About Dr. Qianwen (Cami) Li
About Dr. Qianwen (Cami) Li
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Qianwen Li is an Assistant Professor at the School of Environmental, Civil, Agricultural and Mechanical Engineering (ECAM) at the University of Georgia (UGA).
Email: Cami.Li@uga.edu; Office: Boyd Research and Education Center 716B
Dr. Li’s research interest includes:
AI-powered Traffic Data Collection and Analytics
Connected, Automated, Electric, and Shared Mobility
Transportation Safety
Traffic Flow Theory
Google Scholar page is here.
Education
Education
Ph.D., University of South Florida
Civil Engineering (Transportation), 2022
Milton Pikarsky Memorial Award for the best doctoral dissertation
M.S., University of South Florida
Civil Engineering (Transportation), 2020
Neville A. Parker Award for the best master’s project report
B.S., Shandong University
Computer Science & Technology, 2018
Appointment
Appointment
Assistant Professor
School of Environmental, Civil, Agricultural and Mechanical Engineering, University of Georgia, 2023 – Present
Assistant Research Professor
Center for Urban Transportation Research, University of South Florida, 2022 – 2023
Teaching
Teaching
ENGR8990, Traffic Flow Theory
This course is designed to provide an in-depth exploration of highway traffic flow, covering foundational concepts and a diversity of models. It will examine traffic dynamics from both individual vehicle (microscopic) and overall traffic stream (macroscopic) viewpoints, offering insights into various traffic flow theories.
ENGR8990, Transportation Planning
This course equips students with the knowledge and skills to analyze, design, and implement effective transportation systems. Students will explore the principles of transportation planning, including land use, environmental impact, multimodal systems, and policy frameworks, with a focus on creating sustainable and equitable transportation solutions.
CVLE 4900, Transportation Safety
This course provides a comprehensive overview of transportation safety, focusing on key concepts, theories, and management strategies. It delves into human factors and the behavior of vulnerable road users, highlighting their impact on safety. Through a blend of theory and practical applications, students are equipped to improve transportation safety effectively.
Hiring
Hiring
I am seeking to recruit highly motivated Ph.D. students. Candidates with backgrounds in machine learning, optimization, and control theory are especially encouraged to apply. If you are interested, please send your CV, class ranking and transcripts (undergraduate and graduate) to Cami.Li@uga.edu.
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