Chuchu Fan is the recipient of the 2020 ACM Doctoral Dissertation Award for her dissertation, “Formal Methods for Safe Autonomy: Data-Driven Verification, Synthesis, and Applications.” Honorable Mentions go to Henry Corrigan-Gibbs of the Massachusetts Institute of Technology and Ralf Jung of the Max Planck Institute for Software Systems and MIT.
Fan’s dissertation makes foundational contributions to verification of embedded and cyber-physical systems, and demonstrates applicability of the developed verification technologies in industrial-scale systems. Her dissertation also advances the theory for sensitivity analysis and symbolic reachability; develops verification algorithms and software tools (DryVR, Realsyn); and demonstrates applications in industrial-scale autonomous systems.
Key contributions of her dissertation include the first data-driven algorithms for bounded verification of nonlinear hybrid systems using sensitivity analysis. A groundbreaking demonstration of this work on an industrial-scale problem showed that verification can scale. Her sensitivity analysis technique was patented, and a startup based at the University of Illinois at Urbana-Champaign has been formed to commercialize this approach.
Fan also developed the first verification algorithm for “black box” systems with incomplete models combining probably approximately correct (PAC) learning with simulation relations and fixed point analyses. DryVR, a tool that resulted from this work, has been applied to dozens of systems, including advanced driver assist systems, neural network-based controllers, distributed robotics, and medical devices.
Additionally, Fan’s algorithms for synthesizing controllers for nonlinear vehicle model systems have been demonstrated to be broadly applicable. The RealSyn approach presented in the dissertation outperforms existing tools and is paving the way for new real-time motion planning algorithms for autonomous vehicles.
Fan is the Wilson Assistant Professor of Aeronautics and Astronautics at the Massachusetts Institute of Technology, where she leads the Reliable Autonomous Systems Lab. Her group uses rigorous mathematics including formal methods, machine learning, and control theory for the design, analysis, and verification of safe autonomous systems. Fan received a BA in Automation from Tsinghua University. She earned her PhD in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign.
