Keynote Speakers

Abstract: IoT devices collect significant amount of data at the wireless edge, opening up new potentials for machine learning applications. Current approach to edge intelligence is to offload all the collected data to a cloud server for central processing. This approach is not sustainable considering the expected growth in the number of IoT devices and the traffic they generate. Moreover, it creates significant privacy risks for the users, and introduces delays that cannot be tolerated by most applications. The alternative is to bring the intelligence to the edge, by distributing both the training and the inference tasks across edge devices and servers. In this talk, I will present recent results on efficient distributed inference and training over wireless channels taking into account channel impairments as well as power and bandwidth limitations of wireless devices. This will involve bringing together novel communication and coding techniques with distributed learning algorithms.

Bio: Prof. Deniz Gündüz received the M.S. and Ph.D. degrees in electrical engineering from NYU Polytechnic School of Engineering (formerly Polytechnic University), Brooklyn, NY, in 2004 and 2007, respectively. Following his PhD, he served as a postdoctoral research associate at Princeton University, as a consulting assistant professor at Stanford University, and as a research associate at CTTC in Barcelona, Spain. ln Sep. 2012, he joined the Electrical and Electronic Engineering Department of Imperial College London, UK, where he is a Professor in Information Processing, and leads the Information Processing and Communications Laboratory (IPC-Lab). He is also a part-time faculty member at the University of Modena and Reggio Emilia, Italy, and has held visiting positions at University of Padova (2018-2020) and Princeton University (2009-2012). His research interests lie in the areas of communications and information theory, machine learning, and privacy. Dr. Gündüz is an Area Editor for the IEEE Transactions on Communications and the IEEE Journal on Selected Areas in Communications (JSAC). He also serves as an Editor of the IEEE Transactions on Wireless Communications. He is a Distinguished Lecturer for the IEEE Information Theory Society (2020-22). He is the recipient of the 2017 Early Achievement Award of the IEEE Communication Society – Communication Theory Technical Committee (CTTC), a Starting Grant of the European Research Council (ERC) in 2015, and the 2014 IEEE Communication Society Best Young Researcher Award for the Europe, Middle East and Africa Region. He served as a Symposium Co-Chair for the 2020 IEEE International Conference on Communications (ICC), and as a General Co-chair of the 2019 London Symposium on Information Theory (LSIT).

Abstract: TBD

Bio: Julien Forgeat is an artificial intelligence principal researcher at Ericsson Research. He joined Ericsson in 2010 after spending several years working on network analysis and optimization. He holds an M.Eng. in computer science from the National Institute of Applied Sciences in Lyon, France. At Ericsson, Julien has worked on mobile learning, Internet of Things and big data analytics before specializing in machine learning and AI infrastructure. His current research focuses on the software components required to run AI and machine learning workloads on distributed infrastructures as well as the algorithmic approaches that are best suited for complex distributed and decentralized use-cases.