Yifan Zhang obtained his PhD in Computer Science at the University of Nottingham Ningbo China (UNNC), where he also earned his Bachelor of Computer Science with Artificial Intelligence with First Class honors (09/2016-06/2020). Commenced in September 2020, his doctoral research centers on the automated testing of autonomous driving systems (ADSs), specializing in metamorphic testing (MT), AD simulators, and the exploration of large language models (LLMs) to enhance testing efficiency, thereby ensuring ADS reliability and safety. Dr. Zhang has authored over ten academic papers, eight as the first author, covering these areas, including the integration of LLMs in testing; these have been published in prominent international conferences and journals, including IEEE, ACM, and SCI journals.

Dr. Zhang's practical experience includes significant contributions at NIO. His work there applying MT to the company's ADS and simulator resulted in the discovery of 30% of the total bugs, with 78% of those being critical P0 and P1 level issues. Furthermore, he designed and implemented a scenario-based MT framework at NIO, which markedly improved testing efficiency through the introduction of automated testing and standardized bug reporting processes, reducing the average bug discovery frequency from 5.5 days to 1.6 days.

Previously, Dr. Zhang served as an Intern Software Engineer at the Bank of China Software Development Center in Beijing (07/2019-09/2019). In this role, he participated in the microservices development of a bond system, where he assisted in ensuring smooth service interfaces and data exchange.

Metamorphic Testing, Autonomous Driving Systems, Large Language Models, Software Engineering, Simulation 

https://www.researchgate.net/profile/Yifan-Zhang-58?ev=hdr_xprf