Scholar
Xintao Yan
Google Scholar ID: KZSpfTYAAAAJ
Assistant Professor, The University of Hong Kong
Citations & Impact
Citations
1,420
H-index
8
i10-index
8
Publications
20
Co-authors
11
list available
Publications
Resume (English only)
Academic Achievements
- Publications:
- - 'Learning Naturalistic Driving Environment with Statistical Realism', Nature Communications, 2023.
- - 'Dense Reinforcement Learning for Safety Validation of Autonomous Vehicles', Nature, 2023.
- Academic Achievements:
- - Successfully defended dissertation titled: 'Simulating Naturalistic Driving Environment for Autonomous Vehicles' in October 2023.
- - Will present work titled 'Learning naturalistic driving environment with statistical realism' at IROS 2023 and INFORMS 2023 Annual Meeting in October 2023.
- - Released code for NeuralNDE and D2RL papers in September 2023.
- - Paper on driving environment simulation highlighted and chosen as the front page of the Nature Communications website in June 2023.
- - Paper on driving environment simulation published in Nature Communications and featured in Editor's Highlights in April 2023.
- - Paper on autonomous vehicle testing published in Nature and selected as the cover of March 23, 2023 Issue in March 2023.
Research Experience
- Currently a Postdoctoral research fellow at the University of Michigan, Ann Arbor, working in the Michigan Traffic Lab, advised by Prof. Henry Liu.
Education
- Received Bachelor's degree from the School of Vehicle and Mobility, Tsinghua University in 2018; completed Ph.D. in 2023 from the same lab at the University of Michigan, Ann Arbor, advised by Prof. Henry Liu.
Background
- Research interests revolve around the intersection of automotive engineering, transportation engineering, and artificial intelligence, with a primary emphasis on Connected and Automated Vehicle (CAV) and infrastructure. Aims to enhance the safety performance of CAVs through innovative training and testing methods while understanding their impact on human travel behavior when deployed at scale.
Miscellany
- Contact: Email, Google Scholar, Github
