Jan-Willem van de Meent
Associate professor (UHD)
AMLab and Delta Lab
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 how we can develop data-efficient models in machine learning by incorporating knowledge of an underlying physical system, causal structure, or symmetries of the underlying domain. At a technical level, his group develops inference methods for probabilistic programming systems. He is one of the creators of Anglican, a probabilistic language based on Clojure and of Probabilistic Torch, a library for deep generative models that extends PyTorch. He is also an author on a forthcoming book on probabilistic programming, a draft of which is available on arXiv. To ground methodological work in practice, his group collaborates with researchers in neuroscience, NLP, healthcare, robotics, physics, and chemistry.
Jan-Willem van de Meent served as a founding co-chair of the international conference on probabilistic programming (PROBPROG). He is currently serving as a program chair for the international conference on artificial intelligence and statistics (AISTATS). He was the recipient of an NWO Rubicon Fellowship and is a current recipient of the NSF CAREER award.
ICMLConjugate Energy-Based ModelsIn International Conference on Machine Learning Jun 2021