Toyota Motor North America

Built and calibrated a machine-learning drag prediction model, validated against CFD and wind tunnel data, on Toyota's Vehicle Aerodynamics team.

As an Aerodynamics co-op on Toyota's Vehicle Aerodynamics team within Research and Development, I developed and calibrated a machine-learning drag prediction model, using more than fifteen CFD simulations to correlate its results. I designed more than four parametric components in CATIA GSD, giving the team a way to iterate geometry quickly across simulations, and supported wind tunnel validation for a new model-year vehicle, correlating the physical results against the ML predictions. I also built an aerodynamic database with automated data extraction from more than 1,000 technical reports using agentic AI, and created internal tools for post-processing and test execution that saved an estimated 100-plus engineering hours per month.