Event
Booz Allen Hamilton Colloquium: Tim O'Shea, DeepSig
Friday, September 2, 2022
3:30 p.m.-4:30 p.m.
Jeong H. Kim Engineering Building, Room 1110
Darcy Long
301 405 3114
dlong123@umd.edu
Speaker: Tim O'Shea, Chief Technology Officer, DeepSig
Title: "Deep Learning in the Physical Layer: Building AI-Native Sensing and Communications Systems"
Abstract: Deep learning has been increasingly impacting numerous layers of the networked software stack. Vision, NLP, and voice applications are widely deployed and used today, while research has been demonstrating wide impact within the network, orchestration, and physical layers. In this talk we’ll highlight some key trends and key works in this research area of leveraging data-driven learning within the wireless physical layer, and share some of the ways we’ve been leveraging these approaches to build software solutions for 5G vRAN at DeepSig to provide both data-driven sensing and spectral awareness as well as data-optimized processing of communication signals to drive down power consumption and improve spectral efficiency and link capacity. Many of these trends and high-level techniques have been identified as key enablers for 6G (or NextG), enabling the AI-Native Air Interface, Semantic Communications and Joint Sensing and Communications as enablers of more efficient spectrum sharing, and improved Massive MIMO and Multi-Access techniques. We’ll highlight how some of these techniques may be leveraged in future cellular and wireless systems and highlight some of the key open research areas where much remains to be studied and solved going forward!
Bio: Tim O'Shea is the Co-Founder/CTO of DeepSig, a venture backed startup in Arlington, VA which builds machine learning driven wireless software for sensing and communications systems, focusing on optimizing 5G vRAN and OpenRAN deployments and AI-Native communications systems and 6G candidate technologies. He also serves as a research assistant professor at Virginia Tech. He previously worked as a DOD civilian focused on applied research in software and cognitive radio technologies and rapid prototyping, has helped build and lead the GNU Radio project, is the Co-Chair of the IEEE Emerging Technology Area on Machine Learning for Communications, has Chaired the GNU Radio Conference Technical Proceedings, MLC Workshops at IEEE Globecom and ICC, Served as an Editor on IEEE TCCN and IEEE Trans. Wireless Comms, and is the Co-author of over 50 peer reviewed conference and journal papers and patents focusing on the intersection of wireless communications and machine learning. Previously he worked with Hawkeye 360, Federated Wireless, and Cisco Systems, has served as a technical advisory board member for programs at NSF, DARPA, EU HORIZON 2020, and other DOD programs. He completed his PhD from VT in 2017 and his BS/MS at NC State in 2007.
