Bio

Lu Feng is postdoctoral fellow at the PRECISE Center and Department of Computer & Information Science at the University of Pennsylvania, advised by Professor Insup Lee. She received her D.Phil. (Ph.D.) in Computer Science from the University of Oxford in 2014, under the supervision of Professor Marta Kwiatkowska. She also holds a B.Eng. in Information Engineering from the Beijing University of Posts and Telecommunications and a M.Phil. in Computer Speech, Text and Internet Technology from the University of Cambridge.

Lu is a recipient of the prestigious James S. McDonnell Foundation Postdoctoral Fellowship, which only selects 10 fellows internationally and trans-disciplinary each year. She has also received various other awards, including the ACM SIGMOBILE N2 Women Young Researcher Fellowship, UK Engineering and Physical Sciences Research Council Graduate Scholarship, and Cambridge Trust Scholarship.

Assuring the Safety and Security of Cyber-Physical Systems

Assuring the Safety and Security of Cyber-Physical Systems

Cyber-Physical Systems (CPS)—also called the Safety-Critical Internet of Things—are smart systems that include co-engineered interacting networks of physical and computational components. These highly interconnected and integrated systems provide new functionalities to improve quality of life and enable technological advances in critical areas, such as smart healthcare, transportation, manufacturing, and energy. The increasing complexity and scale of CPS, with high-level expectations of autonomous operation, predictability and robustness, in the presence of environmental uncertainty and resource limitations, pose significant challenges for assuring the safety and security of CPS.

My research is focused on assuring the safety, security and dependability of CPS, through formal methods and data-driven approaches, with particular emphasis on probabilistic modeling and quantitative verification. My doctoral thesis work improves the scalability of probabilistic model checking—a powerful formal verification method that focuses on analyzing quantitative properties of stochastic systems—by developing, for the first time, fully automated compositional verification techniques for probabilistic systems.

My current postdoctoral research includes two themes. One theme is medical CPS, which are life-critical, context-aware, networked systems of medical devices. For example, I have worked on assuring the interoperability of on-demand plug & play medical devices, and model-based development of high-confidence medical devices.

Another theme of my current work is human-in-the-loop CPS. I collaborate with clinicians and develop data-driven modeling framework for studying the behavior of Diabetic patients who depend on insulin pumps. The research outcome could potentially assist in developing safer, more effective, and even personalized treatment devices. In another project, with my collaborators at the Air Force Research Lab, I develop approaches for synthesizing provably correct human-in-the-loop control protocols for unmanned aerial vehicles (UAV). My other on-going projects include human factors in CPS security assurance, and operator behavior signatures for the haptic authentication of surgical robots.