“Exploring the RF Spectrum with Deep Learning” -- Adam Thompson
Deep Learning has revolutionized the computer vision domain with many networks now outperforming traditional machine learning and image processing techniques for scene classification and understanding. Can these same ideas, however, be applied to non-imagery data? In this talk, we will highlight the developments and implications of deep learning on RF data – speaking specifically about signal classification, identification, anomaly detection, and scheduling.
Adam Thompson is a Senior Solutions Architect at NVIDIA. With a background in signal processing, he has spent his career participating in and leading programs focused on deep learning for RF classification, data compression, high-performance computing, and managing and designing applications targeting large collection frameworks. His research interests include deep learning, high-performance computing, systems engineering, cloud architecture/integration, and statistical signal processing. He holds a Masters degree in Electrical & Computer Engineering from Georgia Tech and a Bachelors from Clemson University.