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, Volos Greece.
Next generation networks leveraging artificial intelligence to enable distributed intelligence services at the edge
The transformative power of Artificial Intelligence (AI) became apparent the last few years.
The evolving networking technology permeates the power of AI in every aspect of everyday life and is transforming the way we work, live and interact with our social and natural environment.
In order to cope with the vast amount of data and processing requirements of distributed intelligence services novel synergies between networking and AI need to be leveraged.
An AI enabled intelligence plane may improve the efficiency and adaptivity of the network and cloud resources to the evolving application environment, while provide increased levels of privacy and security.
We will present recent results on combining graph Neural Networks (NN) and transformers for spatio-temporal networks predictions and optimization.
Then we will describe how caching at the edge and the core of the network may boost machine learning services.
Furthermore we will present in-network NN architectures for processing streaming data and provide online network monitoring.
Such advance will provide the components towards an intelligent network plane that will unleash AI services at the network edge.
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