Logic Driven Data Science

Abstract: Data science together with machine learning is prevalent in almost every sector of industry. Many popular techniques, such as deep learning with artificial neural networks, have shown their capabilities in achieving incredible performance and accuracy in helping make Cyber Physical Systems (CPS) smarter. However, data scientists or engineers usually find it challenging to interpret the artifacts learned using such procedures. Also, due to the proliferation of sensors, control engineers have to combat the data deluge problem. They need to process, analyze, and identify structure or logical relations from intractably large amounts of time series data within limited amount of time. Typical machine learning techniques rely on similarity measures defined on complex feature spaces of signals and may overlook the embedded logical structure. In this talk, we explore data analysis from the logicalperspective and introduce supervised and unsupervised learning procedures that utilize Parametric Signal Temporal Logic (PSTL) templates to discover temporal and spatial relations in signal space. The resulting methods not only perform data analysis but also generate formal artifacts to give engineers abstract understanding of the results. We will demonstrate our techniques in many domains, such as automotive testing, medical devices, and online education systems.

Bio: Xiaoqing Jin is a Senior Engineer at Toyota Motors North America R&D. She received her Ph.D. from the University of California at Riverside on topics including symbolic model checking, stochastic model checking, and formal verification and validation for hybrid systems. She began her career in doing advanced research at Toyota where she was responsible for researching and developing techniques and tools to help design and analysis of industrial cyber-physical systems, such as control systems for internal combustion engine vehicles and fuel cell electric vehicles. Her research interests are in the broad area of hybrid systems, temporal logics, machine learning, data analysis, control theory, dynamical systems, and automotive control systems.

Hosted by Professors Paul Bogdan