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Information
Location, Dates & Hotel
About AFCEA
About IEEE ComSoc
About MILCOM and FAQ
2019 Mobile App
Board Members
Technical Program Committee
Technical Track Chairs
MILCOM Fact Sheet
SIGNAL Magazine
ComSoc Magazines
2018 Event Coverage
Program
Agenda
Schedule At A Glance
Speakers
Call for Papers
Technical Program
Unclassified Technical Papers
Unclassified Technical Papers Schedule
Restricted Access Technical Program
Technical Panels
Tutorials
Continuing Education
Author Information
Attend
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Tommaso Melodia, Ph.D.
William Lincoln Smith Chair Professor
Northeastern University
Profile
Professor Melodia is William Lincoln Smith Chair Professor at Northeastern University. He has an extensive background on wireless network modeling and optimization, in particularly distributed convex/nonconvex mixed integer nonlinear optimization as well as experience developing architectures, protocols, and hardware for software-defined and on applications of ML to wireless networking problems. He is the Director of Research for the PAWR Project Office, a $100M public-private partnership that will build 4 city-scale fully programmable platforms for advanced wireless research. He has been a PI or co-PI in over 30 research projects, of which over 10 specifically on reconfigurable and software radios with funding from the Office of Naval Research, Air Force Research Laboratory, Army Research Laboratory, DARPA, the Office of the Secretary of Defense, and the National Science Foundation. Prof. Melodia is an IEEE Fellow, a Distinguished Lecturer of the IEEE, a recipient of the National Science Foundation CAREER award, and a coauthor of several best paper awards. He is the Technical Program Co-Chair of IEEE INFOCOM 2018, the Editor in Chief for Computer Networks, and has served in the Editorial Boards of IEEE Transactions on Wireless Communications, IEEE Transactions on Mobile Computing, IEEE Transactions on Multimedia, IEEE Transactions on Biological, Molecular and Multi-Scale Communications, Smart Health (Elsevier). His papers have been cited over 14,200 times, with an h-index of 41.
Sessions
Tutorial: Deep Learning for RF Signal Classification and Fingerprinting
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