Hi, I’m Yilin (Dylan) Wang—a Ph.D. candidate in the Lyles School of Civil & Construction Engineering at Purdue University, advised by Dr. Yiheng Feng.

About Me

With a multidisciplinary background spanning civil engineering (B.S., Tongji University), smart city and deep learning (M.S., Carnegie Mellon University), and intelligent transportation systems (Ph.D., Purdue University), I bring a unique cross-domain perspective to solving complex problems at the intersection of infrastructure, AI, and autonomy. My research lies at the intersection of cooperative perception, Physical AI, and autonomous driving, where I contribute to building intelligent systems that bridge academic research and industry deployment.

I am currently working at SaferDrive.AI on developing the foundation model for the agentic world model on autonomous vehicle safety validation. I am actively seeking Research Scientist positions in both academia and industrial research institutions, where I can leverage my interdisciplinary expertise to advance the frontiers of autonomous systems, cooperative perception, and AI-driven transportation.

You can find my CV here: Yilin Wang’s Resume

Research Interests

My research focuses on intelligent transportation systems and connected & automated vehicles (CAVs), with expertise in:

• Physical AI & Simulation: City-scale agentic world models, neural network-based driving behavior models, naturalistic and adversarial AV testing
• Cooperative Perception: V2X-based sensor fusion, infrastructure-vehicle cooperative perception systems, LiDAR and camera data processing
• Physics-informed Machine Learning: Integrating domain knowledge (e.g., car-following models) into deep learning for trajectory prediction
• Network Operation & Control: Dynamic vehicle routing, traffic signal control, traffic state estimation and optimization

I integrate multi-modal data (e.g., UAV videos, LiDAR, V2X) with machine learning to enhance mobility, safety, and system optimality. I have led projects funded by agencies including FHWA, Michigan DOT, and CCAT, and am passionate about turning rigorous research into deployable systems that improve real-world transportation networks.