Leandros Tassiulas is the John C. Malone Professor of Electrical Engineering at Yale University, where he served as department head 2016-2022. His current research is on intelligent services and architectures at the edge of next generation networks including Internet of Things, sensing & actuation in terrestrial and non terrestrial environments. He worked in the field of computer and communication networks with emphasis on fundamental mathematical models and algorithms of complex networks, wireless systems and sensor networks. His most notable contributions include the max-weight scheduling algorithm and the back-pressure network control policy, opportunistic scheduling in wireless, the maximum lifetime approach for wireless network energy management, and the consideration of joint access control and antenna transmission management in multiple antenna wireless systems. Dr. Tassiulas is a Fellow of IEEE (2007) and of ACM (2020). His research has been recognized by several awards including the IEEE Koji Kobayashi computer and communications award (2016), the ACM SIGMETRICS achievement award 2020, the inaugural INFOCOM 2007 Achievement Award “for fundamental contributions to resource allocation in communication networks,” several best paper awards including the INFOCOM 1994, 2017 and Mobihoc 2016, a National Science Foundation (NSF) Research Initiation Award (1992), an NSF CAREER Award (1995), an Office of Naval Research Young Investigator Award (1997) and a Bodossaki Foundation award (1999). He holds a Ph.D. in Electrical Engineering from the University of Maryland, College Park (1991) and a Diploma of Electrical Engineering from Aristotle University of Thessaloniki, Greece. He has held faculty positions at Polytechnic University, New York, University of Maryland, College Park and University of Thessaly, Greece.
What do you do with data science?
My work is in the intersection of networked systems and artificial intelligence. There are three related threads that we are currently pursuing: a) Data intensive/machine learning techniques to optimize network operation; b) Network design and optimization for machine learning and AI services; c) Federated and distributed learning in the mobile network edge. Since last year we are part of the NSF center of AI on networked systems.