World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
44
Citations
10242
World Ranking
7464
National Ranking
367

Bernd Bischl 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 Bernd Bischl 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: 291 publications — 72nd percentile

72% of scientists in this discipline score the same or lower.

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

Bernd Bischl 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 Bernd Bischl 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: 44 D-Index — 48th percentile

48% of scientists in this discipline score the same or lower.

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

Overview

Bernd Bischl is affiliated with the Ludwig-Maximilians-Universität München in Germany. Their research contributions predominantly lie within the field of Computer Science, with a specialized focus on Artificial Intelligence, Computer Vision and Pattern Recognition, Computational Theory and Mathematics, Statistics and Probability, and Radiology, Nuclear Medicine and Imaging.

The scientist has published extensively, with a total output of over 300 papers in Computer Science and more than 200 in Artificial Intelligence alone. The main research topics in their work include Machine Learning and Data Classification, Explainable Artificial Intelligence (XAI), Advanced Multi-Objective Optimization Algorithms, Metaheuristic Optimization Algorithms Research, Statistical Methods and Inference, Adversarial Robustness in Machine Learning, and Domain Adaptation and Few-Shot Learning.

Frequent co-authors collaborating with Bernd Bischl include David Rügamer, Giuseppe Casalicchio, Florian Pfisterer, Mina Rezaei, and Lennart Schneider.

Common publication venues for Bernd Bischl's research are:

  • arXiv (Cornell University)
  • Data Mining and Knowledge Discovery
  • Proceedings of the Genetic and Evolutionary Computation Conference Companion
  • Lecture Notes in Computer Science
  • Communications in Computer and Information Science

Among recent papers authored or coauthored by Bernd Bischl are:

  • Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges (2023), published in Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery
  • Predicting personality from patterns of behavior collected with smartphones (2020), published in Proceedings of the National Academy of Sciences
  • Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features (2022), published in Computational Statistics
  • mlr3proba: an R package for machine learning in survival analysis (2021), published in Bioinformatics
  • Deep learning for survival analysis: a review (2024), published in Artificial Intelligence Review

Best Publications

  • OpenML: networked science in machine learning

    Joaquin Vanschoren;Jan N. van Rijn;Bernd Bischl;Luis Torgo

  • Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges

    Bernd Bischl;Martin Binder;Michel Lang;Tobias Pielok

  • Benchmark for filter methods for feature selection in high-dimensional classification data

    Andrea Bommert;Xudong Sun;Bernd Bischl;Jörg Rahnenführer

  • mlr: machine learning in R

    Bernd Bischl;Michel Lang;Lars Kotthoff;Julia Schiffner

  • Tunability: Importance of Hyperparameters of Machine Learning Algorithms

    Philipp Probst;Bernd Bischl;Anne-Laure Boulesteix

  • mlr3: A modern object-oriented machine learning framework in R

    Michel Lang;Martin Binder;Jakob Richter;Patrick Schratz

  • Exploratory landscape analysis

    Olaf Mersmann;Bernd Bischl;Heike Trautmann;Mike Preuss

  • Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges

    Christoph Molnar;Giuseppe Casalicchio;Bernd Bischl

  • Predicting personality from patterns of behavior collected with smartphones.

    Clemens Stachl;Quay Au;Ramona Schoedel;Samuel D Gosling;Samuel D Gosling

  • ASlib: A Benchmark Library for Algorithm Selection

    Bernd Bischl;Pascal Kerschke;Lars Kotthoff;Marius Thomas Lindauer

  • Tunability: Importance of Hyperparameters of Machine Learning Algorithms

    Philipp Probst;Anne-Laure Boulesteix;Bernd Bischl

  • Resampling methods for meta-model validation with recommendations for evolutionary computation

    B. Bischl;O. Mersmann;H. Trautmann;C. Weihs

  • Multi-Objective Counterfactual Explanations

    Susanne Dandl;Christoph Molnar;Martin Binder;Bernd Bischl

  • Algorithm selection based on exploratory landscape analysis and cost-sensitive learning

    Bernd Bischl;Olaf Mersmann;Heike Trautmann;Mike Preuß

  • mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions

    Bernd Bischl;Jakob Richter;Jakob Bossek;Daniel Horn

  • Visualizing the Feature Importance for Black Box Models

    Giuseppe Casalicchio;Christoph Molnar;Bernd Bischl

  • An Open Source AutoML Benchmark

    Pieter Gijsbers;Erin LeDell;Janek Thomas;Sébastien Poirier

  • OpenML: A collaborative science platform

    Jan van Rijn;Bernd Bischl;Luis Torgo;Bo Gao

  • Effectiveness of Random Search in SVM hyper-parameter tuning

    Rafael G. Mantovani;Andre L. D. Rossi;Joaquin Vanschoren;Bernd Bischl

  • General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models.

    Christoph Molnar;Gunnar König;Julia Herbinger;Timo Freiesleben

  • Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features.

    Florian Pargent;Florian Pfisterer;Janek Thomas;Bernd Bischl

  • mlr3proba: An R Package for Machine Learning in Survival Analysis.

    Raphael Sonabend;Franz J Király;Andreas Bender;Bernd Bischl

  • batchtools: Tools for R to work on batch systems

    Michel Lang;Bernd Bischl;Dirk Surmann

  • A novel feature-based approach to characterize algorithm performance for the traveling salesperson problem

    Olaf Mersmann;Bernd Bischl;Heike Trautmann;Markus Wagner

  • Robust Anomaly Detection in Images Using Adversarial Autoencoders

    Laura Beggel;Michael Pfeiffer;Bernd Bischl

Frequent Co-Authors

Joaquin Vanschoren
Joaquin Vanschoren Eindhoven University of Technology
Heike Trautmann
Heike Trautmann University of Münster
Markus Bühner
Markus Bühner Ludwig-Maximilians-Universität München
Heinrich Hussmann
Heinrich Hussmann Ludwig-Maximilians-Universität München
Frank Hutter
Frank Hutter University of Freiburg
Günter Rudolph
Günter Rudolph TU Dortmund University
Frank Neumann
Frank Neumann University of Adelaide
Anne-Laure Boulesteix
Anne-Laure Boulesteix Ludwig-Maximilians-Universität München
Markus Wagner
Markus Wagner Monash University
Peter Marwedel
Peter Marwedel TU Dortmund University

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