Teaching
Teaching Assistant
- CEGE 5180 (4160) Methods for intelligent transportation systems, UMN, Fall 2023
- Graduate- and undergraduate-level
- ITS methods of system control and machine learning
- CEGE 3201 Transportation Engineering, UMN, Spring 2021
- Undergraduate-level
- Traffic Engineering Topic: Geometric factors, traffic flow theory
- ITS Topic: Automated vehicles and intelligent transportation systems
- CEE 410 Traffic Engineering Fundamentals, UW, Spring 2019
- Graduate- and undergraduate-level
- Topic: Concepts of HCM calculation, traffic flow theory, transportation engineering and statistics
Guest Lecturer
- Engineering Discovery Days, University of Washington, Spring 2019
- Topic: Transportation Data Science
Undergraduate Student Supervision
Alexander Halatsis, Aerospace Engineering, University of Minnesota, Summer & Fall 2023.
- Li, T., Halatsis, A., & Stern, R. (2023). RACER: Rational Artificial Intelligence Car-following-model Enhanced by Reality.
- Benjamin Rosenblad, Civil Engineering, University of Minnesota, Fall 2022.
- Li, T., Rosenblad, B., Wang, S., Shang, M., & Stern, R. (2023). Exploring Energy Impacts of Cyberattacks on Adaptive Cruise Control Vehicles. The IEEE Intelligent Vehicles Symposium (IV 2023). doi: 10.1109/IV55152.2023.10186730.
- Matthew Fillippeli, Civil Engineering, University of Minnesota, Spring 2022.
- Li, T., Shang, M., Wang, S., Filippelli, M. & Stern, R. (2022, October). Detecting Stealthy Cyberattacks on Automated Vehicles via Generative Adversarial Networks. In 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), pp. 3632-3637, IEEE
- Joshua Klavins, Civil Engineering, University of Minnesota, Spring 2021 - Summer 2022.
- Li, T., Klavins, J., Xu, T., Davis, G., & Stern, R. (2022). [Understanding driver-pedestrian interactions to predict driver yielding: field experiments in Minnesota]. Under Review
- John Cullom, Computer Science, University of Minnesota, Fall 2020 - Spring 2021.
- Li, T., Cullom, J., & Stern, R. (2021, May). Leveraging video data to better understand driver-pedestrian interactions in a smart city environment. In Proceedings of the Workshop on Data-Driven and Intelligent Cyber-Physical Systems (DI-CPS), pp. 6-11, ACM