Staff Jan-Willem van de Meent Associate professor (UHD) at AMLab and Delta Lab Probabilistic programming, inference, deep learning, and their applications. Max Welling Professor at AMLab, Delta Lab, and Distinguished Scientist at MSR Geometric deep learning, deep generative models, AI for molecular simulation. Erik Bekkers Assistant professor at AMLab Equivariance and geometry in deep learning Patrick Forré Assistant professor, AI4Science Lab manager at AMLab, AI4Science Lab AI4Science, causality, mathematical aspects of machine learning Christian Naesseth Assistant professor at AMLab Monte Carlo methods, variational inference, deep learning, causal inference. ... Eric Nalisnick Assistant professor at AMLab probabilistic machine learning, human-in-the-loop learning, specifying prior ... Herke van Hoof Assistant professor at AMLab Reinforcement learning in structured domains Félice Arends Office Assistant at AMLab and Delta Lab Adminstrative support +31 20 525 7884 Postdocs James Townsend Postdoc at AMLab Probabilistic modeling & inference, lossless compression Masoud Mansoury Postdoc at AMLab Recommender Systems, Contextual Bandits PhD Students Tim Bakker PhD candidate at AMLab with Herke van Hoof and Max Welling Deep Reinforcement Learning for active learning and active sensing Jim Boelrijk PhD candidate at AMLab and AI4Science Lab with Patrick Forré, Bob Pirok, Bernd Ensing, and Alfons Hoekstra Bayesian optimization, quantitative structure-property relationships Natasha Butt PhD candidate at AMLab with Max Welling and Taco Cohen Unsupervised Learning for Source Compression Gabriele Cesa PhD candidate at AMLab with Max Welling, Arash Behboodi, and Taco Cohen Geometric Deep Learning Pim de Haan PhD candidate at AMLab with Max Welling and Taco Cohen Causality, Geometric Deep Learning Evgenii Egorov PhD candidate at AMLab with Max Welling and Roberto Bondesan Monte-Carlo Methods, Combinatorial optimization, ML for Quantum Babak Esmaeili PhD candidate at AMLab with J.W. van de Meent Deep generative models, representation learning, inference Marco Federici PhD candidate at AMLab with Patrick Forré Information theory for machine learning and representation learning Niklas Höpner PhD candidate at AMLab with Herke van Hoof and Ilaria Tiddi (VU) Deep Reinforcement Learning for Human-AI interaction Rob Hesselink PhD candidate at AMLab with Erik Bekkers Geometric deep learning, graphs and group equivariance Emiel Hoogeboom PhD Candidate at Delta Lab with Max Welling Deep Generative Models, Molecular Generation T. Anderson Keller PhD candidate at AMLab, Delta Lab with Max Welling Biologically-inspired Unsupervised Structured Representation Learning David Kuric PhD candidate at AMLab with Herke Van Hoof Hierarchical reinforcement learning, meta-reinforcement learning Leon Lang PhD Candidate at AMLab and CSL with Patrick Forré and Rick Quax Geometric Deep Learning, Multivariate Information Theory Fiona Lippert PhD candidate at AMLab and AI4Science Lab with Patrick Forré and Emiel van Loon AI4Science, spatio-temporal dynamical systems, radar aeroecology Sindy Löwe PhD candidate at AMLab with Max Welling Structured representation learning Matthew Macfarlane PhD candidate at AMLab with Herke van Hoof Reinforcement Learning Putra Manggala PhD candidate at AMLab with Eric Nalisnick Bayesian statistics, probabilistic inference, optimal transport Benjamin Miller PhD candidate at AMLab & GRAPPA with Patrick Forré, Christoph Weniger, Samaya Nissanke, and Max Welling simulation-based inference Teodora Pandeva PhD candidate at AMLab and AI4Science Lab with Joris Mooij, Patrick Forré, Leendert Hamoen, and Martijs Jonker Causal Discovery for Gene Regulation, Domain Adaptation Rob Romijnders PhD candidate at AMLab with Max Welling, Christos Louizos, and Yuki Asano Federated learning, probabilistic inference, differential privacy David Ruhe PhD candidate at AMLab, AI4Science with Patrick Forré Machine Learning for Science (Radio Astronomy) Dharmesh Tailor PhD candidate at AMLab with Eric Nalisnick Robustness and interpretability in deep probabilistic models Sharvaree Vadgama PhD candidate at AMLab with Erik Bekkers and Jakub Tomczak (VU) Geometric latent space modeling and explainable AI Winfried van den Dool PhD candidate at QUVA Lab with Max Welling, Yuki Asano, and Tijmen Blankevoort Hardware-aware learning, analog compute, quantization, solving PDEs using DL Putri van der Linden PhD candidate at AMLab with Erik Bekkers Geometric deep learning, random graph neural networks Qi Wang PhD candidate at AMLab with Herke Van Hoof probabilistic models for meta learning Maurice Weiler PhD candidate at AMLab with Max Welling and Erik Verlinde Equivariant and geometric deep learning Heiko Zimmermann PhD candidate at AMLab with J.W. van de Meent Probabilistic modeling & inference, probabilistic programming
