorowski;Aldo Faisal

  • Unfolding physiological state: mortality modelling in intensive care units

    Marzyeh Ghassemi;Tristan Naumann;Finale Doshi-Velez;Nicole Brimmer

  • Beyond Sparsity: Tree Regularization of Deep Models for Interpretability

    Mike Wu;Michael C. Hughes;Sonali Parbhoo;Maurizio Zazzi

  • A Bayesian framework for learning rule sets for interpretable classification

    Tong Wang;Cynthia Rudin;Finale Doshi-Velez;Yimin Liu

  • Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning

    Stefan Depeweg;José Miguel Hernández-Lobato;Finale Doshi-Velez;Steffen Udluft

  • How machine-learning recommendations influence clinician treatment selections: the example of the antidepressant selection.

    Maia Jacobs;Melanie F. Pradier;Thomas H. McCoy;Roy H. Perlis

  • A Bayesian nonparametric approach to modeling motion patterns

    Joshua Joseph;Finale Doshi-Velez;Albert S. Huang;Nicholas Roy

  • Considerations for Evaluation and Generalization in Interpretable Machine Learning

    Finale Doshi-Velez;Been Kim

  • Designing AI for Trust and Collaboration in Time-Constrained Medical Decisions: A Sociotechnical Lens

    Maia Jacobs;Jeffrey He;Melanie F. Pradier;Barbara Lam

  • An Evaluation of the Human-Interpretability of Explanation

    Isaac Lage;Emily Chen;Jeffrey He;Menaka Narayanan

  • The Infinite Partially Observable Markov Decision Process

    Finale Doshi-velez

  • Learning and policy search in stochastic dynamical systems with Bayesian neural networks

    Stefan Depeweg;José Miguel Hernández-Lobato;Finale Doshi-Velez;Steffen Udluft

  • How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation.

    Menaka Narayanan;Emily Chen;Jeffrey He;Been Kim

  • A Roadmap for a Rigorous Science of Interpretability.

    Finale Doshi-Velez;Been Kim

  • Evaluating Reinforcement Learning Algorithms in Observational Health Settings

    Omer Gottesman;Fredrik D. Johansson;Joshua Meier;Jack Dent

  • Accountability of AI Under the Law: The Role of Explanation

    Finale Doshi-Velez;Mason A. Kortz

Frequent Co-Authors

Roy H. Perlis
Roy H. Perlis Harvard University
Emma Brunskill
Emma Brunskill Stanford University
Been Kim
Been Kim Google (United States)
Volker Roth
Volker Roth University of Basel
George Konidaris
George Konidaris Brown University
Maurizio Zazzi
Maurizio Zazzi University of Siena
Isaac S. Kohane
Isaac S. Kohane Harvard University
José Miguel Hernández-Lobato
José Miguel Hernández-Lobato University of Cambridge

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