Fei Ye
About Fei Ye
Fei Ye is a Research Scientist currently working at TuSimple, specializing in predictive models and planning algorithms for autonomous vehicles. She has a strong background in optimization and data analysis, with previous experience at institutions such as the University of California, Berkeley and Mitsubishi Electric Research Laboratories.
Current Role at TuSimple
Fei Ye is currently employed as a Research Scientist at TuSimple, a company focused on developing autonomous driving technology. She has held this position since 2020 and is based in the San Francisco Bay Area. In her role, she develops predictive models and planning algorithms for autonomous vehicles, utilizing deep learning and deep reinforcement learning techniques.
Education and Expertise
Fei Ye holds a Master of Arts degree from Wheaton College. Her academic background includes a strong emphasis on optimization and data analysis. She has developed expertise in spatial temporal data mining and human behavior analysis, which supports her research in autonomous vehicle technology.
Research Experience at University of California, Berkeley
Fei Ye worked as a Postdoctoral Researcher at the University of California, Berkeley from 2019 to 2020. During this year, she focused on vehicle trajectory prediction and anomaly detection, contributing to advancements in the field of autonomous driving.
Previous Research Positions
Prior to her role at TuSimple, Fei Ye held several research positions. She was a Research Assistant at the University of California, Riverside from 2015 to 2019, and at Northeastern University in the Action Lab from 2013 to 2014. Additionally, she interned at Mitsubishi Electric Research Laboratories in 2018 for three months, where she further developed her research skills.
Ph.D. Research Focus
Fei Ye's Ph.D. research included significant work on cooperative lane change and graph-based motion planning. Her research contributed to the understanding of vehicle behavior in complex driving scenarios, enhancing the capabilities of autonomous systems.