Optimizing & Securing Wireless 5G, IoT and Beyond with Machine Learning, Dr. Timothy O'Shea

Machine Learning and data-driven models for signal processing and information security and analytics have been an extremely exciting space over the past few years, leading to state-of-the-art performance in computer vision, natural language processing, voice recognitions, and many other applications.

Wireless applications have long struggled with the tradeoff between tractable optimization problems and model deficit, where our models for the propagation environment are not rich enough to represent the stochastic nature of the environment richly enough to optimize to its full potential capacity. Data-driven methods have up-ended many years’ worth of thinking in this domain, and allowed us to accomplish both, leveraging rich and accurate data and distribution information in deployed wireless systems while also maintaining simple high-level problem definition which are data-driven.

This webinar will highlight where these technologies impact current and future wireless systems, specifically focusing on how visibility into the radio access network can help secure our ever-denser wireless systems carrying critical infrastructure and a range of wireless tools we depend on every day. It will highlight the wireless cyber-intrusion detection work being conducted jointly by Virginia Tech and DeepSig and discuss possibilities for where the technology is headed beyond 5G networks today.

Biography

Dr. Timothy O'Shea, a research assistant professor at the Hume Center in Arlington, VA and also Co-Founder/CTO of DeepSig, a venture backed startup building machine learning driven wireless capabilities.

Dr. O'Shea previously worked with a DOD lab on software and cognitive radio applied research technologies and rapid prototyping, helping build and lead the GNU Radio project, is a Co-Chair of the IEEE Emerging Technology Area on Machine Learning for Communications, an editor for Transactions on Wireless Communications and TCCN, Co-Chair for IEEE GlobeCom and IEEE ICC ML4Comms workshops in 2020, and Co-author of over 50 peer reviewed conference and journal papers with over 3000 citations, over 20 patents, primarily focusing on the intersection of wireless communications and machine learning.

Previously he was a technical consultant for Hawkeye 360, Federated Wireless, O’Shea Research, and at Cisco Systems. He has served as a technical advisory board member for programs at NSF, DARPA, EU HORIZON 2020, and DOD programs. He completed his PhD from VT ECE in 2017 and his BS/MS in ECE at NC State in 2007.