Linda Bushnell

Linda Bushnell is a Research Professor in the Department of Electrical and Computer Engineering at the University of Washington - Seattle. She received her Ph.D. in Electrical Engineering and Computer Sciences from University of California - Berkeley in 1994, her M.A. in Mathematics from University of California - Berkeley in 1989, her M.S. in Electrical Engineering from University of Connecticut - Storrs in 1987, and her B.S. in Electrical Engineering from University of Connecticut - Storrs in 1985. She also received her MBA from the University of Washington Foster School of Business in 2010. Her research interests include networked control systems and cyber-physical systems. She is a Fellow of the IEEE for contributions to networked control systems. She is a Fellow of IFAC for contributions to the analysis and design of networked control systems. She is a recipient of the US Army Superior Civilian Service Award, NSF ADVANCE Fellowship, and IEEE Control Systems Society Distinguished Member Award. She has been a member of the IEEE since 1985, and a member of the IEEE CSS since 1990. She is the Treasurer of the American Automatic Control Council.

• IEEE Fellow, for contributions to networked control systems
• IFAC Fellow, for contributions to the analysis and design of networked control systems
• KTH Digital Futures Scholar-in-Residence, 2024
• Wallenberg AI, Autonomous Systems and Software Program Guest Researcher, 2024
• Fellow, Asia-Pacific Artificial Intelligence Association, 2024
• Distinguished Member, IEEE Control Systems Society, 2017
• Distinguished Lecturer, IEEE Control Systems Society, 2014 - 2018
• Recognition Award, IEEE Control Systems Society, 2007
• Chair’s Award, UW Department of EE, 2003
• ADVANCE Fellow Award, National Science Foundation, 2002
• Mentor of the Year Award, UW, Women in Science and Engineering, 2002
• US Army Superior Civilian Service Award, 2000
• Eta Kappa Nu, inducted 1985, Beta Omega Chapter: University of Connecticut

AFOSR DURIP, HydraAI: LLM and GenAI Compute Platform to Support AFOSR Multi-Scale Hydra

KACST, Advancing LLM Alignment and Trustworthiness

DARPA, Kallisti

J-1. D. Sahabandu, S. Moothedath, J. Allen, L. Bushnell, W. Lee, R. Poovendran, “RL-ARNE: A Reinforcement Learning Algorithm for Computing Average Reward Nash Equilibrium of Nonzero-Sum Stochastic Games, IEEE Transactions on Automatic Control, Technical Notes and Correspondence, 69:11, pp. 7824-7831, (Nov. 2024). DOI: 10.1109/TAC.2024.3403693

J-2. S. Moothedath, D. Sahabandu, J. Allen, A. Clark, L. Bushnell, W. Lee, and R. Poovendran, “Dynamic Information Flow Tracking for Detection of Advanced Persistent Threats: A Stochastic Game Approach,” IEEE Transactions on Automatic Control (Oct. 2024). DOI: 10.1109/TAC.2024.3403675

J-3. S. Moothedath, D. Sahabandu, J. Allen, L, Bushnell, W. Lee, and R. Poovendran, “Stochastic Dynamic Information Flow Tracking Game using Supervised Learning for Detecting Advanced Persistent Threats,” Automatica ,vol. 159 (Jan. 2024).