OptiChat: Making Optimization Models Explainable with Agentic AI (in collaboration with Chewy)
OptiChat: This project explores how agentic AI, optimization, and data analysis can be combined to make large-scale decision models more interpretable in real-world settings. In collaboration with Chewy, students will help extend the OptiChat framework, which uses large language models and structured tool use to explain optimization results, compare model runs, interpret sensitivity information, and draw insights from historical solutions without requiring repeated re-solves. The project is especially relevant for students interested in optimization, machine learning, and practical decision-support systems, and offers hands-on experience at the intersection of optimization, AI, and industry applications.
Can Li
Data Analytics
Faculty-led Research
Fall 2026
Industry Sponsored
Large Language Models
Machine Learning
Optimization
West Lafayette