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.