Office: N4.019

Max-Planck-Ring 4

72076 Tübingen

Germany

Max-Planck-Ring 4

72076 Tübingen

Germany

+49 7071 601 551

+49 7071 601 552

My scientific interests are in the field of machine learning and inference from empirical data. In particular, I study kernel methods for extracting regularities from possibly high-dimensional data. These regularities are usually statistical ones, however, in recent years I have also become interested in methods for finding causal structures that underly statistical dependences. I have worked on a number of different applications of machine learning - in our field, you get "to play in everyone's backyard." Most recently, I have been trying to play in the backyard of astronomers and photographers.

I am heading the Department of Empirical Inference; take a look at our last formal **Research Overview** and **Alumni List**.

Many of my papers can downloaded if you click on the tab "publications;" alternatively, from arxiv or from http://www.kernel-machines.org/. Some additional links:

- We have written a book about causality that was just published as an open access title at MIT Press (PDF, with Jonas Peters and Dominik Janzing).
- Photographs: view of the Alps from the southern black forest, a rainbow in La Palma, a lunar eclipse in 2007, the Andromeda galaxy, the Milky Way on the Roque de los Muchachos, the North America Nebula, the constellation Orion with Barnard's loop, and finally a picture of a beautiful northern light, which I took a few years ago from the plane, on the way home from a conference in Vancouver. I always try to get a window seat when flying home from the North American west coast - it is surprizingly common to see northern lights. Looking at the night sky is a fascinating and humbling experience.
- Some chapters of our book Learning with Kernels.
- Review paper on kernel methods in the Annals of Statistics.
- Short high-level introduction on statistical learnig theory (in German) that appeared in the 2004 Jahrbuch of the Max Planck Society.
- Obituary for Alexej Chervonenkis (NIPS 2014).
- I am a member of the LIGO scientific collaboration to detect gravitational waves
- With the growing interest in (how to make money with) big data, machine learning has significantly gained in popularity. We have published an article in the German newspaper
*FAZ*, discussing some of the implications.*Disclaimer: the text that appears above our names was neither written nor approved by us.* - A children's book
- I do not engage in military research, and I believe AI/ML should not be used for aggressive military purposes. Open letter against autonomous AI weapons / open letter against a military collaboration of KAIST, with positive outcome / IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems

Machine Learning Causal Inference Artificial Intelligence Computational Photography Statistics

- M.Sc. in mathematics and Lionel Cooper Memorial Prize, University of London (1992)
- Diplom in physics (Tübingen, 1994)
- doctorate in computer science from the Technical University Berlin (1997); thesis on Support Vector Learning (main advisor: V. Vapnik, AT&T Bell Labs) won the annual dissertation prize of the German Association for Computer Science (GI)
- scientific member of the Max Planck Society, 2001
- awards won by his lab
- J. K. Aggarwal Prize of the International Association for Pattern Recognition, 2006
- Max Planck Research Award, 2011
- Academy Prize of the Berlin-Brandenburg Academy of Sciences and Humanities, 2012
- Royal Society Milner Award, 2014
- Member of the German National Academy of Science (Leopoldina)
- Fellow of the ACM (Association for Computing Machinery)
- Gottfried-Wilhelm-Leibniz-Preis of the German Science Foundation (2018)
- Honorarprofessor at the Technical University Berlin (computer science) and at the Eberhard-Karls University Tübingen (physics)
- list of publications as of January 2015
- "ISI highly cited" (added in 2010)
- the h Index for Computer Science
- Google Scholar page
- co-editor-in-chief of JMLR
- member of the boards of the NIPS foundation and of the International Machine Learning Society
- PC member (e.g., NIPS, COLT, ICML, UAI, DAGM, CVPR, Snowbird Learning Workshop) and co-chair of various conferences (COLT'03, DAGM'04, NIPS'05, NIPS'06 and the first two kernel workshops).
- co-founder of the Machine Learning Summer Schools
- two-page CV: PDF.

If you'd like to **contact** me, please consider these two notes:

*1. I recently became co-editor-in-chief of JMLR. I work for JMLR because I believe in its open access model, but it takes a lot of time. During my JMLR term, please don't convince me to do other journal or grant reviewing duties.*

*2. I am not very organized with my e-mail so if you want to apply for a position in my lab, please send your application only to Sekretariat-Schoelkopf@tuebingen.mpg.de. Note that we do not respond to non-personalized applications that look like they are being sent to a large number of places simultaneously.*

We are always happy to receive outstanding applications for **PhD positions **and **postdocs**.

684 results
(View BibTeX file of all listed publications)

**Robust EEG Channel Selection Across Subjects for Brain Computer Interfaces**
*EURASIP Journal on Applied Signal Processing*, 2005(19, Special Issue: Trends in Brain Computer Interfaces):3103-3112, (Editors: Vesin, J. M., T. Ebrahimi), 2005 (article)

**Implicit Surface Modelling as an Eigenvalue Problem**
In *Proceedings of the 22nd International Conference on Machine Learning*, pages: 937-944, (Editors: L De Raedt and S Wrobel), ACM, New York, NY, USA, ICML, 2005 (inproceedings)

**The human brain as large margin classifier**
*Proceedings of the Computational & Systems Neuroscience Meeting (COSYNE)*, 2, pages: 1, 2005 (poster)

**Attentional Modulation of Auditory Event-Related Potentials in a Brain-Computer Interface**
In *BioCAS04*, (S3/5/INV- S3/17-20):4, IEEE Computer Society, Los Alamitos, CA, USA, 2004 IEEE International Workshop on Biomedical Circuits and Systems, December 2004 (inproceedings)

**Efficient face detection by a cascaded support-vector machine expansion**
*Proceedings of The Royal Society of London A*, 460(2501):3283-3297, A, November 2004 (article)

**Joint Kernel Maps**
(131), Max-Planck-Institute for Biological Cybernetics, Tübingen, November 2004 (techreport)

**Pattern detection methods and systems and face detection methods and systems**
United States Patent, No 6804391, October 2004 (patent)

**Pattern Recognition: 26th DAGM Symposium, LNCS, Vol. 3175**
*Proceedings of the 26th Pattern Recognition Symposium (DAGM‘04)*, pages: 581, Springer, Berlin, Germany, 26th Pattern Recognition Symposium, August 2004 (proceedings)

**Kernel Methods in Computational Biology**
pages: 410, Computational Molecular Biology, MIT Press, Cambridge, MA, USA, August 2004 (book)

**Prediction on Spike Data Using Kernel Algorithms**
In *Advances in Neural Information Processing Systems 16*, pages: 1367-1374, (Editors: S Thrun and LK Saul and B Schölkopf), MIT Press, Cambridge, MA, USA, 17th Annual Conference on Neural Information Processing Systems (NIPS), June 2004 (inproceedings)

**Ranking on Data Manifolds**
In *Advances in neural information processing systems 16*, pages: 169-176, (Editors: S Thrun and L Saul and B Schölkopf), MIT Press, Cambridge, MA, USA, 17th Annual Conference on Neural Information Processing Systems (NIPS), June 2004 (inproceedings)

**Support Vector Channel Selection in BCI**
*IEEE Transactions on Biomedical Engineering*, 51(6):1003-1010, June 2004 (article)

**Learning with Local and Global Consistency**
In *Advances in Neural Information Processing Systems 16*, pages: 321-328, (Editors: S Thrun and LK Saul and B Schölkopf), MIT Press, Cambridge, MA, USA, 17th Annual Conference on Neural Information Processing Systems (NIPS), June 2004 (inproceedings)

**Advances in Neural Information Processing Systems 16: Proceedings of the 2003 Conference**
*Proceedings of the Seventeenth Annual Conference on Neural Information Processing Systems (NIPS 2003)*, pages: 1621, MIT Press, Cambridge, MA, USA, 17th Annual Conference on Neural Information Processing Systems (NIPS), June 2004 (proceedings)

**Learning to Find Pre-Images**
In *Advances in Neural Information Processing Systems 16*, pages: 449-456, (Editors: S Thrun and LK Saul and B Schölkopf), MIT Press, Cambridge, MA, USA, 17th Annual Conference on Neural Information Processing Systems (NIPS), June 2004 (inproceedings)

**Kernel Hebbian Algorithm for single-frame super-resolution**
In *Computer Vision - ECCV 2004, LNCS vol. 3024*, pages: 135-149, (Editors: A Leonardis and H Bischof), Springer, Berlin, Germany, 8th European Conference on Computer Vision (ECCV), May 2004 (inproceedings)

**A Compression Approach to Support Vector Model Selection**
*Journal of Machine Learning Research*, 5, pages: 293-323, April 2004 (article)

**Human Classification Behaviour Revisited by Machine Learning**
7, pages: 134, (Editors: Bülthoff, H.H., H.A. Mallot, R. Ulrich and F.A. Wichmann), 7th T{\"u}bingen Perception Conference (TWK), Febuary 2004 (poster)

**Selective Attention to Auditory Stimuli: A Brain-Computer Interface Paradigm**
7, pages: 102, (Editors: Bülthoff, H.H., H.A. Mallot, R. Ulrich and F.A. Wichmann), 7th T{\"u}bingen Perception Conference (TWK), February 2004 (poster)

**EEG Channel Selection for Brain Computer Interface Systems Based on Support Vector Methods**
7, pages: 50, (Editors: Bülthoff, H.H., H.A. Mallot, R. Ulrich and F.A. Wichmann), 7th T{\"u}bingen Perception Conference (TWK), February 2004 (poster)

**Experimentally optimal v in support vector regression for different noise models and parameter settings**
*Neural Networks*, 17(1):127-141, January 2004 (article)

**Efficient Approximations for Support Vector Machines in Object Detection**
In *DAGM 2004*, pages: 54-61, (Editors: CE Rasmussen and HH Bülthoff and B Schölkopf and MA Giese), Springer, Berlin, Germany, Pattern Recognition, Proceedings of the 26th DAGM Symposium, 2004 (inproceedings)

**Kernel Methods for Manifold Estimation**
In *Proceedings in Computational Statistics*, pages: 441-452, (Editors: J Antoch), Physica-Verlag/Springer, Heidelberg, Germany, COMPSTAT, 2004 (inproceedings)

**A Regularization Framework for Learningfrom Graph Data**
In *ICML Workshop on Statistical Relational Learning and Its Connections to Other Fields*, pages: 132-137, ICML, 2004 (inproceedings)

**Multivariate Regression with Stiefel Constraints**
(128), MPI for Biological Cybernetics, Spemannstr 38, 72076, Tuebingen, 2004 (techreport)

**A kernel view of the dimensionality reduction of manifolds**
In *Proceedings of the Twenty-First International Conference on Machine Learning*, pages: 369-376, (Editors: CE Brodley), ACM, New York, NY, USA, ICML, 2004, also appeared as MPI-TR 110 (inproceedings)

**Protein Functional Class Prediction with a Combined Graph**
In *Proceedings of the Korean Data Mining Conference*, pages: 200-219, Proceedings of the Korean Data Mining Conference, 2004 (inproceedings)

**Learning from Labeled and Unlabeled Data Using Random Walks**
In *Pattern Recognition, Proceedings of the 26th DAGM Symposium*, pages: 237-244, (Editors: Rasmussen, C.E., H.H. Bülthoff, M.A. Giese and B. Schölkopf), Pattern Recognition, Proceedings of the 26th DAGM Symposium, 2004 (inproceedings)

**Learning from Labeled and Unlabeled Data Using Random Walks**
Max Planck Institute for Biological Cybernetics, 2004 (techreport)

**A Tutorial on Support Vector Regression**
*Statistics and Computing*, 14(3):199-222, 2004 (article)

**Multivariate Regression via Stiefel Manifold Constraints**
In *Lecture Notes in Computer Science, Vol. 3175*, pages: 262-269, (Editors: CE Rasmussen and HH Bülthoff and B Schölkopf and MA Giese), Springer, Berlin, Germany, Pattern Recognition, Proceedings of the 26th DAGM Symposium, 2004 (inproceedings)

**Implicit estimation of Wiener series**
In *Machine Learning for Signal Processing XIV, Proc. 2004 IEEE Signal Processing Society Workshop*, pages: 735-744, (Editors: A Barros and J Principe and J Larsen and T Adali and S Douglas), IEEE, New York, Machine Learning for Signal Processing XIV, Proc. IEEE Signal Processing Society Workshop, 2004 (inproceedings)

**Behaviour and Convergence of the Constrained Covariance**
(130), MPI for Biological Cybernetics, 2004 (techreport)

**Statistische Lerntheorie und Empirische Inferenz**
*Jahrbuch der Max-Planck-Gesellschaft*, 2004, pages: 377-382, 2004 (misc)

**Feature Selection for Support Vector Machines Using Genetic Algorithms**
*International Journal on Artificial Intelligence Tools (Special Issue on Selected Papers from the 15th IEEE International Conference on Tools with Artificial Intelligence 2003)*, 13(4):791-800, 2004 (article)

**Semi-supervised kernel regression using whitened function classes**
In *Pattern Recognition, Proceedings of the 26th DAGM Symposium, Lecture Notes in Computer Science, Vol. 3175*, LNCS 3175, pages: 18-26, (Editors: CE Rasmussen and HH Bülthoff and MA Giese and B Schölkopf), Springer, Berlin, Gerrmany, 26th DAGM Symposium, 2004 (inproceedings)

**A Primer on Kernel Methods**
In *Kernel Methods in Computational Biology*, pages: 35-70, (Editors: B Schölkopf and K Tsuda and JP Vert), MIT Press, Cambridge, MA, USA, 2004 (inbook)

**Transductive Inference with Graphs**
Max Planck Institute for Biological Cybernetics, 2004, See the improved version Regularization on Discrete Spaces. (techreport)

**Implicit Wiener series for capturing higher-order interactions in
images**
*Sensory coding and the natural environment*, (Editors: Olshausen, B.A. and M. Lewicki), 2004 (poster)

**Classification and Memory Behaviour of Man Revisited by Machine**
*CSHL Meeting on Computational & Systems Neuroscience (COSYNE)*, 2004 (poster)

**Support Vector Channel Selection in BCI**
(120), Max Planck Institute for Biological Cybernetics, Tuebingen, Germany, December 2003 (techreport)

**Learning Theory and Kernel Machines: 16th Annual Conference on Learning Theory and 7th Kernel Workshop (COLT/Kernel 2003), LNCS Vol. 2777**
*Proceedings of the 16th Annual Conference on Learning Theory and 7th Kernel Workshop (COLT/Kernel 2003)*, *COLT/Kernel 2003*, pages: 746, Springer, Berlin, Germany, 16th Annual Conference on Learning Theory and 7th Kernel Workshop, November 2003, Lecture Notes in Computer Science ; 2777 (proceedings)

**Cluster Kernels for Semi-Supervised Learning**
In *Advances in Neural Information Processing Systems 15*, pages: 585-592, (Editors: S Becker and S Thrun and K Obermayer), MIT Press, Cambridge, MA, USA, 16th Annual Conference on Neural Information Processing Systems (NIPS), October 2003 (inproceedings)

**Kernel Dependency Estimation**
In *Advances in Neural Information Processing Systems 15*, pages: 873-880, (Editors: S Becker and S Thrun and K Obermayer), MIT Press, Cambridge, MA, USA, 16th Annual Conference on Neural Information Processing Systems (NIPS), October 2003 (inproceedings)

**Statistical Learning Theory, Capacity and Complexity**
*Complexity*, 8(4):87-94, July 2003 (article)

**Ranking on Data Manifolds**
(113), Max Planck Institute for Biological Cybernetics, 72076 Tuebingen, Germany, June 2003 (techreport)

**Kernel Hebbian Algorithm for Iterative Kernel Principal Component Analysis**
(109), MPI f. biologische Kybernetik, Tuebingen, June 2003 (techreport)

**Learning with Local and Global Consistency**
(112), Max Planck Institute for Biological Cybernetics, Tuebingen, Germany, June 2003 (techreport)

**Dealing with large Diagonals in Kernel Matrices**
*Annals of the Institute of Statistical Mathematics*, 55(2):391-408, June 2003 (article)

**Implicit Wiener Series**
(114), Max Planck Institute for Biological Cybernetics, June 2003 (techreport)