AI
About

I am a research scientist at Google Brain, located in Amsterdam the Netherlands. My research interests are in generative modeling, variational inference and normalizing flows, and deep learning for physics and other sciences. I received my PhD in theoretical condensed matter physics in 2016 at the University of Amsterdam, and continued as a postdoctoral researcher in machine learning at the University of Amsterdam with Prof. Max Welling. In 2019 I won the Faculty of Science Lecturer of the Year award at the University of Amsterdam for teaching a machine learning course in the master of AI.
For the most up-to-date list of my publications, see my Google Scholar page.


Selected publications:

Integer Discrete Flows and Lossless Compression
E Hoogeboom, JWT Peters, R van den Berg, M Welling
Accepted at Advances in Neural Information Processing Systems (NeurIPS) 2019

Emerging Convolutions for Generative Normalizing Flows
E Hoogeboom, R van den Berg, M Welling
International Conference on Machine Learning (ICML) 2019

Differentiable probabilistic models of scientific imaging with the Fourier slice theorem
K Ullrich, R van den Berg, M Brubaker, D Fleet, M Welling
Conference on Uncertainty in Artificial Intelligence (UAI) 2019

Sinkhorn autoencoders
G Patrini, R van den Berg, P Forré, M Carioni, S Bhargav, M Welling, T Genewein, F Nielsen
Conference on Uncertainty in Artificial Intelligence (UAI) 2019

Sylvester normalizing flows for variational inference
R. van den Berg, L Hasenclever, JM Tomczak, M Welling
Conference on Uncertainty in Artificial Intelligence (UAI) 2018

Graph Convolutional Matrix Completion
R van den Berg, TN Kipf, M Welling
KDD Deep Learning Day 2018

Modeling Relational Data with Graph Convolutional Networks
M Schlichtkrull, TN Kipf, P Bloem, R van den Berg, I Titov, M Welling
European Semantic Web Conference (ESWC) 2018