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Jie Fu

Associate Professor, Department of Electrical and Computer Engineering, University of Florida

Fu’s lab focuses on developing intelligent and (semi-)autonomous systems through the integration of control theory, machine learning, and formal methods. At our lab, we aim to build trustworthy systems in complex decision-making and dynamic environments, in coordination with or under the supervision of human operators. The research contains several main areas: (1) Preference-aware decision making with human-on-the loop; (2) learning-enabled adaptive control and planning with complex temporal objectives; (3) game-theoretic design of provably correct autonomous systems. The applications include human-autonomy shared control, security and safety for cyber-physical systems, and cyber security.

 

Lab Director:

Dr. Jie Fu is an Associate Professor with the Department of Electrical and Computer Engineering at the University of Florida, Gainesville, FL, USA. She received the M.Sc. degree in Electrical Engineering and Automaton from Beijing Institute of Technology, Beijing, China, in 2009, and the Ph.D. degree in Mechanical Engineering from the University of Delaware, Newark, DE, USA, in 2013. From 2013 to 2015, she was a Postdoctoral Scholar with the University of Pennsylvania. From 2016-2021, she was an Assistant Professor with the Department of Robotics Engineering, with an affiliation in Dept. of ECE, at Worcester Polytechnic Institute, Worcester, MA. In 2021, she joined the Department of Electrical and Computer Engineering at the University of Florida as an assistant professor. Fu’s research focuses on developing control theory and planning algorithms for constructing secured (semi-)autonomous systems with high-level logic reasoning and adaptive decision-making capabilities.  Dr. Fu has also received several early career awards, including the AFOSR Young Investigator Award and the DARPA Young Faculty Award in 2021, and the NSF CAREER Award in 2022. She has received funding for her research projects from ARO, NSF, DARPA, and AFOSR.

News: 

  • Oct 2025: Our paper “Defensive Deception Against Subspace-Based AoA Localization Under Jamming Attacks” has been accepted and presented at IEEE MILCOM 2025. This is a collaborative research with Dr. Mingyue Ji (ECE, UF), Dr. Ahmed Hemida (ARL), and Dr. Charles Kamhoua (ARL).
  • Jul 2025: Dr. Jie Fu has been promoted to Associate Professor with tenure, effective August 16, 2025.
  • Mar 2025: Dr. Jie Fu (PI), Prof. Warren Dixon (Co-PI), and Dr. Mengran Xue (Co-PI, RTX BBN) have been awarded a $1,346,542 research grant from the U.S. Army Research Laboratory for their project titled: “Strategic Planning in Hierarchical Games: Achieving Rapid Dominance in the Uncertain World of Conflict (Shadow)”. We are excited to continue the collaboration with ARL on this TBAM CYCLE 2.
  • Mar 2025: Our paper “Adaptive Incentive Design for Markov Decision Processes with Unknown Rewards” has been accepted to Transactions on Machine Learning Research. Congratulations to my Ph.D. student Haoxiang Liu for leading this work in collaboration with Dr. Shuo Han and others. This paper proposes an adaptive mechanism for incentive design in MDPs with unknown rewards, building toward more robust decision-making under uncertainty.
  • Feb 2025: Our paper “Sequential Decision Making in Stochastic Games with Incomplete Preferences over Temporal Objectives” is accepted to AAAI 2025 (AI-Alignment Track). This work, in collaboration with Dr. Abhishek Kulkarni and Dr. Ufuk Topcu from UT Austin, explores new methods for decision-making when agents have uncertain preferences over temporal logic objectives. Looking forward to sharing this at AAAI! 
  • Feb 2025: Excited to share that “Synthesis of Dynamic Masks for Information-Theoretic Opacity in Stochastic Systems” is accepted to ICCPS 2025. This work, led by Sumukha Udupa and Chongyang Shi, proposes a novel method for enforcing opacity using dynamic masks—advancing information-aware security in cyber-physical systems.
  • Feb 2025: Our paper “Learning Nash Equilibrium of Markov Potential Games with a Shared Constraint via Primal-Dual Optimization” is accepted to AAAI 2025. Congrats to Postdoc Dr. Songtao Feng for leading this exciting work on multi-agent reinforcement learning. (Acceptance rate: 32%)
  • Jan 2025: Our paper “Integrating Contact-aware CPG System for Learning-based Soft Snake Robot Locomotion Controllers” is accepted by IEEE Transactions on Robotics. We plan to present this at the upcoming IROS conference by my former Ph.D student, Dr. Xuan Liu! He has contributed several impactful research on bio-inspired learning-based control of soft robotic systems.  A preprint is available. This work builds on the previous T-RO paper on contact-free locomotion and RL for soft snake robot.