A Role for Message Passing in Data Assimilation?
Marc Deisenroth, University College London
14 November 2023
Estimating the latent state of a dynamical system based on noisy observations is a common challenge underlying many tasks in engineering, robotics, or weather modeling. We will discuss two perspectives of state estimation: temporal inference and spatial inference. In this talk, I will provide a machine learning perspective on state estimation and discuss what role message passing can play when solving large-scale spatio-temporal inference problems as they appear in data assimilation.
About the speaker
Professor Marc Deisenroth is the DeepMind Chair of Machine Learning and Artificial Intelligence at University College London, Deputy Director of the UCL Centre for Artificial Intelligence, and part of the UNESCO Chair on Artificial Intelligence at UCL. He also holds a visiting faculty position at the University of Johannesburg. Marc co-leads the Sustainability and Machine Learning Group at UCL. His research interests center around data-efficient machine learning, probabilistic modeling and autonomous decision making with applications in climate/weather science, nuclear fusion, and robotics.