[ Scientific Interests | VIGRA | CV | Publications | Teaching ]

## Prof. Dr. Ullrich Köthe

Group Leader in the Visual Learning Lab Heidelberg

Mathematikon B (Berliner Str. 43), level 3, office A119

**Email:** ullrich.koethe (at) iwr.uni-heidelberg.de

**Phone:** +49 6221 54 14834

**Fax:** +49-6221-54 5276

**Address:**

Interdisciplinary Center for Scientific Computing (IWR)

University of Heidelberg

Im Neuenheimer Feld 205

69120 Heidelberg

## Scientific Interests

I’m heading the subgroup on “Explainable Machine Learning”. Explainable learning shall open-up the blackbox of successful *machine learning* algorithms, in particular neural networks, to provide *insight* rather than mere numbers. To this end, we are designing powerful new algorithms on the basis of *invertible neural networks* and apply them to *medicine, image analysis, and the natural and life sciences*.

In addition, I’m interested in generic software bringing state-of-the-art algorithms to the end user and maintain the VIGRA image analysis library.

## Teaching

**By individual arrangement**

- Master and bachelor theses in the field of machine learning and image analysis (Informatik, Scientific Computing, Physics)
- Practicals, creditable for e.g. BSc Informatik (IFP), MSc Informatik (IFM), Physics (WPProj)

**Summer Term 2021**

- Lecture Advanced Machine Learning

**Previous semesters**

- Lecture Fundamentals of Machine Learning (WS 14/15, WS 15/16, WS 17/18, WS 18/19, WS 20/21)
- Lecture Advanced Machine Learning (SS 15, SS 16, SS 18, SS 19)
- Lecture Algorithmen und Datenstrukturen (SS 08, SS 12, SS 14, SS 17, SS 20)
- Lecture Einführung in die Praktische Informatik (WS 16/17)
- Autumn School A Crash Course in Machine Learning with Applications in Natural- and Life Sciences (September 2019)
- Hackathon eLearning Challenge (SS 20)
- Seminars:
- How do I lie with statistics? (WS 19/20)
- Artificial Intelligence for Games (SS 19)
- Explainable Machine Learning (SS 18)
- Ist künstliche Intelligenz gefährlich? (SS 17)
- Algorithms to Analyse Big Data (WS 14/15)

- Programming courses Python (WS 15/16) and C++ (WS 14/15, SS 15)

## Selected Publications

Please refer to my profile at Google Scholar and the DBLP Citation Database for a more complete list. My pre-2010 publications can also be found here.

**Theses:**

- U. Köthe:
*“Reliable Low-Level Image Analysis”*

Habilitation Thesis, Department Informatik, University of Hamburg, 318 pages, Hamburg 2008

Abstract | PDF (10 MB) - U. Köthe:
*“Generische Programmierung für die Bildverarbeitung”*

PhD Thesis, Fachbereich Informatik, Universität Hamburg, 274 pages, Hamburg 2000, ISBN: 3-8311-0239-2. (in German)

Abstract | PDF (12.5 MB)

**Recent and popular papers:**

- P. Sorrenson, C. Rother, U. Köthe:
*“Disentanglement by Nonlinear ICA with General Incompressible-flow Networks (GIN)”*, Intl. Conf. Learning Representations, 2020.

Abstract | PDF - L. Ardizzone, R. Mackowiak, C. Rother, U. Köthe:
*“Exact Information Bottleneck with Invertible Neural Networks: Getting the Best of Discriminative and Generative Modeling”*, arXiv:2001.06448, 2020.

Abstract | PDF - S. Radev, U. Mertens, A. Voss, L. Ardizzone, U. Köthe:
*“BayesFlow: Learning complex stochastic models with invertible neural networks”*, arXiv:2003.06281, 2020.

Abstract | PDF - S. Wolf, A. Bailoni, C. Pape, N. Rahaman, A. Kreshuk, U. Köthe, F.A. Hamprecht:
*“The Mutex Watershed and its Objective: Efficient, Parameter-Free Graph Partitioning”*. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020.

Link | PDF - L. Ardizzone, C. Lüth, J. Kruse, C. Rother, U. Köthe,
*“Guided Image Generation with Conditional Invertible Neural Networks”*, arXiv:1907.02392, 2019.

Abstract | PDF - S. Berg, D. Kutra, …, U. Köthe, F.A. Hamprecht, A. Kreshuk:
*“ilastik: interactive machine learning for (bio)image analysis”*, Nature Methods, vol. 16, pp. 1226–1232, 2019.

Link - L. Ardizzone, J. Kruse, S. Wirkert, D. Rahner, E.W. Pellegrini, R.S. Klessen, L. Maier-Hein, C. Rother, U. Köthe:

*“Analyzing Inverse Problems with Invertible Neural Networks”*

arXiv:1808.04730, Intl. Conf. Learning Representations, 2019.

Abstract | PDF - Stefan T. Radev, Ulf K. Mertens, Andreas Voss, Ullrich Köthe:

*“Towards end‐to‐end likelihood‐free inference with convolutional neural networks”*

British Journal of Mathematical and Statistical Psychology. doi: 10.1111/bmsp.12159, 2019.

Abstract | PDF - S. Wolf, C. Pape, A. Bailoni, N. Rahaman, A. Kreshuk, U. Köthe, F.A. Hamprecht:

*“The Mutex Watershed: Efficient, Parameter-Free Image Partitioning”*

in: Europ. Conf. Computer Vision (ECCV’18), pp. 546-562 , 2018.

Abstract | PDF - S. Wolf, L. Schott, U. Köthe, F.A. Hamprecht:

*“Learned Watershed: End-to-End Learning of Seeded Segmentation”*

in: Intl. Conf. Computer Vision (ICCV’17), pp. 2030-2038, 2017.

Abstract | PDF - C. Sommer, C. Straehle, U. Köthe, F.A. Hamprecht:

*“ilastik: Interactive learning and segmentation toolkit”*

In: IEEE International Symposium on Biomedical Imaging (ISBI), pp. 230-233, 2011.

Abstract | PDF - U. Köthe:
*“Edge and Junction Detection with an Improved Structure Tensor”*

in: B. Michaelis, G. Krell (Eds.): Pattern Recognition, Proc. of 25th DAGM Symposium, Magdeburg 2003, Springer LNCS 2781, pp. 25-32, 2003.

Abstract | PDF –**Awarded the main prize of the German Pattern Recognition Society (DAGM) 2003** - U. Köthe:
*“Integrated Edge and Junction Detection with the Boundary Tensor”*

in: ICCV ‘03, Proc. of 9th Intl. Conf. on Computer Vision, Nice 2003, vol. 1, pp. 424-431, 2003.

Abstract | PDF - B. Andres, U. Köthe, M. Helmstaedter, W. Denk, F.A. Hamprecht:

*“Segmentation of SBFSEM Volume Data of Neural Tissue by Hierarchical Classification”*

in: G. Rigoll (Ed.): Pattern Recognition, Proc. DAGM 2008, Springer LNCS 5096 , pp. 142-152, 2008.

Abstract | BibTeX | PDF –**Received a Best Paper Award from the German Association for Pattern Recognition (DAGM)** - B. Andres, T. Kröger, K. Briggmann, W. Denk, N. Norogod, G. Knott, U. Köthe, F.A. Hamprecht:

*“Globally Optimal Closed-Surface Segmentation for Connectomics”*

in: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (Eds.) : 12th Eur. Conf. Computer Vision (ECCV 2012) part III, Springer LNCS 7574, pp. 778-791, 2012.

Abstract | BibTeX | PDF - T. Beier, B. Andres, U. Köthe, F.A. Hamprecht:

*“An Efficient Fusion Move Algorithm for the Minimum Cost Lifted Multicut Problem”*

in: Leibe, B., Matas, J., Sebe, N., Welling, M. (Eds.) : 14th Eur. Conf. Computer Vision (ECCV 2016), 2016.

Abstract | PDF - U. Köthe:
*“Reusable Software in Computer Vision”*

in: B. Jähne, H. Haussecker, P. Geissler (Eds.): Handbook of Computer Vision and Applications, Volume 3: Systems and Applications, pp. 103-132, San Diego: Academic Press, 1999.

PDF - U. Köthe, M. Felsberg:

*“Riesz-Transforms Versus Derivatives: “On the Relationship Between the Boundary Tensor and the Energy Tensor”*

in: R. Kimmel, N. Sochen, J. Weickert (Eds.): Scale Space and PDE Methods in Computer Vision, Springer LNCS 3459, pp. 179-191, 2005.

Abstract | PDF - A. Kreshuk, U. Köthe, E. Pax, D. Bock, F.A. Hamprecht:

*“Automated Detection of Synapses in Serial Section Transmission Electron Microscopy Image Stacks”*

PLoS ONE 9(2): e87351, 2014.

Abstract | BibTeX | PDF - B. Kausler, M. Schiegg, B. Andres, M. Lindner, U. Köthe, H. Leitte, J. Wittbrodt, L. Hufnagel, F.A. Hamprecht:

*“A discrete chain graph model for 3D+t cell tracking with high misdetection robustness”*

in: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (Eds.) : 12th Eur. Conf. Computer Vision (ECCV 2012) part III, Springer LNCS 7574, pp. 144-157, 2012.

Abstract | BibTeX | PDF - M. Hanselmann, U. Köthe, M. Kirchner, B.Y. Renard, E.R. Amstalden, K. Glunde, R.M.A. Heeren, F.A. Hamprecht:

*“Towards Digital Staining using Imaging Mass Spectrometry and Random Forests”*

Journal of Proteome Research, 8(7):3558-3567, 2009

Abstract | BibTeX | PDF - B. Menze, B. Kelm, N. Splitthoff, U. Köthe, F.A. Hamprecht:

*“On oblique random forests”*

in: Mach. Learning and Knowledge Discovery in Databases, Springer LNCS 6912, pp. 453-469, 2011.

Abstract | PDF - U. Köthe, F. Herrmannsdörfer, I. Kats, F.A. Hamprecht:

*“SimpleSTORM: a fast, self-calibrating reconstruction algorithm for localization microscopy”*

Histochemistry and Cell Biology, 141(6):613–627, 2014.

Abstract | PDF - H. Meine, U. Köthe, P. Stelldinger:

*“A Topological Sampling Theorem for Robust Boundary Reconstruction and Image Segmentation”*

Discrete Applied Mathematics (DGCI Special Issue), 157(3):524-541, 2009.

Abstract | PDF - U. Köthe:
*“What Can We Learn from Discrete Images about the Continuous World?”*

in: D. Coeurjolly, I. Sivignon, L. Tougne, F. Dupont (Eds.): Discrete Geometry for Computer Imagery, Proc. DGCI 2008, Springer LNCS 4992, pp. 4-19, 2008.

Abstract | PDF - P. Stelldinger, U. Köthe:

*“Towards a general sampling theory for shape preservation”*

Image and Vision Computing, Special Issue Discrete Geometry for Computer Vision, 23(2): 237-248, 2005.

Abstract | PDF

## Curriculum Vitae

since 2018 | Associate Professor and group leader in the Visual Learning Lab Heidelberg |

26. Nov. 2008 | Habilitation for a thesis entitled “Reliable Low-Level Image Analysis”, Department of Informatics, University of Hamburg |

2008-2012 | Senior scientist at Heidelberg Collaboratory for Image Processing (HCI) |

2007-2017 | Vice Group Leader of the Image Analysis ans Learning Group (formerly: Multidimensional Image Processing), University of Heidelberg |

Spring semester 2004 | Guest researcher at Computer Vision Laboratory, Linköping University, Sweden |

1999-2007 | Assistant professor (officially: “Hochschulassistent”) in the Cognitive Systems Group, University of Hamburg |

29. Feb. 2000 | Dr. rer. nat. (PhD) for a thesis entitled “Generische Programmierung für die Bildverarbeitung”, Department of Informatics, University of Hamburg |

Spring semester 1993 | Guest researcher at Sarnoff Corporation, Princeton, USA |

1992-1999 | Research assistant at the Fraunhofer Institute for Computer Graphics, Rostock |

1986-1991 | Study of physics at University of Rostock, Diploma thesis on “Mikroskopische Herleitung einer Ratengleichung am Beispiel der Nukleonen-Deuteron-Reaktion” |