World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
76
Citations
39035
World Ranking
1314
National Ranking
75

Peter A. Flach 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 Peter A. Flach 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: 349 publications — 82nd percentile

82% of scientists in this discipline score the same or lower.

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

Peter A. Flach 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 Peter A. Flach 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: 76 D-Index — 91st percentile

91% of scientists in this discipline score the same or lower.

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

Overview

Peter A. Flach is affiliated with the University of Bristol in the United Kingdom. Their research primarily focuses on the field of Computer Science, with significant contributions in Artificial Intelligence and related subfields.

The scientist's work spans several subfields including Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems and Management, Signal Processing, and Surgery. Their research topics cover Explainable Artificial Intelligence (XAI), Adversarial Robustness in Machine Learning, Machine Learning and Data Classification, Anomaly Detection Techniques and Applications, Context-Aware Activity Recognition Systems, Data Stream Mining Techniques, and Scientific Computing and Data Management.

Frequent coauthors collaborating with Peter A. Flach include Raúl Santos-Rodríguez, Kacper Sokol, Miquel Perelló-Nieto, Taku Yamagata, and Emma L. Tonkin.

They have published extensively in several venues. The most frequent publication venues include arXiv (Cornell University), which accounts for 19 publications, Machine Learning with 2 publications, as well as venues such as Zenodo (CERN European Organization for Nuclear Research), KI - Künstliche Intelligenz, and the IEEE Journal of Biomedical and Health Informatics.

Notable recent papers authored or coauthored by Peter A. Flach are:

  • One Explanation Does Not Fit All, 2020, KI - Künstliche Intelligenz
  • Classifier calibration: a survey on how to assess and improve predicted class probabilities, 2023, Machine Learning
  • Human Activity Recognition Based on Dynamic Active Learning, 2020, IEEE Journal of Biomedical and Health Informatics
  • FAT Forensics: A Python Toolbox for Implementing and Deploying Fairness, Accountability and Transparency Algorithms in Predictive Systems, 2020, The Journal of Open Source Software
  • One Explanation Does Not Fit All: The Promise of Interactive Explanations for Machine Learning Transparency, 2020, arXiv (Cornell University)

Best Publications

  • Advances in Neural Information Processing Systems 28

    Peter A Flach;Meelis Kull

  • Machine Learning: The Art and Science of Algorithms that Make Sense of Data

    Peter Flach

  • Evaluation Measures for Multi-class Subgroup Discovery

    Tarek Abudawood;Peter Flach

  • On Graph Kernels: Hardness Results and Efficient Alternatives

    Thomas Gärtner;Thomas Gärtner;Peter A. Flach;Stefan Wrobel

  • Multi-Instance Kernels

    Thomas Gärtner;Peter A. Flach;Adam Kowalczyk;Alex J. Smola

  • Rule Evaluation Measures: A Unifying View

    Nada Lavrac;Peter A. Flach;Blaz Zupan

  • Subgroup Discovery with CN2-SD

    Nada Lavrač;Branko Kavšek;Peter Flach;Ljupčo Todorovski

  • FACE: Feasible and Actionable Counterfactual Explanations

    Rafael Poyiadzi;Kacper Sokol;Raul Santos-Rodriguez;Tijl De Bie

  • Abduction and Induction

    Peter A. Flach;Antonis C. Kakas

  • Proceedings of the 28th International Conference on Machine Learning

    José Hernández-Orallo;Peter A Flach;Cesar Ferri

  • Propositionalization approaches to relational data mining

    Stefan Kramer;Nada Lavrač;Peter Flach

  • Learning Decision Trees Using the Area Under the ROC Curve

    César Ferri;Peter A. Flach;José Hernández-Orallo

  • CRISP-DM Twenty Years Later: From Data Mining Processes to Data Science Trajectories

    Fernando Martinez-Plumed;Lidia Contreras-Ochando;Cesar Ferri;Jose Hernandez-Orallo

  • The geometry of ROC space: understanding machine learning metrics through ROC isometrics

    Peter A. Flach

  • Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence

    Yu Chen;Tom Diethe;Peter A Flach

  • Precision-Recall-Gain curves: PR analysis done right

    Peter A. Flach;Meelis Kull

  • ROC 'n' rule learning: towards a better understanding of covering algorithms

    Johannes Fürnkranz;Peter A. Flach

  • Abduction and induction: essays on their relation and integration

    Peter A Flach;Antonis C Kakas

  • Bridging e-Health and the Internet of Things: The SPHERE Project

    Ni Zhu;Tom Diethe;Massimo Camplani;Lili Tao

  • Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS 2017)

    Meelis Kull;Telmo De Menezes E Silva Filho;Peter A Flach

  • Improved Dataset Characterisation for Meta-learning

    Yonghong Peng;Peter A. Flach;Carlos Soares;Pavel Brazdil

  • Machine Learning: ECML 2001

    Luc De Raedt;Peter Flach

  • Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration

    Meelis Kull;Miquel Perello Nieto;Markus Kängsepp;Telmo Silva Filho

Frequent Co-Authors

Nada Lavrač
Nada Lavrač Jozef Stefan Institute
José Hernández-Orallo
José Hernández-Orallo Universitat Politècnica de València
Antonis C. Kakas
Antonis C. Kakas University of Cyprus
Ian J Craddock
Ian J Craddock University of Bristol
Tijl De Bie
Tijl De Bie Ghent University
Kate Tilling
Kate Tilling University of Bristol
Shaomin Wu
Shaomin Wu University of Kent
Stan Matwin
Stan Matwin Dalhousie University
Johannes Fürnkranz
Johannes Fürnkranz Johannes Kepler University of Linz
Debbie A. Lawlor
Debbie A. Lawlor University of Bristol

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