Distinguished Lecture: "Machine Learning Meets Mobile Communications" by Dr. H. Vincent Poor

Friday, June 14, 2019 10:30 am - 10:30 am EDT (GMT -04:00)

"Machine Learning Meets Mobile Communications"

Dr. H. Vincent Poor
Speaker: Dr. H. Vincent Poor


H. Vincent Poor is the Michael Henry Strater University Professor at Princeton University. He received his Ph.D. in EECS from Princeton in 1977. From 1977 until joining the Princeton faculty in 1990, he was on the faculty at the University of Illinois at Urbana-Champaign. He has held visiting positions at several other universities including, and most recently, Berkeley and Cambridge.  Dr. Poor’s research interests are in information theory and signal processing, 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 of London, and other national and international academies. He received the Marconi and Armstrong Awards of the IEEE Communications Society in 2007 and 2009, respectively, and the IEEE Alexander Graham Bell Medal in 2017.


Mobile communications and machine learning are two of the most exciting 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. This talk will illuminate these two perspectives, drawing largely on examples for recent research in the field. These include the use of learning mechanisms in communications problems such as proactive caching, resource allocation and security, and the consideration of communications issues arising in distributed learning problems such as federated learning and social learning.