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.

Have a look at my Resume.

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**, 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]