Staff Jan-Willem van de Meent Associate professor (UHD) at AMLab and Delta Lab AI for scalable and data-efficient scientific computation. 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 Sara Magliacane Assistant professor at AMLab Causality, causal representation learning, causality-inspired ML, dynamical s... Christian Naesseth Assistant professor at AMLab Monte Carlo methods, variational inference, deep learning, causal inference. ... Herke van Hoof Associate professor at AMLab Modular reinforcement learning Postdocs Hany Abdulsamad Postdoc at AMLab, Delta Lab Decision making, experimental design, statistical inference James Townsend Postdoc at AMLab Probabilistic modeling & inference, lossless compression PhD Students Grigory Bartosh PhD candidate at AMLab with C. A. Naesseth Deep generative models, diffusion models, unsupervised learning Natasha Butt PhD candidate at AMLab with Max Welling and Taco Cohen Unsupervised Learning for Source Compression Marius Captari PhD candidate at AMLab with Herke van Hoof Reinforcement Learning Jacobus Dijkman PhD candidate at AMLab with Jan-Willem van de Meent, Max Welling, and Bernd Ensing Deep Learning for Catalyst Design Evgenii Egorov PhD candidate at AMLab with Max Welling and Roberto Bondesan Monte-Carlo Methods, Combinatorial optimization, ML for Quantum Floor Eijkelboom PhD candidate at AMLab, Delta Lab with Jan-Willem van de Meent and Max Welling Physics-informed generative modeling Alejandro García PhD candidate at AMLab with Erik Bekkers and Daan Pelt (ULEI) Geometrical and Topological Representation Learning Niklas Höpner PhD candidate at AMLab with Herke van Hoof and Ilaria Tiddi (VU) Deep Reinforcement Learning for Human-AI interaction Tin Hadzi Veljkovic PhD candidate at AMLab, Delta Lab with Jan-Willem van de Meent and Michael Tiemann Physics-inspired ML, Neural PDE Solvers Roel Hulsman PhD candidate at AMLab with Sara Magliacane causality-inspired machine learning, causal discovery for time-series Metod Jazbec PhD candidate at AMLab, Delta Lab with Eric Nalisnick, Dan Zhang, and Stephan Mandt Bayesian Deep Learning, Anytime Uncertainty 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 Clélia de Mulatier Geometric Deep Learning, Multivariate Information Theory Cong Liu PhD candidate at AMLab and AI4Science Lab with Patrick Forré Geometric Deep Learning, Protein Design, Graph Neural Networks 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 Jakub Reha PhD candidate at AMLab with Sara Magliacane, Ana Mićković, and Max Welling Causality-inspired Machine Learning for Fintech Rob Romijnders PhD candidate at AMLab with Max Welling, Christos Louizos, and Yuki Asano Federated learning, probabilistic inference, differential privacy Daan Roos PhD candidate at AMLab, Delta Lab with Jan-Willem van de Meent and Sebastian Gerwinn Causality, Bayesian inference Mona Schirmer PhD candidate at AMLab, Delta Lab with Eric Nalisnick and Dan Zhang Distribution shifts, continual learning Mátyás Schubert PhD candidate at AMLab with Sara Magliacane and Max Welling Causality-inspired Reinforcement Learning Dharmesh Tailor PhD candidate at AMLab with Eric Nalisnick Robustness and interpretability in deep probabilistic models Alexander Timans PhD candidate at AMLab, Delta Lab with Eric Nalisnick, Kaspar Sakmann, and Christoph-Nikolas Straehle Uncertainty quantification, probabilistic inference, statistics 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 Rajeev Verma PhD candidate at AMLab, Delta Lab with Eric Nalisnick and Volker Fischer AI safety, human-ai compatibility and complementarity, decision making and un... Adi Watzman PhD candidate at AMLab with Herke van Hoof Hierarchical reinforcement learning, meta-reinforcement learning David Wessels PhD candidate at AMLab with Erik Bekkers and Efstatios Gavves Geometry grounded representations in deep learning Olga Zaghen PhD candidate at AMLab with Erik Bekkers and Rita Fioresi Geometric and Topological Deep Learning, Graph Neural Networks Max Zhdanov PhD candidate at AMLab with J.W. van de Meent, Max Welling, and Alfons Hoekstra Learning PDEs from data, physics-informed and geometric deep learning Heiko Zimmermann PhD candidate at AMLab with J.W. van de Meent Probabilistic modeling & inference, probabilistic programming Alumni Tim Bakker PhD candidate at AMLab with Herke van Hoof and Max Welling Active sensing and active learning 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 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 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 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 Fiona Lippert PhD candidate at AMLab and AI4Science Lab with Patrick Forré, Emiel van Loon, and Alfons Hoekstra AI4Science, spatio-temporal dynamical systems, radar aeroecology Sindy Löwe PhD candidate at AMLab with Max Welling Structured representation learning 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 David Ruhe PhD candidate at AMLab, AI4Science with Patrick Forré Machine Learning for Science (Radio Astronomy) 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 Masoud Mansoury Postdoc at AMLab Recommender Systems, Contextual Bandits Eric Nalisnick Assistant professor at AMLab probabilistic machine learning, human-in-the-loop learning, specifying prior ...
