COMSYS Talk :Machine Learning Meets Mobile Communications

Prof. Vincent Poor - Michael Henry Strater University Professor at Princeton University
Communication systems

Date: July 5th 2019
Location: Eurecom - Eurecom

Title: Machine Learning Meets Mobile Communications Abstract: Mobile communications and machine learning are two of the most exciting and rapidly developing technological fields of our time. In recent times these two fields have begun to merge in two fundamental ways. First, while mobile communications has developed largely as a model-driven field, the complexities of many emerging communications scenarios is giving rise to the need to introduce data-driven methods into the design and analysis of mobile networks. And, conversely, many machine learning problems are by their nature distributed due to either physical limitations or privacy concerns; this distributed nature gives rise to the need to consider mobile networks as part of learning mechanisms, i.e., as platforms for machine learning. This talk will illuminate these two perspectives, while focusing primarily on the latter, by considering communication issues arising in distributed learning problems such as federated learning and collaborative learning. These issues will be illustrated through examples from recent research in the field. Bio: Dr. H. Vincent Poor is the Michael Henry Strater University Professor at Princeton University. He received his Ph.D. in EECS from Princeton in 1977, and from then until joining the Princeton faculty in 1990, he was on the faculty of the University of Illinois. During 2006 - 2016, he served as Dean of Princeton's School of Engineering and Applied Science. He has also held visiting positions at several other universities, including most recently at Berkeley and Cambridge. Dr. Poor's research interests are in signal processing and information theory, and their applications in wireless networks, energy systems and related fields. He is a member of the U.S. National Academy of Engineering and the U.S. National Academy of Sciences, and is a foreign member of the Chinese Academy of Sciences, the Royal Society, and other national and international academies. Recent recognition of his work includes the 2017 IEEE Alexander Graham Bell Medal, and a D.Sc. honoris causa from Syracuse University, also in 2017. --------------------------------------------------------------------------------------- Resumé: Communications et apprentissage sont deux des plus importants domaines technologiques de notre temps. Recemment ces deux sujets ont commencé un processus de convergence. Dans le cadre des systèmes de communications, la complexité des réseaux force l'usage graduel de méthodes basées sur les données. Du coté apprentissage, la nature distribuée des problèmes devient de plus en plus prenante. Ce phénomène conduit intuitivement à l’utilisation des communications sans fil pour connecter des agents d'apprentissage qui sont physiquement distribués. Ce séminaire mettra ces tendances en perspective en considérant aussi des développements récents tels que le federated learning et l'apprentissage collaboratif. Des exemples seront donnés pour illustrer le propos.

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