I'm a Civil Engineering Ph.D. candidate at Stevens Institute of Technology, working in the Smart Infrastructure Lab with Prof. Yi Bao. My research builds language-model agents and physics-guided AI for structural engineering — systems that automate design while staying interpretable and verifiable against engineering codes. My work has appeared in Automation in Construction, Advanced Engineering Informatics, and Engineering Structures.
Download CVStructural design is slow, costly, and hard to audit, and the machine-learning tools meant to accelerate it often behave as black boxes. My work asks how AI can do real structural engineering while staying transparent and checkable: multi-agent language-model frameworks that reason over design codes to produce verifiable reinforced-concrete designs, and physics-guided, interpretable models that ground predictions in mechanics while confronting the data scarcity and bias specific to this field.
An LLM framework that decomposes structural design into specialized agents, reasons over design codes, and returns code-compliant, interpretable reinforced-concrete designs.
J. Chen, Y. Bao · 2025Quantifies how biased training data undermines structural ML and develops sampling strategies for more reliable, generalizable models.
J. Chen, Y. Bao · 2024Notes and essays on AI for structural engineering.
Open to collaborations, research discussions, and questions about any of the work above.
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