I am a PhD student in machine learning under supervision of Max Welling at the QUVA lab of the University of Amsterdam, as well as a Research Associate at Qualcomm AI Research. Currently I am working on the intersection of data-efficient machine learning and geometry.
Previously, I was a masters student Artificial Intelligence in Amsterdam, where I worked on geometric auto-encoders and variational inference. I wrote my thesis on Causal Imitation Learning as visiting scholar at the Robotic AI and Learning Lab at UC Berkeley supervised by Sergey Levine.
Before moving to AI, I studied the inner working of the universe with a masters in theoretical physics at the University of Cambridge and a bachelors in physics from the University of Amsterdam.
I am currently working on geometric deep learning, in which the study of symmetry can lead to practical, principled and data-efficient deep learning methods. Going beyond, I am investigating whether some of that methodology can be generalized from group-spaces to other problem domains, perhaps using category theory. Lastly, I think causality is quite interesting.
Pim de Haan*, Maurice Weiler*, Taco Cohen, Max Welling:
Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs
ICLR 2021 (spotlight) [openreview]
Julian Suk, Pim de Haan, Phillip Lippe, Christoph Brune, Jelmer M. Wolterink
Mesh convolutional neural networks for wall shear stress estimation in 3D artery models
STACOM 2021 [arxiv]
Pim de Haan, Taco Cohen, Max Welling:
Natural Graph Networks
NeurIPS 2020 [arxiv]
Pim de Haan, Dinesh Jayaraman, Sergey Levine: Causal Confusion in Imitation Learning
NeurIPS 2019 (oral, 0.5% of submissions) [arxiv]
Luca Falorsi, Pim de Haan, Tim R. Davidson, Patrick Forré: Reparameterizing Distributions on Lie Groups
AISTATS 2019 (oral) [arxiv]
Miranda C. N. Cheng, Vassilis Anagiannis, Maurice Weiler, Pim de Haan, Taco S. Cohen, Max Welling:
Covariance in Physics and Convolutional Neural Networks
ICML 2019 Workshop on Theoretical Physics for Deep Learning [arxiv]
Pim de Haan*, Luca Falorsi*: Topological Constraints on Homeomorphic Auto-Encoding
NeurIPS 2018 workshop on Integration of Deep Learning Theories [arxiv]
Luca Falorsi*, Pim de Haan*, Tim R. Davidson*, Nicola De Cao, Maurice Weiler, Patrick Forré, Taco S. Cohen: Explorations in Homeomorphic Variational Auto-Encoding
ICML 2018 workshop on Theoretical Foundations and Applications of Deep Generative Models [arxiv]