I moved to the Lab of Prof. Jan Benda. Lastest information, code, notes, and publication can be found on my .
Natural Image Statistics We us the family of Lp-spherically and Lp-nested symmetric distributions to obtain more accurate morels of natural images and quantitatively assess normative hypotheses about the role of orientation selectivity and divisive normalization in the primate early visual system.
UNIVERSVM: A SVM Implementation for Large Scale Transduction and Inference with a Universum The UNIVERSVM is a SVM implementation written in C++. Its functionality comprises large scale transduction (as described in ), sparse solutions (as described in ) and inference with a universum (as described in ).
NEC-Labs ABCDetc letter/digit/symbol dataset (in collaboration and at and at )
We started to collect a dataset consisting of digits, symbols, uppercase and lowercase letters. At the moment it comprises around 50.000 examples.
If you want to contribute, please download the , fill out the columns as indicated, scan it at 300 dpi and email it to ()
Large Scale Optimization (in collaboration with , and at )
We show how the Concave-Convex Procedure can be applied to Transductive SVMs, which traditionally requires solving a combinatorial search problem. This provides for the first time a highly scalable algorithm in the nonlinear case.
Learning Depth from Stereo (Student Research Project) The depth of a point in space can be estimated by observing its image position from two different viewpoints. The classical approach to stereo vision calculates depth from the two projection equations which together form a stereocamera model. An unavoidable preparatory work for this approach is to estimate the parameters of the camera. This can become quite tedious.
In this study, we approached the depth estimation problem from a different point of view by applying generic machine learning algorithms to learn the mapping from image coordinates to spatial position. .
- October 2010 -December 2010: intership in the group of Gilles Laurent at the Max Planck Insitute for Brain Research in Frankfurt a.M.
- March 2007 - January 2012: PhD student in the of
- August 2005 - October 2005: internship at the NEC laboratories in Princeton
- 2002-2007: additional studies in philosophy
- 2000-2007: studies in bioinformatics at the University of Tübingen
- since 2007: PhD student at the junior research group Matthias Bethge (Department for Empirical Inference)
- 2003-2007: Research assistant at the Max-Planck-Institute for Biological Cybernetics, Prof. Schölkopf
- 2002-2003: Tutor at the at the University of Tuebingen, Prof. Rosenstiel
- 2002: Programmer at the at the University of Tuebingen, Prof. Hauck
- 2001-2002: Programmer at the at the University of Tuebingen, Prof. Zell (supervisor Igor Fischer)
Scholarships and Awards
- Best Paper Award at the Iternational Conference for Machine Learning 2006 (ICML 2006) for the paper
- German National Academic Foundation (Studienstiftung des dt. Volkes, January 2008 - February 2010)
- Essential Mathematics for Neuroscience Lecture (Winterterm 2009, with J.-P. Lies), Graduate School of Neural and Behavioural Sciences, University of Tübingen
- Essential Mathematics for Neuroscience Lecture (Winterterm 2008, with J. Macke), Graduate School of Neural and Behavioural Sciences, University of Tübingen
- Essential Mathematics for Neuroscience Lecture (Winterterm 2007, with J. Macke), Graduate School of Neural and Behavioural Sciences, University of Tübingen
- Ethics for Computer Scientists Seminar (Winterterm 2006, with P. Berens and D. Gümbel), University of Tübingen
- Machine Learning and Neuroscience Practical Course (Winterterm 2004, with A. Gretton, Jeremy Hill and Dilan Görür), University of Tübingen