Assistant Professor, Saint Louis University, United States
Tianyi Li is an Assistant Professor in the Department of Civil Engineering at Saint Louis University. His research interests lie at the intersection of transportation engineering, cyber-physical systems (T-CPS), and machine learning, with a focus on traffic modeling, safety, and urban mobility. Prior to his current role, Li earned his Ph.D. in Transportation Engineering from the University of Minnesota, where he worked under the guidance of Dr. Raphael Stern. He holds a Master’s degree from the University of Washington and a Bachelor’s degree from Iowa State University. Li’s interdisciplinary expertise includes the application of data science and machine learning to improve traffic flow, safety, and sustainability. He has gained industry experience working with organizations like Futurewei Technology and the Washington State Department of Transportation. His ongoing research contributes significantly to advancing the understanding of smart cities, transportation systems, and public safety.
Profile
Orcid
Education
Tianyi Li holds a Ph.D. in Transportation Engineering from the University of Minnesota, which he is expected to complete in June 2024. Under the mentorship of Dr. Raphael Stern, his doctoral research explores transportation-cyber-physical systems (T-CPS) and the integration of machine learning in transportation systems. Li also earned a Master of Science in Transportation Engineering from the University of Washington in December 2019, where he worked with Dr. Yinhai Wang. During his undergraduate years at Iowa State University, Li graduated with a Bachelor of Science in Civil Engineering in May 2017, earning cum laude honors and membership in Tau Beta Pi. He also received certifications in Python programming, machine learning, and data science through Coursera. Li’s academic training has provided him with a solid foundation in transportation engineering, data analysis, and the application of cutting-edge technologies like AI and machine learning to transportation systems.
Experience
Tianyi Li has diverse experience both in academia and industry. Since August 2024, he has been serving as an Assistant Professor in the Department of Civil Engineering at Saint Louis University. In this role, he focuses on conducting research and teaching students about transportation systems, cyber-physical systems, and data science. Li’s industry experience includes a summer internship at Futurewei Technology in Bellevue, Washington, where he worked on applied machine learning for smart cities and contributed to a journal paper on taxi mobility services. In addition, he interned with the Washington State Department of Transportation in 2019, focusing on tolling data analysis, performance evaluations, and operational improvements. Li also gained early career experience as an Assistant Engineer Intern with China Railway First Group in 2017, where he participated in site surveys, field measurements, and AutoCAD drawing. His internships provided him valuable hands-on experience in the transportation field, bridging academic research and real-world applications.
Awards and Honors
Tianyi Li has received numerous prestigious awards throughout his academic and professional career. Notable recognitions include the Dwight David Eisenhower Transportation Fellowship (2021-2024), which supports his ongoing research on transportation systems. He also received the Matthew J. Huber Award for Excellence in Transportation Research and Education in 2024, which acknowledges his exceptional contributions to the field of transportation engineering. Li has earned multiple Department of Civil, Environmental, and Geo-Engineering Travel Awards (2023), enabling him to present at international conferences. Additionally, he was awarded the Best Presentation Award at the UMN Transportation Seminar in 2022 for his outstanding work in transportation research. Other honors include the NSF Travel Award, the Transportation Research Board (TRB) Student Travel Award, and the 2022 ITS Minnesota Educational Scholarship. Li has also received multiple Dwight David Eisenhower Transportation Fellowships (2021-2023), which have supported his research initiatives in transportation safety and smart mobility.
Research Focus
Tianyi Li’s research focuses on the intersection of transportation systems, cyber-physical systems (T-CPS), and machine learning. His work aims to integrate advanced data science techniques into transportation engineering to improve traffic flow, safety, and urban mobility. Key research topics include traffic estimation, modeling, and control, with a particular emphasis on leveraging machine learning and deep learning methods to optimize transportation systems. Li has explored the energy impacts of cyberattacks on adaptive cruise control vehicles, as well as the detection of stealthy cyberattacks on automated vehicles. His research also addresses transportation safety, including driver behavior at unsignalized intersections and pedestrian-driver interactions. Li’s focus on sustainable urban mobility involves developing innovative solutions for smart cities, incorporating the integration of public transit signal priority into traffic signal control. Overall, his interdisciplinary research aims to advance transportation infrastructure using emerging technologies to create safer, more efficient, and sustainable urban transportation systems.
Publications
- Identifying built environment factors influencing driver yielding behavior at unsignalized intersections: A naturalistic open-source dataset collected in Minnesota 🛣️🚗
- Exploring Energy Impacts of Cyberattacks on Adaptive Cruise Control Vehicles 🚙💻
- Naturalistic open-source pedestrian-driver yielding dataset collected in Minnesota 🚶♂️🚗
- Car-following-response Based Vehicle Classification via Deep Learning 🧠🚘
- Assessing the Impact of Disruptive Events on Urban Mobility: A Case Study of Chicago Taxis during COVID-19 🚖📉
- Integrating public transit signal priority into max-pressure signal control: Methodology and simulation study on a downtown network 🚍🚦
- Detecting Stealthy Cyberattacks on Automated Vehicles via Generative Adversarial Networks 🛑🤖
- Robustness of vehicle identification via trajectory dynamics to noisy measurements and malicious attacks 🚙🔒
- Taxi Utilization Rate Maximization by Dynamic Demand Prediction: A Case Study in the City of Chicago 🚖📊