I am a Staff Research Scientist at Isomorphic Labs in Lausanne. Previously I was a Researcher and manager at Apple and founder of the Health AI team Zurich, an AI research team focusing on questions of robustness, generalization, interpretability, mechanistic understanding and in-silico hardware design. Previously, I was the first-ever postdoc at Vector Institute and University of Toronto with Rich Zemel, also collaborating with David Duvenaud and Roger Grosse. Prior to that, I did another postdoc in the lab of Matthias Bethge in Tübingen and was a Ph.D. student at the University of Amsterdam under supervision of Arnold Smeulders. My research mainly focuses on gaining better understanding of open challenges in generative modeling, representation learning and robust decision-making. My background is in physics with a specialization in neuroscience.
I have been working on representation learning with generative models, on invertible neural networks and normalizing flows, on robustness under distribution shift as well as on the connection between invariance and generalization. Currently, I mostly think about ood generalization, (private) representation learning, algorithmic bias and the consequences implied by methods and common practices in AI/ML research.
A. Wehenkel, J. Behrmann, A. Miller, G. Sapiro, O. Sener, M. Cuturi, J.-H. Jacobsen. Simulation-based Inference for Cardiovascular Models. Under Submission. [Paper]
O. Senouf, J. Behrmann, J.-H. Jacobsen, P. Frossard, E. Abbe, A. Wehenkel. Inferring Cardiovascular Biomarkers with Hybrid Model Learning. NeurIPS 2023 Deep Inverse Workshop. [Paper]
S. Di, E. de Bézenac, E. Fox, J.-H. Jacobsen, A. Karpatne, V. Kashtanova, G. Louppe, N. Takeishi, A. Wehenkel. Synergy of Scientific and Machine Learning Modeling ("SynS & ML"). ICML 2023 Workshop Organizer. [Website]
A. Blaas, A. C. Miller, L. Zapella, J.-H. Jacobsen, C. Heinze-Deml. Considerations for Distribution Shift Robustness in Health. ICLR 2023, Workshop on Trustworthy Machine Learning for Healthcare. (ORAL PRESENTATION), Best Paper Honorable Mention. [Paper]
A. Wehenkel, J. Behrmann, H. Hsu, G. Sapiro, G. Louppe, J.-H. Jacobsen. Robust Hybrid Learning With Expert Augmentation. TMLR, 2023. [Paper]
M. Goldstein, J.-H. Jacobsen, O. Chau, A. Saporta, A. Puli, R. Ranganath, A. C. Miller. Learning Invariant Representations with Missing Data. CLeaR, 2022. [Paper]
D. Krueger, E. Caballero, J.-H. Jacobsen, A. Zhang, J. Binas, R. Le Priol, A. Courville. Out-of-Distribution Generalization via Risk Extrapolation (REx). ICML, 2021. (LONG ORAL PRESENTATION) [Paper, Code]
E. Creager, J.-H. Jacobsen, R. Zemel. Environment Inference for Invariant Learning. ICML, 2021. [Paper, Code]
J. Behrmann*, P. Vicol*, K. C. Wang*, Roger Grosse, J.-H. Jacobsen. Understanding and Mitigating Exploding Inverses in Invertible Neural Networks. AISTATS, 2021. [Paper, Code]
R. Geirhos*, J.-H. Jacobsen*, C. Michaelis*, R. Zemel, W. Brendel, M. Bethge, F. Wichmann. Shortcut Learning in Deep Neural Networks. Nature Machine Intelligence, 2020. [Paper, Code]
W. Grathwohl, X. Li, K. Swersky, M. Hashemi, J.-H. Jacobsen, M. Norouzi, G. Hinton. Scaling RBMs to High Dimensional Data with Invertible Neural Networks. ICML INNF+, 2020. [Paper]
S. Zhao, J.-H. Jacobsen, W. Grathwohl. Joint Energy-Based Models for Semi-Supervised Classification. ICML UDL, 2020. [Paper]
F. Tramer, J. Behrmann, N. Carlini, N. Papernot, J.-H. Jacobsen. Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations. ICML, 2020. [Paper, Code]
C. Finlay, J.-H. Jacobsen, L. Nurbekyan, A. Oberman. How To Train Your Neural ODE. ICML, 2020. [Paper]
W. Grathwohl, K. C. Wang, J.-H. Jacobsen, D. Duvenaud, R. Zemel. Cutting out the Middle-Man: Training and Evaluating Energy-Based Models without Sampling. ICML, 2020. [Paper]
W. Grathwohl, K. C. Wang*, J.-H. Jacobsen*, David Duvenaud, Mohammad Norouzi, Kevin Swersky. Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One. ICLR, 2020. (ORAL PRESENTATION) [Paper, Code]
E. Fetaya*, J.-H. Jacobsen*, W. Grathwohl, R. Zemel. Understanding the Limitations of Conditional Generative Models. ICLR, 2020. [Paper]
R. T. Q. Chen, J. Behrmann, D. Duvenaud, J.-H. Jacobsen. Residual Flows for Invertible Generative Modeling. NeurIPS, 2019. (SPOTLIGHT PRESENTATION) [Paper, Code]
Q. Li*, S. Hague*, C. Anil, J. Lucas, R. Grosse, J.-H. Jacobsen. Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks. NeurIPS, 2019. [Paper, Code]
J. Behrmann*, W. Grathwohl*, R. T. Q. Chen, D. Duvenaud, J.-H. Jacobsen*. Invertible Residual Networks. ICML, 2019. (LONG ORAL PRESENTATION) [Paper, Code]
E. Creager, D. Madras, J.-H. Jacobsen, M. Weis, K. Swersky, T. Pitassi, R. Zemel. Flexibly Fair Representation Learning by Disentanglement. ICML, 2019. [Paper]
J.-H. Jacobsen, J. Behrmann, N. Carlini, F. Tramer, N. Papernot. Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness. SafeML Workshop, ICLR 2019. [Paper, Code]
J.-H. Jacobsen, J. Behrmann, R. Zemel, M. Bethge. Excessive Invariance Causes Adversarial Vulnerability. ICLR, 2019. [Paper]
J.-H. Jacobsen, A.W.M. Smeulders, E. Oyallon. i-RevNet: Deep Invertible Networks. ICLR, 2018. [Paper, Code]
J.-H. Jacobsen, B. de Brabandere, A.W.M. Smeulders. Dynamic Steerable Blocks in Deep Residual Networks. BMVC, 2017. [Paper]
J.-H. Jacobsen, E. Oyallon, S. Mallat, A.W.M. Smeulders. Hierarchical Attribute CNNs. ICML PADL, 2017. [Paper, Code]
J.-H. Jacobsen, B. de Brabandere, A.W.M. Smeulders. Dynamic Steerable Frame Networks. Pre-print, 2017. [Paper]
J.-H. Jacobsen, J. v. Gemert, Z. Lou, A.W.M. Smeulders. Structured Receptive Fields in CNNs. CVPR, 2016. [Paper, Code]
J.-H. Jacobsen, A.W.M. Smeulders. Deep Learning for Neuroimage Classification. OHBM, 2015.
J.-H. Jacobsen, J. Stelzer, T. H. Fritz, G. Chételat, R. L. Joie, R. Turner. Why musical memory can be preserved in advanced Alzheimer's disease. BRAIN, 2015. [Paper] [Scientific commentary by Clark and Warren].
R. Turner, J.-H. Jacobsen. What stays when everything goes. OUPblog, 2015. Oxford University Press Blog.
Jörn-Henrik Jacobsen