Professor Marc Mézard, Director of l’École normale supérieure, Paris, France, will deliver the Abdus Salam Distinguished Lecture Series 2020. The third and last talk in the 2020 series will take place on Wednesday 29 January 2020 @ 11.00 hrs in the Budinich Lecture Hall.

Abstract:
A major challenge of contemporary statistical inference is the large-scale limit, where one wants to discover the values of many hidden parameters, using large amount of data. In recent years, ideas from statistical physics of disordered systems, notably the cavity method, have helped to develop new algorithms for important inference problems, ranging from error correcting codes in information theory to compressed sensing, machine learning and generalized linear regression.  The talk will review these developments and explain how they can be used, together with the replica method, to identify phase transitions in benchmark statistical ensembles of inference problems. 

The talk will be livestreamed from the ICTP website.
All are welcome to attend the events. 

Biosketch: Marc Mézard is a theoretical physicist. He received a PhD from Ecole Normale Supérieure in Paris, followed by a post-doc in Rome, and became the head of the statistical physics group in Paris-Sud University. He has been the director of École normale supérieure since 2012. His main field of research is the statistical physics of disordered systems and its use in various branches of science - biology, economics and finance, information theory, computer science, statistics, and signal processing. In recent years his research has focused on information processing in neural networks. He has received the Lars Onsager prize from the American Physical Society, the Humboldt-Gay-Lussac prize, the silver medal of CNRS and the Ampere prize of the French Academy of Science. He is a member of the European Academy of Science

The Abdus Salam Distinguished Lecture Series receives generous support from the Kuwait Foundation for the Advancement of Sciences (KFAS). For more information see: https://kfas.ictp.it/