
Jan-Willem van de Meent
Associate professor (UHD)
AMLab and Delta Lab
Informatics Institute
University of Amsterdam
Science Park, Lab 42, L4.13
Dr. Jan-Willem van de Meent is an Associate Professor (Universitair Hoofddocent) at the University of Amsterdam. He co-directs the AMLab with Max Welling and co-directs the Uva Bosch Delta Lab with Theo Gevers. He also holds a position as an Assistant Professor at Northeastern University, where he is currently on leave. Prior to becoming faculty at Northeastern, he held a postdoctoral position with Frank Wood at Oxford, as well as a postdoctoral position with Chris Wiggins and Ruben Gonzalez at Columbia University. He carried out his PhD research in biophysics at Leiden and Cambridge with Wim van Saarloos and Ray Goldstein.
Jan-Willem van de Meent’s group develops models for artificial intelligence by combining probabilistic programming and deep learning. A major theme in this work is understanding which inductive biases can enable models to generalize from limited data. Inductive biases can take the form of a simulator that incorporates knowledge of an underlying physical system, causal structure, or symmetries of the underlying domain. At a technical level, his group develops inference methods, along with corresponding language abstractions to make these methods more modular and composable. To guide this technical work, his group collaborates extensively to develop models for neuroscience, robotics, NLP, healthcare, and the physical sciences.
Jan-Willem van de Meent is one of the creators of Anglican, a probabilistic language based on Clojure. His group currently develops Probabilistic Torch, a library for deep generative models that extends PyTorch. He is an author on a forthcoming book on probabilistic programming, a draft of which is available on arXiv. He is a co-chair of the international conference on probabilistic programming (PROBPROG). He was the recipient of an NWO Rubicon Fellowship and is a current recipient of the NSF CAREER award.
Recent Publications
2022
2021
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ICML