Signe Redfield

Signe Redfield is the Director of the Laboratory for Autonomous Systems Research at the Naval Research Laboratory (NRL).  She has served as Co-Chair for the IEEE Technical Committees for Verification of Autonomous Systems (TC-VAS) and Performance Evaluation and Benchmarking of Robotic and Automation Systems (TC-PEBRAS).  She co-authored the first IEEE RAS standard, 1872-2015, and served as Secretary for standard 1872.1-2024, “Robot Task Representation”.  She was detailed to the Joint Artificial Intelligence Center (JAIC) as the acting lead of their Test and Assessment group in 2019, and returned to the Naval Research Laboratory (NRL) Space Technology Division’s Robotics and Machine Learning Section where she developed assurance cases and supported the development of verification and testing tools for autonomous systems.  She designed the Payload Mission Manager software component for DARPA’s Robotic Servicing of Geosynchronous Satellites (RSGS) program, which integrates the payload fault management system with operator generated automated, supervised, and fully autonomous behavior scripts.  She served as the NRL RSGS Algorithms lead from 2014-2017 and the de facto Fault Management lead from 2015-2019.  Before her arrival at NRL in 2014, she spent three years in London as the ONR Global Associate Director for Autonomy and Unmanned Systems.  Prior to her term at ONR Global, Dr. Redfield worked at the Naval Surface Warfare Center, Panama City Division in Florida where she worked on heterogeneous teams of autonomous maritime vehicles and led the development of a new architecture, enabling the simulation and testing of a variety of arbitration mechanisms to control teams of vehicles.

Don Sofge is a Computer Scientist and Roboticist at the Naval Research Laboratory (NRL) with 36 years of experience (23 at NRL) in Artificial Intelligence, Machine Learning, and Control Systems R&D. He leads the Distributed Autonomous Systems Section in the Navy Center for Applied Research in Artificial Intelligence (NCARAI), where he develops nature-inspired computing paradigms to challenging problems in sensing, artificial intelligence, and control of autonomous robotic systems. He has served as PI/Co-PI on dozens of federally-funded R&D efforts, and has more than 200 refereed publications (including 13 edited books) in robotics, artificial intelligence, machine learning, planning, sensing, control, and related disciplines, and one patent on virtual state estimation for semiconductor fabrication. His current research focuses on control of autonomous teams or swarms of heterogeneous robotic systems.

Dr. Mae Seto earned her B.A.Sc. in the Electrical Engineering option of Engineering Physics and her Ph.D. in Mechanical Engineering – both at the University of British Columbia (Canada). Then, she tenured her NSERC Industrial Post‐Doctoral Fellowship with ISE Ltd. performing R&D on marine autonomous systems. Next, she was a Defence Scientist with Defence R&D Canada for 16 years where she continued her research in autonomy, autonomous systems and underwater acoustics. Seven years ago, she was appointed Associate Professor at Dalhousie University’s Department of Mechanical Engineering with cross‐appointment to the Electrical and Computer Engineering Dept. Dr. Seto is currently a Full Professor and Director of the Intelligent Systems Laboratory and was the Irving Shipbuilding Research Chair in Marine Engineering and Autonomous Systems. She is a consultant with the Canadian Department of National Defence and Defence R&D Canada.  Dr. Seto has served on several NATO Science &Technology working groups and teams. She is a PI with the Transforming Climate Action on topics related to smart ocean platforms for persistent autonomous sensing.

Dr. John Sustersic is an expert in cognitively advanced, complex autonomous systems, hardware-software codesign, and formal semantics. Dr. Sustersic has served as PI of multiple autonomy programs funded by a variety of DoD and Industry sponsors and culminating in multiple real-world test events. An expert in hardware-software co-design and distributed system engineering, John earned his h.D. in Computer Science and Engineering from the Pennsylvania State University. Dr. Sustersic’s research focuses on developing robust decision-making for truly autonomous systems capable of being trusted with delegated authorities in difficult, military and real-world decision domains. His work levers COTS components, such as deep learning frameworks, to rapidly develop complex tools quickly and efficiently, enabling research into complex problems while developing testable capabilities through use of agile-sprint tools.