Jan-Willem van de Meent

Associate professor (UHD)
AMLab and Delta Lab
Informatics Institute
University of Amsterdam
Science Park, Lab 42, L4.13

 

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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

  1. Bio. Pysch.
    Interoception as Modeling, Allostasis as Control
    Sennesh, Eli, Theriault, Jordan, Brooks, Dana, Meent, Jan-Willem, Barrett, Lisa Feldman, and Quigley, Karen S.
    Biological Psychology 2022
  2. Neuroinf.
    A Computational Neural Model for Mapping Degenerate Neural Architectures
    Khan, Zulqarnain, Wang, Yiyu, Sennesh, Eli, Dy, Jennifer, Ostadabbas, Sarah, van de Meent, Jan-Willem, Hutchinson, J. Benjamin, and Satpute, Ajay B.
    Neuroinformatics Mar 2022
  3. WACV
    Enhancing Few-Shot Image Classification With Unlabelled Examples
    Bateni, Peyman, Barber, Jarred, Meent, Jan-Willem, and Wood, Frank
    In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Jan 2022

2021

  1. AISTATS
    Rate-Regularization and Generalization in Variational Autoencoders
    Bozkurt, Alican, Esmaeili, Babak, Tristan, Jean-Baptiste, Brooks, Dana, Dy, Jennifer, and Meent, Jan-Willem
    In International Conference on Artificial Intelligence and Statistics Mar 2021
  2. UAI
    Learning proposals for probabilistic programs with inference combinators
    Stites, Sam, Zimmermann, Heiko, Wu, Hao, Sennesh, Eli, and Meent, Jan-Willem
    In Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence 27–30 jul 2021
  3. EMNLP
    Disentangling Representations of Text by Masking Transformers
    Zhang, Xiongyi, van de Meent, Jan-Willem, and Wallace, Byron
    In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing Nov 2021
  4. NAACL
    On the Impact of Random Seeds on the Fairness of Clinical Classifiers
    Amir, Silvio, van de Meent, Jan-Willem, and Wallace, Byron
    In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies Jun 2021
  5. NeurIPS
    Nested Variational Inference
    Zimmermann, Heiko, Wu, Hao, Esmaeili, Babak, and Meent, Jan-Willem
    In Advances in Neural Information Processing Systems Jun 2021
  6. AAMAS
    Action Priors for Large Action Spaces in Robotics
    Biza, Ondrej, Wang, Dian, Platt, Robert, Meent, Jan-Willem, and Wong, Lawson L.S.
    In Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems Jun 2021
  7. ICML
    Conjugate Energy-Based Models
    Wu*, Hao, Esmaeili*, Babak, Wick, Michael L, Tristan, Jean-Baptiste, and van de Meent, Jan-Willem
    In International Conference on Machine Learning Jun 2021

2020

  1. NeurIPS
    Neural Topographic Factor Analysis for fMRI Data
    Sennesh*, Eli, Khan*, Zulqarnain, Wang, Yiyu, Hutchinson, J. Benjamin, Satpute, Ajay, Dy, Jennifer, and van de Meent, Jan-Willem
    Advances in Neural Information Processing Systems 2020
  2. ICML
    Amortized Population Gibbs Samplers with Neural Sufficient Statistics
    Wu, Hao, Zimmermann, Heiko, Sennesh, Eli, Le, Tuan Anh, and van de Meent, Jan-Willem
    Advances in Neural Information Processing Systems 2020
  3. MLHC
    Query-Focused EHR Summarization to Aid Imaging Diagnosis
    McInerney, Denis Jered, Dabiri, Borna, Touret, Anne-Sophie, Young, Geoffrey, van de Meent, Jan-Willem, and Wallace, Byron C.
    Machine Learning for Healthcare Apr 2020