Models, Data & AI Workgroup Talk - 22/02/2022 -18:00 CET - 5p.m. UK Time.

Models, Data & AI Workgroup Talk - 22/02/2022 -18:00 CET - 5p.m. UK Time.
Talk: Efficient Scientific Inference Using Stochastic Dynamical Systems
By Aaron King

Abstract: Questions about the mechanistic operation of biological systems are naturally formulated as stochastic processes, but confronting such models with data can be challenging. In this talk, I describe the essence of the difficulty, highlighting both the technical issues and the importance of the "plug-and-play property". I then illustrate some effective approaches to efficient inference based on such models. I conclude by sketching promising new developments and describing some open problems.

Biosketch: Aaron King is the Nelson G. Hairston Professor of Ecology, Evolutionary Biology, and Complex Systems at the University of Michigan, External Professor at the Santa Fe Institute, and a Fellow of the American Association for the Advancement of Science. He is an applied mathematician and a theoretical ecologist whose work focuses on the dynamics of host-pathogen interactions from the scale of infections to that of epidemics. He is best known for his work developing sophisticated computational methods for efficiently extracting information from noisy, incomplete observations and exploiting these techniques to obtain insights into the determinants of transmission and susceptibility on the basis of time series data of various kinds.

ZOOM Meeting: The meeting is Tuesdays at 18:00 CET (Paris-Berlin-Rome), 5:00 p.m. GMT (London). The Zoom URL is

Meeting-ID: 815 7299 3899
Code: 364883

Background: This is a workgroup meeting in the areas of mathematical modelling, data analysis, machine learning and artificial intelligence. Current activities:

  • Parametrisation of epidemiological models, in cooperation with the Covid-19 project.
  • Model selection problems
  • Implementations are done with RStudio and Jupyter Notebooks

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Tuesday, 29 November 2022