Hi! I am a PhD candidate at AMLab supervised by Max Welling, Jan-Willem van de Meent and Alfons Hoekstra. I am trying to learn PDEs from data using deep learning. Before joining the University of Amsterdam, I was a research assistant at Helmholtz AI, where I worked on applications of machine learning for material science. I also spent some time working on graph neural networks and generative modelling with applications in neuroscience. Long ago, I developed statistical models of clinical treatment at TU Dresden.
Overall, my research interests revolve around physics-inspired deep learning
and geometric deep learning
. I am also interested in AI4Science and the applications of machine learning to physics.
In my free time, I enjoy reading, playing basketball and testing gravity when skateboarding.
news
Feb 2024 | Together with David Ruhe, Maurice Weiler and others, we have developed Clifford-steerable CNNs - a new class of isometry-equivariant CNNs that are able to operate on pseudo-Euclidean spaces, such as Euclidean space and Minkowski spacetime. Really strong results on PDEs, new relativistic benchmark and lots of very cool math. The paper is available here. |
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Sep 2023 | Our paper “Implicit Convolutional Kernels for Steerable CNNs” has been accepted to NeurIPS 2023! We developed a simple and efficient way to parameterize group equivariant convolutional kernels using implicit neural representation, which greatly simplifies the design of steerable CNNs. Check out the blog post for details. |
Apr 2023 | Absolutely thrilled to announce that I will join the University of Amsterdam and start my PhD this spring, working with Max Welling, Jan-Willem van de Meent and Alfons Hoekstra! |
latest posts
Oct 2023 | Implicit Kernels for Steerable CNNs |
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