TILOS-HDSI Distinguished Colloquium: The Synergy between Machine Learning and the Natural Sciences

Max Welling, Research Chair in Machine Learning at the University of Amsterdam

Abstract: Traditionally machine learning has been heavily influenced by neuroscience (hence the name artificial neural networks) and physics (e.g. MCMC, Belief Propagation, and Diffusion based Generative AI). We have recently witnessed that the flow of information has also reversed, with new tools developed in the ML community impacting physics, chemistry and biology. Examples include faster DFT, Force-Field accelerated MD simulations, PDE Neural Surrogate models, generating druglike molecules, and many more. In this talk I will review the exciting opportunities for further cross fertilization between these fields, ranging from faster (classical) DFT calculations and enhanced transition path sampling to traveling waves in artificial neural networks.

Prof. Max Welling is a research chair in Machine Learning at the University of Amsterdam and a Distinguished Scientist at MSR. He is a fellow at the Canadian Institute for Advanced Research (CIFAR) and the European Lab for Learning and Intelligent Systems (ELLIS) where he also serves on the founding board. His previous appointments include VP at Qualcomm Technologies, professor at UC Irvine, postdoc at U. Toronto and UCL under supervision of prof. Geoffrey Hinton, and postdoc at Caltech under supervision of prof. Pietro Perona. He finished his PhD in theoretical high energy physics under supervision of Nobel laureate Prof. Gerard ‘t Hooft.


Feb 21 2024


Pacific Standard Time
2:00 PM - 3:30 PM

Local Time

  • Timezone: America/New_York
  • Date: Feb 21 2024
  • Time: 5:00 PM - 6:30 PM

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HDSI 123 and Zoom
3234 Matthews Ln, La Jolla, CA 92093



Other Organizers

Halicioglu Data Science Institute
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