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2025
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Computer Science
Germany
2026

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Best Scientists

D-Index
182
Citations
233981
World Ranking
560
National Ranking
21

Computer Science

D-Index
180
Citations
213304
World Ranking
11
National Ranking
2

Research.com Recognitions

  • 2026 - Research.com Computer Science in Germany Leader Award
  • 2025 - Research.com Best Scientists Award
  • 2025 - Research.com Computer Science in Germany Leader Award
  • 2023 - Research.com Computer Science in Germany Leader Award
  • 2022 - Research.com Computer Science in Germany Leader Award
  • 2019 - BBVA Foundation Frontiers of Knowledge Award
  • 2017 - ACM Fellow For contributions to the theory and practice of machine learning
  • 2016 - German National Academy of Sciences Leopoldina - Deutsche Akademie der Naturforscher Leopoldina – Nationale Akademie der Wissenschaften Informatics
  • 2011 - Max Planck Research Award Intelligent systems
  • 2006 - IAPR J. K. Aggarwal Prize "For advancing the field of kernel methods and showing its wide applicability to pattern recognition problems."
  • Member of the European Academy of Sciences and Arts
  • Member of the European Academy of Sciences and Arts
  • Member of the European Academy of Sciences and Arts
  • Member of the European Academy of Sciences and Arts

Overview

Bernhard Schölkopf is affiliated with the Max Planck Institute for Intelligent Systems in Germany. Their research contributions are deeply embedded in the field of computer science, with a particular focus on artificial intelligence and machine learning.

The scientist's recent publications include:

  • Towards Total Recall in Industrial Anomaly Detection, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Toward Causal Representation Learning, 2021, Proceedings of the IEEE
  • A whole-body FDG-PET/CT Dataset with manually annotated Tumor Lesions, 2022, Scientific Data
  • Causality for Machine Learning, 2022, ACM eBooks
  • A Survey of Algorithmic Recourse: Contrastive Explanations and Consequential Recommendations, 2022, ACM Computing Surveys

Frequently collaborating co-authors include:

  • Julius von Kügelgen (47 joint publications)
  • Francesco Locatello (40 joint publications)
  • Stefan Bauer (25 joint publications)
  • Mrinmaya Sachan (23 joint publications)
  • Michel Besserve (22 joint publications)

Publications are often featured in venues such as:

  • arXiv (Cornell University) with 278 publications
  • Zenodo (CERN European Organization for Nuclear Research) with 14 publications
  • Repository for Publications and Research Data (ETH Zurich) with 4 publications
  • Proceedings of the AAAI Conference on Artificial Intelligence with 4 publications
  • bioRxiv (Cold Spring Harbor Laboratory) with 4 publications

Their scholarly work covers a range of interdisciplinary subfields, including:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Molecular Biology
  • Statistics and Probability
  • Control and Systems Engineering

Key research topics associated with their work include:

  • Domain Adaptation and Few-Shot Learning
  • Bayesian Modeling and Causal Inference
  • Explainable Artificial Intelligence (XAI)
  • Adversarial Robustness in Machine Learning
  • Reinforcement Learning in Robotics
  • Generative Adversarial Networks and Image Synthesis
  • Machine Learning and Algorithms

Bernhard Schölkopf is also the author of the book Handbook of Statistical Bioinformatics, published by Springer International Publishing in 2022.

The awards and recognitions received include:

  • BBVA Foundation Frontiers of Knowledge Award, 2019
  • ACM Fellow, 2017, for contributions to the theory and practice of machine learning
  • German National Academy of Sciences Leopoldina - Deutsche Akademie der Naturforscher Leopoldina - Nationale Akademie der Wissenschaften, 2016 (Informatics)
  • Max Planck Research Award, 2011 (Intelligent systems)
  • IAPR J. K. Aggarwal Prize, 2006, for advancing the field of kernel methods and showing its wide applicability to pattern recognition problems
  • Member of the European Academy of Sciences and Arts

Best Publications

  • Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond

    Bernhard Scholkopf;Alexander J. Smola

  • A tutorial on support vector regression

    Alex J. Smola;Bernhard Schölkopf

  • Nonlinear component analysis as a kernel eigenvalue problem

    Bernhard Schölkopf;Alexander Smola;Klaus-Robert Müller

  • Estimating the Support of a High-Dimensional Distribution

    Bernhard Schölkopf;John C. Platt;John C. Shawe-Taylor;Alex J. Smola

  • Support vector machines

    M.A. Hearst;S.T. Dumais;E. Osman;J. Platt

  • Semi-Supervised Learning (Chapelle, O. et al., Eds.; 2006) [Book reviews]

    O. Chapelle;B. Scholkopf;A. Zien

  • Advances in kernel methods: support vector learning

    Bernhard Schölkopf;Christopher J. C. Burges;Alexander J. Smola

  • Learning with kernels

    Bernhard Schölkopf

  • An introduction to kernel-based learning algorithms

    K.-R. Muller;S. Mika;G. Ratsch;K. Tsuda

  • Learning with Local and Global Consistency

    Dengyong Zhou;Olivier Bousquet;Thomas N. Lal;Jason Weston

  • New Support Vector Algorithms

    Bernhard Schölkopf;Alex J. Smola;Robert C. Williamson;Peter L. Bartlett

  • Fisher discriminant analysis with kernels

    S. Mika;G. Ratsch;J. Weston;B. Scholkopf

  • Kernel Principal Component Analysis

    Bernhard Schölkopf;Alex J. Smola;Klaus-Robert Müller

  • A kernel two-sample test

    Arthur Gretton;Karsten M. Borgwardt;Malte J. Rasch;Bernhard Schölkopf

  • Support vector learning

    B Schölkopf

  • Kernel methods in machine learning

    Thomas Hofmann;Bernhard Schölkopf;Alexander J. Smola

  • Support Vector Method for Novelty Detection

    Bernhard Schölkopf;Robert C Williamson;Alex J. Smola;John Shawe-Taylor

  • A Kernel Method for the Two-Sample-Problem

    Arthur Gretton;Karsten M. Borgwardt;Malte Rasch;Bernhard Schölkopf

  • A Generalized Representer Theorem

    Bernhard Schölkopf;Bernhard Schölkopf;Ralf Herbrich;Ralf Herbrich;Alex J. Smola

  • Correcting Sample Selection Bias by Unlabeled Data

    Jiayuan Huang;Arthur Gretton;Karsten M. Borgwardt;Bernhard Schölkopf

  • Greedy Layer-Wise Training of Deep Networks

    Bernhard Schölkopf;John Platt;Thomas Hofmann

  • WILLIAMSON, ESTIMATING THE SUPPORT OF A HIGH-DIMENSIONAL DISTRIBUTION

    B Scholkopf;J C Platt;J Shawe Taylor

  • Analysis of Representations for Domain Adaptation

    Bernhard Schölkopf;John Platt;Thomas Hofmann

Frequent Co-Authors

Thomas Hofmann
Thomas Hofmann ETH Zurich
John Platt
John Platt Google (United States)
Alexander J. Smola
Alexander J. Smola Amazon (United States)
Dominik Janzing
Dominik Janzing Amazon (United States)
Arthur Gretton
Arthur Gretton University College London
Gunnar Rätsch
Gunnar Rätsch ETH Zurich
Jan Peters
Jan Peters Technical University of Darmstadt
Kun Zhang
Kun Zhang Carnegie Mellon University
Stefan Harmeling
Stefan Harmeling TU Dortmund University
Felix A. Wichmann
Felix A. Wichmann University of Tübingen

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