My scientific interests are in the field of inference from empirical data, in particular machine learning and perception. 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 - if you work in data analysis, you get "to play in everyone's backyard." Most recently, I have been trying to play in the backyard of astronomers and photographers.
- The first chapter of our book Learning with Kernels is available online.
- If your interest in machine learning is a mathematical one, you might prefer our review paper on kernel methods in the Annals of Statistics.
- For a general audience, I wrote a short high-level introduction on statistical learnig theory (in German) that appeared in the 2004 Jahrbuch of the Max Planck Society.
- An obituary for Alexej Chervonenkis, one of the founders of statistical learning theory, presented at NIPS 2014.
- 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
- Some 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.
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. In particular, we are looking for PhD students with interests in general machine learning (including kernel methods and causal inference) or computational imaging (photography, astronomy, MR).
- 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
- Honorarprofessor at the Technical University Berlin (computer science) and at the Eberhard-Karls University Tübingen (physics)
- "ISI highly cited" (added in 2010)
- the h Index for Computer Science
- Google Scholar page
- co-editor-in-chief of JMLR
- board member, ACM Books
- 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.
R. Küffner, N. Zach, R. Norel, J. Hawe, D. Schoenfeld, L. Wang, G. Li, L. Fang, L. Mackey, O. Hardiman, M. Cudkowicz, A. Sherman, G. Ertaylan, M. Grosse-Wentrup, T. Hothorn, J. Ligtenberg, JH. Macke, T. Meyer, B. Schölkopf, L. Tran, R. Vaughan, G. Stolovitzky, ML. Leitner
(2015). Crowdsourced analysis of clinical trial data to predict amyotrophic lateral sclerosis progression Nature Biotechnology, 33, 51-57
M. Bensch, S. Martens, S. Halder, J. Hill, F. Nijboer, A. Ramos, N. Birbaumer, M. Bodgan, B. Kotchoubey, W. Rosenstiel, B. Schölkopf, A. Gharabaghi
(2014). Assessing attention and cognitive function in completely locked-in state with event-related brain potentials and epidural electrocorticography Journal of Neural Engineering, 11, (2), 026006
J. Zscheischler, MD. Mahecha, J. Buttlar, S. Harmeling, M. Jung, A. Rammig, JT. Randerson, B. Schölkopf, SI. Seneviratne, E. Tomelleri, S. Zaehle, M. Reichstein
(2014). A few extreme events dominate global interannual variability in gross primary production Environmental Research Letters, 9, (3), 035001
M. Gomez Rodriguez, K. Gummadi, B. Schölkopf
(2014). Quantifying Information Overload in Social Media and its Impact on Social Contagions In: Proceedings of the Eighth International Conference on Weblogs and Social Media, (Ed) E. Adar, P. Resnick, M. De Choudhury, B. Hogan, and A. Oh, AAAI Press, 170-179, ICWSM 2014
H. Daneshmand, M. Gomez Rodriguez, L. Song, B. Schölkopf
(2014). Estimating Diffusion Network Structures: Recovery Conditions, Sample Complexity & Soft-thresholding Algorithm In: Proceedings of the 31st International Conference on Machine Learning, W&CP 32 (1), (Ed) Eric P. Xing and Tony Jebara, JMLR, 793-801, ICML 2014
K. Muandet, B. Sriperumbudur, B. Schölkopf
(2014). Kernel Mean Estimation via Spectral Filtering In: Advances in Neural Information Processing Systems 27, (Ed) Z. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence and K.Q. Weinberger, Curran Associates, Inc., 1–9, 28th Annual Conference on Neural Information Processing Systems (NIPS 2014)
K. Zhang, B. Schölkopf, K. Muandet, Z. Wang, Z. Zhou, C. Persello
(2014). Single-Source Domain Adaptation with Target and Conditional Shift In: Regularization, Optimization, Kernels, and Support Vector Machines, (Ed) JAK Suykens, M Signoretto, and A Argyriou, Chapman and Hall/CRC, Boca Raton, USA, 427-456
T. Schultz, L. Schlaffke, B. Schölkopf, T. Schmidt-Wilcke
(2013). HiFiVE: A Hilbert Space Embedding of Fiber Variability Estimates for Uncertainty Modeling and Visualization (Ed) B Preim, P Rheingans, and H Theisel, Computer Graphics Forum, 32, (3), Blackwell Publishing, Oxford, UK, 121–130, Eurographics Conference on Visualization (EuroVis) 2013
TO. Zander, B. Battes, B. Schölkopf, M. Grosse-Wentrup
(2013). Towards neurofeedback for improving visual attention In: Proceedings of the Fifth International Brain-Computer Interface Meeting: Defining the Future, (Ed) J.d.R. Millán, S. Gao, R. Müller-Putz, J.R. Wolpaw, and J.E. Huggins, Verlag der Technischen Universität Graz, Article ID: 086, ISBN: 978-3-85125-260-6, 5th International Brain-Computer Interface Meeting
D. Lopez-Paz, P. Hennig, B. Schölkopf
(2013). The Randomized Dependence Coefficient In: Advances in Neural Information Processing Systems 26, (Ed) C.J.C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K.Q. Weinberger, 1–9, 27th Annual Conference on Neural Information Processing Systems (NIPS 2013)
M. Grosse-Wentrup, S. Harmeling, T. Zander, J. Hill, B. Schölkopf
(2013). How to Test the Quality of Reconstructed Sources in Independent Component Analysis (ICA) of EEG/MEG Data In: Proceedings of the 3rd International Workshop on Pattern Recognition in NeuroImaging (PRNI), IEEE Xplore Digital Library, 102-105, PRNI 2013
J. Mooij, D. Janzing, B. Schölkopf
(2013). From Ordinary Differential Equations to Structural Causal Models: the deterministic case In: Proceedings of the Twenty-Ninth Conference Annual Conference on Uncertainty in Artificial Intelligence, (Ed) A Nicholson and P Smyth, AUAI Press, Corvallis, Oregon, 440-448, UAI 2013