Prof. Dr. Fred Hamprecht

Heidelberg Collaboratory for Image Processing (HCI)
Interdisciplinary Center for Scientific Computing (IWR) and Department of Physics and Astronomy
Heidelberg University
69120 Heidelberg, Germany

Phone: +49 6221-54 14803
Fax:      +49 6221-54 5276
Email:  fred.hamprecht@iwr.uni-heidelberg.de

FIELDS OF INTEREST

Machine learning (active, weakly supervised, structured) for automated image analysis
Bioimage Analysis

CURRENTLY FUNDED PROJECTS

DFG: Morphodynamik der Pflanzen
DFG: A higher level for neuron reconstruction
DFG: Verbessertes Zell-Tracking dank Berücksichtigung von falsch negativen und falsch positiven Detektionen, von Zellteilungen und von Bewegungsmodellen höherer Ordnung
EU: Human Brain Project
Baden-Württemberg Stiftung: An integrated high throughput and super-resolution platform for the fluorescence microscopic analysis of miRNA targets in live cells
DFG: Research Training Group 1653 on Spatio/Temporal Probabilistic Graphical Models and Applications in Image Analysis
DFG / Excellence Initiative: Postdoctoral position from the Excellence Cluster Cellular Networks; PhD positions from the Heidelberg Graduate School of Mathematical and Computational Methods for the Sciences
Howard Hughes Medical Institute, Janelia Farm Research Campus: software development for ilastik (interactive learning and segmentation toolkit)

AWARDS & HONORS

2016, 2018, 2019 ISBI 2012, SNDEMI3D and CREMI connectomics segmentation challenges: Top of leaderboard
2015 40 Leading Scientists under 40 (“Capital” Magazine)
2014 MICCAI Brain Tumor Segmentation Challenge (BraTS 2014): first prize (with J. Kleesiek, G. Urban et al.)
2013 DAGM 2013 award (jointly with C. Strähle, U. Köthe)
2012 Machine Learning in Medical Imaging MLMI 2012 best paper award (jointly with X. Lou, L. Fiaschi, U. Köthe)
2008 DAGM 2008 award (jointly with B. Andres, U. Köthe, M. Helmstädter, W. Denk)
2007 Sonderpreis IT & LifeScience: bwcon 2007 (jointly with B. H. Menze, B. M. Kelm, C. Zechmann)
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2014 – 2015 Weston Visiting Professor in the Dept. of Applied Mathematics and Computer Science at the Weizmann Institute for Science and Technology, Rehovot
2012 – present Head of visitor research project, HHMI Janelia Farm Research Campus
2010 – 2011 Fellow at the Marsilius Kolleg, Heidelberg University
2008 – present Full Professor for Image Analysis and Learning, Heidelberg University
2008 Promotion to Full Professor
2007 – 2012 Scientific consulting for Heidelberger Druckmaschinen AG
2006 – 2011 Affiliated Professor in the Pathology Department, Children´s Hospital Boston
2001 – present Scientific consulting for Robert Bosch GmbH
2001 – present Professor for Multidimensional Image Processing, Heidelberg University
2001 Postdoctoral work at Seminar for Statistics, ETH Zurich, Switzerland
1998 – 2001 Graduate studies, ETH Zurich, Switzerland
1993 – 1998 Studies of Chemistry at ETH Zurich, EPF Lausanne, Imperial College, University of Cambridge

2019 – present PI, Excellence Cluster “STRUCTURES”
2015 Area Chair: Computer Vision and Pattern Recognition
2013-2015 Head, CellNetworks “MathClinic” image processing core facility
2008 – present Co-founder and Director, Heidelberg Collaboratory for Image Processing (HCI)
2007 – present Principal Investigator of the Excellence Initiative Heidelberg Graduate School of Mathematical and Computational Methods for the Sciences
2007 – 2012 Principal Investigator, “Zukunftskonzept” of Heidelberg University within the German Excellence Initiative
2006 – 2008 Coordinator, Technical Platform of “Viroquant” Research Initiative for Systems Biology of Virus-Cell Interactions
2004 – 2010 Steering Committee of “Deutsche Arbeitsgemeinschaft für Mustererkennung”

Wolf, S, Bailoni, A, Pape, C, Rahaman, N, Kreshuk, A, Köthe, U and Hamprecht, FA (2020). The Mutex Watershed and its Objective: Efficient, Parameter-Free Graph Partitioning. IEEE Transactions on pattern analysis and machine intelligence.

Berg, S, Kutra, D, …, Hamprecht, FA and Kreshuk, A (2019). Ilastik: interactive machine learning for (bio) image analysis. Nature Methods, 16(12), pp.1226-1232.

Draxler, F, Veschgini, K, Salmhofer, M and Hamprecht, F (2018), July. Essentially no barriers in neural network energy landscape. In International conference on machine learning (pp. 1309-1318).

Weiler, M., Hamprecht, FA and Storath, M (2018). Learning steerable filters for rotation equivariant cnns. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 849-858).

Beier, T, Pape, C, Rahaman, N, Prange, T, Berg, S, Bock, DD, Cardona, A, Knott, GW, Plaza, SM, Scheffer, LK, Koethe, U, Kreshuk, A, Hamprecht, FA (2017). Multicut brings automated neurite segmentation closer to human performance. Nature Methods, 14(2), pp.101-102.

Kappes J, Andres B, Hamprecht FA, Schnörr C, Nowozin S, Batra D, Kim S, Kausler BX, Kröger T, Lellmann J, Komodakis N, Savchynskyy B, Rother C (2015). A comparative study of modern inference techniques for discrete energy minimization problems. Intern J Computer Vision. 1-30

Schiegg M, Hanslovsky P, Haubold C, Köthe U, Hufnagel L, Hamprecht FA (2015). Graphical model for joint segmentation and tracking of multiple dividing cells. Bioinformatics. 31(6):948-56

Andres B, Kröger T, Briggman L, Denk W, Korogod N, Knott G, Köthe U, Hamprecht FA (2012). Globally optimal closed-surface segmentation for connectomics. ECCV. 778-791

Lou X, Hamprecht FA (2011). Structured learning for cell tracking. NIPS. 1296-1304.

Hamprecht FA, Cohen AJ, Tozer DJ Handy NC (1998). Development and assessment of new exchange-correlation functionals. The J Chem Physics. 109(15):6264-6271

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