Gary Lee’s research is focused on one of the dominant and enduring challenges in the field of communication: managing and rejecting interference in communication systems and networks, which otherwise strongly limit performance.  The traditional approach of hand-crafting models of interference phenomena does not scale and thus fails to captures the increasingly complex forms of interference present in today’s radio frequency (RF) and related environments.    Motivated by this observation, Gary has been pursuing new data-driven approaches for modeling and mitigating such interference, leveraging modern machine learning tools and architectures to advance the state-of-the-art.   To help foster broader engagement of the community in this area, Gary has been working with his colleagues to curate rich RF datasets and associated challenge problems, several of which are available at  Gary is a Ph.D. graduate student working in the Signals, Information, and Algorithms Laboratory supervised by Prof. Gregory Wornell.