Simon Kohl
I am working on something new in the AI for biology space.
Previously I co-developed AlphaFold, have built AlphaFold's widely used uncertainty prediction 'pLDDT' and have co-lead DeepMind's protein design team. Before joining DeepMind I was a PhD student at the German Cancer Research Center in Heidelberg, Germany, where I developed generative models for biomedical image segmentation.
Broadly, I am interested in developing deep generative neural networks for natural science and real world applications. My research is often concerned with uncertainty quantification and the generation of multi-modal outputs in problems that allow multiple solutions.
Invited Talks
Fantastic ML x Biology Problems and Where to Find Them
BioML seminar, UC Berkeley, Berkeley, California, November 2023
Cambridge Computer Lab seminar, University of Cambridge, Cambridge, UK, January 2024
DataLearning group seminar, Imperial College, London, UK, February 2024
Biochemistry department seminar, Cal State, Los Angeles, California, March 2024
Highly accurate protein structure prediction with AlphaFold
Protein & Antibody Engineering Summit (PEGS), Barcelona, Spain, November 2022
Francis Crick Institute, London, UK, October 2022
Harvard Evergrande Center for Immunologic Diseases, Cambridge, USA, May 2022, German Cancer Research Center, Heidelberg, Germany, May 2022
MIT guest lecture at Institute for Chemical Engineering, Cambridge, USA, April 2022, London, UK, March 2022
Institute of Computational Biology, Helmholtz Center Munich, Germany, January 2022
A Hierachical Probabilistic U-Net for Modeling Multi-Scale Ambiguities
Oral Presentation @ ML4H NeurIPS Workshop, Vancouver, December 2019
A Probabilistic U-Net for Segmentation of Ambiguous Images
Oxford Applied and Theoretical Machine Learning Group, University of Oxford, February 2019
Spotlight Presentation @ NeurIPS, Montreal, December 2018
Mini-NeurIPS, CSML Seminar Series, University College London, November 2018
NEC Laboratories Europe, Heidelberg, November 2018
Visual Learning Lab, University of Heidelberg, October 2018
Düsseldorf Bio-DataSeminar, University of Düsseldorf, September 2018
From Unstructured Data to Medically Relevant Information
ISMRM Nordic Chapter Meeting, Trondheim, Norway, October 2018
Machine Learning & Ethics
International Journal of Cancer, Editor’s Meeting, Heidelberg, June 2017
Introduction to Generative Adversarial Networks meetup, Karlsruhe, April 2017
Highly accurate protein structure prediction for the human proteome
Kathryn Tunyasuvunakool, Jonas Adler, Zachary Wu, Tim Green, Michal Zielinski, Augustin Žídek, Alex Bridgland, Andrew Cowie, Clemens Meyer, Agata Laydon, Sameer Velankar, Gerard J. Kleywegt, Alex Bateman, Richard Evans, Alexander Pritzel, Michael Figurnov, Olaf Ronneberger, Russ Bates, Simon A. A. Kohl, Anna Potapenko, Andrew J. Ballard, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Ellen Clancy, David Reiman, Stig Petersen, Andrew W. Senior, Koray Kavukcuoglu, Ewan Birney, Pushmeet Kohli, John Jumper & Demis Hassabis
Nature 2021
pdf database blog
Nature 2021
Highly accurate protein structure prediction with AlphaFold
John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, Alex Bridgland, Clemens Meyer, Simon AA Kohl, Andrew J Ballard, Andrew Cowie, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Jonas Adler, Trevor Back, Stig Petersen, David Reiman, Ellen Clancy, Michal Zielinski, Martin Steinegger, Michalina Pacholska, Tamas Berghammer, Sebastian Bodenstein, David Silver, Oriol Vinyals, Andrew W Senior, Koray Kavukcuoglu, Pushmeet Kohli, Demis Hassabis
Nature 2021
pdf code colab slides results blog video
Nature 2021
nnU-Net: A Self-Configuring Method for Deep Learning-based Biomedical Image Segmentation
Fabian Isensee, Paul F. Jäger, Simon A. A. Kohl, Jens Petersen, Klaus H. Maier-Hein
Nature Methods 2020
pdf code
Nature Methods 2020
Contrastive Training for Improved Out-of-Distribution Detection
Jim Winkens, Rudy Bunel, Abhijit Guha Roy, Robert Stanforth, Vivek Natarajan, Joseph R. Ledsam, Patricia MacWilliams, Pushmeet Kohli, Alan Karthikesalingam, Simon Kohl, Taylan Cemgil, S. M. Ali Eslami, Olaf Ronneberger
arXiv 2020
arXiv 2020
A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities
Simon A. A. Kohl, Bernardino Romera-Paredes, Klaus H. Maier-Hein, Danilo Jimenez Rezende, S. M. Ali Eslami, Pushmeet Kohli, Andrew Zisserman, Olaf Ronneberger
Medical Imaging Workshop, NeurIPS 2019
pdf code data+weights oral
Med-NeurIPS 2019
Classification of Cancer at Prostate MRI: Deep Learning versus Clinical PI-RADS Assessment
Patrick Schelb, Simon Kohl, Jan Philipp Radtke, Manuel Wiesenfarth, Philipp Kickingereder, Sebastian Bickelhaupt, Tristan Anselm Kuder, Albrecht Stenzinger, Markus Hohenfellner, Heinz-Peter Schlemmer, Klaus H Maier-Hein and David Bonekamp
RSNA Radiology 2019
pdf code editorial
Radiology 2019
Deep Probabilistic Modeling of Glioma Growth
Jens Petersen, Paul F. Jäger, Fabian Isensee, Simon A. A. Kohl, Ulf Neuberger, Wolfgang Wick, Jürgen Debus, Sabine Heiland, Martin Bendszus, Philipp Kickingereder, Klaus H. Maier-Hein
pdf code
Unsupervised Anomaly Localization using Variational Auto-Encoders
David Zimmerer, Fabian Isensee, Jens Petersen, Simon Kohl, Klaus Maier-Hein
Automated design of deep learning methods for biomedical image segmentation
Fabian Isensee, Jens Petersen, Simon A. A. Kohl, Paul F. Jäger, Klaus H. Maier-Hein
arXiv 2019
pdf code
arXiv 2019
Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection
Paul F Jaeger, Simon AA Kohl, Sebastian Bickelhaupt, Fabian Isensee, Tristan Anselm Kuder, Heinz-Peter Schlemmer, Klaus H Maier-Hein
Machine Learning for Health Workshop, NeurIPS 2019
pdf code
ML4H Workshop
NeurIPS 2019
A Probabilistic U-Net for Segmentation of Ambiguous Images
Simon AA Kohl, Bernardino Romera-Paredes, Clemens Meyer, Jeffrey De Fauw, Joseph R Ledsam, Klaus H Maier-Hein, SM Ali Eslami, Danilo Jimenez Rezende, and Olaf Ronneberger
Neural Information Processing Systems (NeurIPS'18)
pdf spotlight video tf code pytorch re-code
NeurIPS 2018
A Case for the Score: Identifying Image Anomalies using Variational Autoencoder Gradients
David Zimmerer, Jens Petersen, Simon AA Kohl, and Klaus Maier-Hein
Medical Imaging Workshop at NeurIPS 2018
pdf workshop
Med-NeurIPS 2018
nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation
Fabian Isensee, Jens Petersen, Andre Klein, David Zimmerer, Paul F Jaeger, Simon Kohl, Jakob Wasserthal, Gregor Koehler, Tobias Norajitra, Sebastian Wirkert, and Klaus H Maier-Hein
MICCAI 2018 Challenge: Medical Segmentation Decathlon (winning entry)
pdf leaderboard code
Radiomic Machine Learning for Characterization of Prostate Lesions with MRI: Comparison to ADC Values
David Bonekamp*, Simon Kohl*, Manuel Wiesenfarth, Patrick Schelb, Jan Philipp Radtke, Michael Götz, Philipp Kickingereder, Kaneschka Yaqubi, Bertram Hitthaler, Nils Gählert, Tristan Anselm Kuder, Fenja Deister, Martin Freitag, Markus Hohenfellner, Boris A Hadaschik, Heinz-Peter Schlemmer, and Klaus H Maier-Hein
RSNA Radiology 2018
pdf editorial
Radiology 2018
Adversarial Networks for the Detection of Aggressive Prostate Cancer
Simon Kohl, David Bonekamp, Heinz-Peter Schlemmer, Kaneschka Yaqubi, Markus Hohenfellner, Boris Hadaschik, Jan-Philipp Radtke, and Klaus Maier-Hein
ML4H Workshop at Neural Information Processing Systems 2017
pdf pdf (long) workshop
ML4H Workshop
NeurIPS 2017
External Projects
The Post Binary
A 3-day conference on Artificial Intelligence in Art & Design held at the Museum Angewandte Kunst Frankfurt am Main
Co-Organizer, November 2018
A Seminar Series on Machine Learning & Artificial Intelligence
Co-Initiator and -Organizer, now Program Committee Member, April 2017 - present
website youtube
Semantic Segmentation of Ambiguous Images
German Cancer Research Center (DKFZ) & Department of Computer Science, Karlsruhe Institute of Technology (KIT).
Advised by Prof. Klaus Maier-Hein & Prof. Rainer Stiefelhagen.
PhD Thesis, December 2019
Dalitz Analysis of B−→D+π−π−
Institute for Experimental Nuclear Physics, Karlsruhe Institute of Technology (KIT). Advised by Prof. Michael Feindt.
Master Thesis, June 2016
Density Functional Theory of Tetrahedrites
Computational Condensed Matter Group, Oregon State University & Institute of Nanotechnology, Karlsruhe Institute of Technology (KIT). Advised by Prof. Guenter Schneider & Prof. Ferdinand Evers.
Bachelor Thesis, July 2014
pdf APS abstract
Main Journal: 2022
Main Conference: 2020, 2019
Workshops: Machine Learning for Structural Biology 2020 (
Main Conference: 2017
Workshops: UNSURE 2019 (
Main Conference: 2019
Main Conference: 2019
© 2023 Simon Kohl