Brian M. Sadler

You are driving with an expert passenger named Alice. She watches how you drive, and assesses your every move, reaction, or lack of reaction, patiently waiting to provide help. Bob, on the other hand, will be your chauffer, and you let him take over. This is the driverless car, and you are entirely reliant on Bob’s expertise. Whom do you choose, Alice or Bob?

The race to full autonomy is on, but smart infrastructure is needed for mass adoption. This requires resilient coordination, self-healing networks, learning, and rapid collaborative decision making with humans and machines. The problem difficulty is driven by environmental variation and complexity, tempo, and interaction between autonomous and human operation, while design is complicated by heterogeneity, scale, and communications rate.

Biography:

Brian M. Sadler (Fellow IEEE, Fellow ARL) is the Army Senior Scientist for Intelligent Systems at the Army Research Laboratory (ARL) in Adelphi, MD, and lectured at Johns Hopkins University in communications and signal processing for 15 years. He has been an Associate and Guest Editor for a variety of journals in communications, signal processing, and robotics, including the IEEE Journal on Selected Areas in Communications, IEEE Transactions on Signal Processing, and IEEE Transactions on Robotics, as well as IEEE JSTSP, IEEE SP Magazine, International Journal of Robotics Research, and Autonomous Robots. He received Best Paper Awards from the IEEE Signal Processing Society in 2006 and 2010, was a Distinguished Lecturer for the IEEE SPS Society in 2017-2018, and was General Co-Chair of the 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP’16). His research areas have included radio and optical communications, sensor networking, autonomous multi-agent networking and control, physical layer security, semantic knowledge bases, aeroacoustics, and mixed-signal integrated circuit architectures.

His current focus is on multi-disciplinary approaches to distributed intelligent systems, incorporating communications networking, distributed processing, learning, and control. This includes collaborative autonomy that blends resilient networking and processing, physical layer techniques for robust security and authentication, and new ways for using low-VHF communications in complex environments.

Address:United States