Fred Hamprecht

Professor for Image Analysis and Learning
Heidelberg Collaboratory for Image Processing (HCI)
Interdisciplinary Center for Scientific Computing (IWR) and
Department of Physics and Astronomy
Heidelberg University
Im Neuenheimer Feld 205, 04.312; D-69120 Heidelberg
Tel: +49 6221 5414803; Lab Manager Barbara Werner: +49 6221 5414833
fred.hamprecht@iwr.uni-heidelberg.de
Lab with Fred, 11.2018
Hi! I develop machine learning algorithms for image analysis. More specifically, I am interested in principled methods that ingest images or video, and output "structured" predictions, such as the partitioning of an image into its constituent parts, or the tracking of all objects in a video. Most of our applications are in the life sciences; for instance, we try and segment all cells in a brain or a plant, or track virus components before assembly. My focus is on methods that have a sound mathematical background, such as combinatorial optimization or algebraic graph theory, while being widely applicable and useful in practice. I attach particular importance to methods that can be trained with few examples, or by weak supervision, to empower colleagues from other disciplines to analyze their own data, conveniently. To the same end, I have launched the ilastik program for interactive machine learning (paper) that is now headed by Anna Kreshuk.

I enjoy, and feel most privileged, to be able to work on things unknown, and to teach the next generation of scientists and engineers.

I am proud that several past PhD students continue to serve in research and education, including

  • Anna Kreshuk, Group Leader at EMBL
  • Bjoern Andres, Global Head of AI Research at the Bosch Center for Artificial Intelligence, and Honorary Professor at Tübingen University
  • Bjoern Menze, Professor at TU Munich
  • Bernhard Renard, Director at Robert-Koch Institute for Infectious Diseases and Professor at FU Berlin
Luckily, I am blessed with a fantastic lab whose members happen to be both extremely gifted and nice.

News

10.2019 After a decennial effort, ilastik has become a potent tool for (bio-) image analysis. Development, including a "deep" version, is now headed by Anna Kreshuk. A new Perspective in Nature Methods summarizes the status quo.

09.2019 We have held the top spots in the ISBI and CREMI connectomics segmentation challenges almost continuously for three years. Kudos to Constantin Pape, Steffen Wolf, Thorsten Beier, Nasim Rahaman & co!

08.2019 Back from a fantastic sabbatical at Uppsala University. Thanks to Carolina Wahlby for hosting me!

09.2018 Thomas Hehn and I have won a best paper award in GCPR 2018 for our work on "Differentiable Decision Trees". Update: An extended version is now published by IJCV.

06.2018 Ullrich Koethe has been promoted to Adjunct Professor. Congratulations!

Publications

List of publications by me (Google Scholar) or the entire lab (hosted locally).

Selected publications:

  • Multicut brings automated neurite segmentation closer to human performance. Beier, T, Pape, C, Rahaman, N, Prange, T, Berg, S, Bock, D, Cardona, A, Knott, G W, Plaza, S M, Scheffer, L K, Köthe, U, Kreshuk, A and Hamprecht, F A.
    Nature Methods (2017) [10.1038/nmeth.4151]
  • A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems J. H. Kappes, B. Andres, F. A. Hamprecht, C. Schnörr, S. Nowozin, D. Batra, S. Kim, B. X. Kausler, T. Kröger, J. Lellmann, N. Komodakis, B. Savchynskyy, C. Rother.
    IJCV 2015 [10.1007/s11263-015-0809-x | Technical Report | BibTeX]
  • Graphical Model for Joint Segmentation and Tracking of Multiple Dividing Cell M. Schiegg, P. Hanslovsky, C. Haubold, U. Köthe, L. Hufnagel, F. A. Hamprecht.
    Bioinformatics 2015 [10.1093/bioinformatics/btu764 | Technical Report | URL | BibTeX]
  • Globally Optimal Closed-Surface Segmentation for Connectomics B. Andres, T. Kröger, K. L. Briggmann, W. Denk, N. Norogod, G. Knott, U. Köthe, F. A. Hamprecht
    ECCV 2012 [10.1007/978-3-642-33712-3_56 | Technical Report | BibTeX]
  • Optimal lattices for sampling H. R. Künsch, E. Agrell, F. A. Hamprecht IEEE Transactions on Information Theory, (2005) [10.1109/TIT.2004.840864]
  • Development and assessment of new exchange-correlation functionals F. A. Hamprecht, A. J. Cohen, D. J. Tozer, N. C. Handy Journal of Chemical Physics, (1998) [10.1063/1.477267]

Current Teaching

Winter semester 2019: Machine Learning

Some video lectures

Machine Learning for Computer Vision (Winter Term 2017)

  • 1. Introduction
  • 2. Undirected Probabilistic Graphical Models
  • 2.1 MAP & Priors
  • 2.2 Markov Random Fields
  • 2.3 Gibbs Sampling
  • 2.4 MRF as Integer Linear Program (I)
  • 2.5 MRF as Integer Linear Program (II)
  • 2.6 Tree-Shaped MRF
  • 2.7 Belief Propagation
  • 2.8 Gaussian MRF (I)
  • 2.9 Gaussian MRF (II)
  • 3. Neural Networks
  • 3.1 Perceptrons
  • 3.2 Back Propagation
  • 3.3 Introduction to Deep Learning
  • 3.4 Deep Learning Architectures
  • 3.5 Natural Gradient Optimization (I)
  • 3.6 Natural Gradient Optimization (II)
  • 3.7 Combining Graphical Models & Neural Networks (I)
  • 3.8 Combining Graphical Models & Neural Networks (II)
  • 4. Directed Probabilistic Graphical Models
  • 4.1 Reinforcement Learning
  • 4.2 Policy Gradient
  • 6.3 Robotics

    Pattern Recognition Video Lectures (Summer Term 2012)

    Full playlist here.

    Image Analysis Video Lectures (Summer Term 2013 / 2015).

    Full playlist here, including some added material (scroll to last entries).