PhD Dissertation Defense - Xin Qin

Presentation title:  Data-driven and Logic-based Analysis of Learning-enabled Cyber-Physical Systems
Names of the guidance committee members: Jyotirmoy Deshmukh, Chao Wang, Souti Chattopadhyay, and Yan Liu
 
Abstract: 
Rigorous analysis of cyber-physical systems (CPS) is becoming increasingly important, particularly for safety-critical applications incorporating learning-enabled components. Given a system requirement such as "if the system deviates from the center of the road, it should return to the center in time," we aim to evaluate how well the system satisfies this requirement in uncertain environments. The defense will center around three main pillars: (1) performing verification for initial states and during the runtime of the system, (2) demonstrating how to reuse verification results for unseen systems, and (3) designing new specification languages to alleviate sensitivity to noise.  Since these three pillars all involve a similar approach of black-box modeling and analysis using properties related to specification languages, we anticipate that future work could integrate the results from various stages of this thesis.  This integration would facilitate the sharing and reuse of findings at each stage, thereby enhancing system safety analysis and improving the scalability of the reasoning process.
 
 
 
 
 
 
 

Location: Henry Salvatori Computer Science Center (SAL) - 213

Audiences: Everyone Is Invited

Contact: Ellecia Williams