Openings

The lab welcomes inquiries from prospective Ph.D. and master’s students, undergraduate researchers, visiting students, and potential collaborators interested in AI-enabled transportation systems, transportation resilience, connected and automated vehicles, traffic flow modeling, and active traffic safety.

Prospective Graduate Students

I plan to recruit one Ph.D. student starting in Spring 2027. The ideal candidate should have strong interests or experience in AI and large language models for transportation, including but not limited to computer vision, vision-language models (VLMs), multimodal LLMs, and transportation safety. The position will focus on applications of these methods in transportation safety and resilience.

Applicants with backgrounds in transportation engineering, civil engineering, computer science, data science, machine learning, statistics, or related fields are encouraged to reach out. Relevant preparation includes experience in programming, including the use of AI-assisted coding tools, mathematical modeling, machine learning, or optimization.

Availability depends on research fit, timing, and funding. In your initial email, please include a one-page statement of research interests, a CV, a transcript if applicable, and links to publications or prior projects.

Undergraduate and Visiting Students

Undergraduate and visiting students may contribute to projects involving data analysis, benchmarking, simulation, literature reviews, and visualization. When contacting the lab, please describe your technical background, expected timeline, and topics of interest.

Collaboration

The group welcomes collaborations on AI for transportation systems, including data-driven modeling, multimodal and agentic AI, large language models, decision-support tools, and their applications in transportation safety, resilience, operations, and infrastructure systems.