Biography
I’m a machine learning researcher at Qualcomm AI Research in Amsterdam. Previously, I did my PhD in machine learning at the University of Amsterdam under the supervision of Cees Snoek and Gertjan Burghouts. I was also part of the ELLIS PhD program in cooperation with TNO. My research interests include structured prediction and generative models for structured data such as sets, graphs, or images.
Interests
- Machine Learning
- Computer Vision
Education
PhD in Machine Learning, expected 2023
University of Amsterdam
MSc in Computer Science, 2017
Technical University of Munich
BSc in Computer Science, 2015
Technical University of Munich
Publications
Neural Networks Are Graphs! Graph Neural Networks for Equivariant Processing of Neural Networks
2nd Annual Topology, Algebra, and Geometry in Machine Learning Workshop at ICML (TAG-ML), 2023.
2nd Annual Topology, Algebra, and Geometry in Machine Learning Workshop at ICML (TAG-ML), 2023.
Unlocking Slot Attention by Changing Optimal Transport Costs
International Conference on Machine Learning (ICML), 2023.
International Conference on Machine Learning (ICML), 2023.
Self-Guided Diffusion Models
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
Multiset-Equivariant Set Prediction with Approximate Implicit Differentiation
International Conference on Learning Representations (ICLR), 2022.
International Conference on Learning Representations (ICLR), 2022.
Set Prediction without Imposing Structure as Conditional Density Estimation
International Conference on Learning Representations (ICLR), 2021.
International Conference on Learning Representations (ICLR), 2021.
Industry experience
Research Intern
Qualcomm AI Research
Research machine learning methods for computation graph scheduling.
Consultant
KPMG