Professor for Computer Science
Head of Explainable Machine Learning research group
Co-Head of Visual Learning Lab
Heidelberg University
SCIENTIFIC VITA
| 1986 – 1991 | Diploma studies in Physics, Rostock University, Germany |
| 1991 | Research Assistant, University Hospital, Rostock, Germany |
| 1992 – 1999 | Research Associate, Fraunhofer Institute for Computer Graphics, Rostock, Germany |
| 1993 | Guest Researcher at David Sarnoff Research Center, Princeton, USA |
| 1999 – 2000 | PhD Student, Cognitive Systems Lab, Hamburg University, Germany |
| 2000 | PhD (Dr. rer. nat.) in Computer Science, Hamburg University, Germany |
| 2000 – 2007 | Assistant Professor, Cognitive Systems Lab, Hamburg University, Germany |
| 2004 | Guest Researcher at the Computational Vision and Active Perception Laboratory, Royal Institute of Technology, Stockholm, and the Computer Vision Laboratory, Linköping University, Sweden |
| 2008 | Habilitation in Computer Science, Hamburg University, Germany |
| 2007 – 2017 | Vice Head of Image Analysis and Learning Lab, Heidelberg University, Germany |
| Since 2017 | Head of Explainable Machine Learning research group Co-Head of Visual Learning Lab, Heidelberg University, Germany |
| Since 2018 | Adjunct Professor (außerplanmäßiger Professor) for Computer Science, Heidelberg University, Germany |
ACADEMIC SERVICES
Reviewer for 10+ Journals/conferences, including IEEE PAMI, International Journal on Computer Vision, Image and Vision Computing, Pattern Recognition Letters, GCPR, DGCI, ICCV, ECCV, CVPR
Program Committee Member for German Conference on Pattern Recognition (since 2007), Discrete Geometry for Computer Imagery (2009, 2011), Advanced Concepts for Intelligent Vision Systems (2005), International Conference on Computer Vision Theory and Applications (2006, 2007)
Head of Technical Committee 18 “Discrete Geometry” of the International Association for Pattern Recognition (2008-2011)
Organizer of workshop “Applications of Discrete Geometry and Mathematical Morphology” (2010) and International IWR Summer School ” Machine Learning with Applications in Natural- and Life Sciences” (2019)
TEACHING
Lecture “Fundamentals of Machine Learning” (2014, 2015, 2017, 2018)
Lecture “Advanced Machine Learning” (2015, 2016, 2018, 2019)
Lecture “Algorithms and Data Structures” (2008, 2012, 2014, 2017, 2020)
Lecture “Introduction to Computer Science” (2016)
Course “Introduction to Programming” (2009, 2014, 2015)
Course “A Crash Course in Machine Learning” (2019)
Tutorial “Normalizing Flows and Invertible Neural Networks in Computer Vision” (2020)
Seminars on “How do I lie with statistics?”, “Artificial intelligence for games”, “Explainable machine learning”, “Is artificial intelligence dangerous?”, “Algorithms for big data”
AWARDS
| 1993 | Stipendium zur Förderung des wissenschaftlichen Nachwuchses in den neuen Bundesländern |
| 2003 | DAGM 2003 Main Prize |
| 2008 | DAGM 2008 Award (jointly with B. Andres, F. Hamprecht, M. Helmstädter, W. Denk) |
| 2012 | Machine Learning in Medical Imaging MLMI 2012 best paper award (jointly with X. Lou, L. Fiaschi, F. Hamprecht) |
| 2013 | DAGM 2013 Award (jointly with C. Strähle, F. Hamprecht) |
RELEVANT PUBLICATIONS
Dr. Köthe has published >100 papers (h-index: 32, i10-index: 60, overall citation count: 3740; source: google scholar). See https://scholar.google.de/citations?user=gt-yaNMAAAAJ for a complete publication list.
List of ten representative publications:
1. Sorrenson P, Rother C, Köthe U: “Disentanglement by Nonlinear ICA with General Incompressible-flow Networks (GIN)”, In: Intl. Conf. on Learning Representations (ICLR), arXiv:2001.04872, 2020
2. Ardizzone, L, Kruse, J, Wirkert, S, Rahner, D, Pellegrini, E W, Klessen, R S, Maier-Hein, L, Rother, C, and Köthe, U: “Analyzing inverse problems with invertible neural networks”, In: Intl. Conf. on Learning Representations (ICLR), arXiv:1808.04730, 2019
3. Radev, S T, Mertens, U K, Voss, A, and Köthe, U: “Towards end‐to‐end likelihood‐free inference with convolutional neural networks”, British Journal of Mathematical and Statistical Psychology, doi:10.1111/bmsp.12159, 2019
4. Berg, S, Kutra, D, Kroeger, T, Straehle, C N, Kausler, B X, Haubold, C, Schiegg, M, Ales, J, Beier, T, Rudy, M, Eren, K, Cervantes, J, Xu, B, Beuttenmueller, F, Wolny, A, Zhang, C, Koethe, U, Hamprecht, F A, and Kreshuk A: “ilastik: interactive machine learning for (bio) image analysis”, Nature Methods, doi:10.1038/s41592-019-0582-9, 2019
5. Wolf, S, Pape, C, Bailoni, A, Rahaman, N, Kreshuk, A, Köthe, U, and Hamprecht, F A: “The mutex watershed: efficient, parameter-free image partitioning”, In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 546-562, 2018
6. Andres, B, Kröger, T, Briggmann, K L, Denk, W, Norogod, N, Knott, G, Köthe, U and Hamprecht, F A: “Globally Optimal Closed-Surface Segmentation for Connectomics”. 12th Eur. Conf. Computer Vision (ECCV 2012), Springer LNCS 7574, pp. 778-791, 2012.
7. Sommer, C, Straehle, C N, Köthe, U, Hamprecht, F A: “ilastik: Interactive learning and segmentation toolkit”, 8th Intl. Symp. Biomedical Imaging (ISBI 2011), pp. 230-233, 2011.
8. Menze, B, Kelm, B H, Splitthoff, N, Köthe, U and Hamprecht, F A: “On oblique random forests”, Mach. Learning and Knowledge Discovery in Databases, Springer LNCS 6912, pp. 453-469, 2011.
9. Stelldinger, P, Köthe, U: “Towards a general sampling theory for shape preservation”, Image and Vision Computing, 23(2): 237-248, 2005.
10. Köthe, U: “Edge and Junction Detection with an Improved Structure Tensor”, in: B. Michaelis, G. Krell (Eds.): Proc. of 25th DAGM Symposium, Springer LNCS 2781, pp. 25-32, 2003.