Radio Frequency Machine Learning for National Security Applications
Radio Frequency Machine Learning (RFML) is the novel application of state-of-the-art deep machine learning and reinforcement learning applied to wireless radio frequency communication systems. For commercial systems, these technologies are considered to be a key enabler of upcoming 5G, 6G, and so-called NextG systems. For national security applications, these technologies are critically important in electronic warfare and cybersecurity applications. In this talk, Dr. Headley will overview research in RFML, with a particular focus on the Virginia Tech National Security Institute’s research portfolio.
Dr. William Headley
Associate Director, Spectrum Dominance Division, Virginia Tech National Security Institute
Dr. William “Chris” Headley is the Associate Director for the Spectrum Dominance Division at the Virginia Tech National Security Institute, where he has served as a principal or co-principal investigator on a multitude of government and commercial projects totaling over $20M. Within the division he primarily oversees the Radio Frequency Machine Learning (RFML) portfolio which is at the forefront of this emerging field. Through his courtesy appointment within Virginia Tech’s Electrical and Computer Engineering department, he also serves as a mentor and advisor to both undergraduate and graduate student researchers, providing them with hands-on research opportunities through these projects as well as guiding them towards their degree requirements. Dr. Headley earned his BS/MS/PhD in Electrical Engineering at Virginia Tech. He has written over 30 conference/journal publications and holds an active security clearance. His current research interests include spectrum sensing, radio frequency machine learning, and virtual reality educational opportunities.
Host: Professor Alex Wyglinski