MS in Mechanical Engineering from Purdue. Four years of internships across automotive propulsion, welding automation, and telecom — diagnosing real systems, programming collaborative robots, and shipping the software around them. Now focused on where learned control meets physical hardware.
I came up through mechanical engineering — FEA on engine baffles, packaging redesigns, heat shields for a Formula SAE car. The through-line in everything I've enjoyed most, though, has been the moment a physical system starts making decisions on its own.
At Miller Welding Automation that meant developing over 100 automation parameters for a new collaborative welding robot, then building the cloud database and web frontend the team used to manage them. At Stellantis it meant reading service data across a fleet to find the pattern behind a transmission failure. At Nokia it meant extending an AI chatbot's knowledge base.
I'm now deliberately moving into AI robotics — learned control, perception, and manipulation. This site is where I'll document those experiments as I build them.
A robotics lab notebook is coming here — experiments in learned control, perception, and manipulation as I work through them.
Established an active aerodynamic drag reduction system for the 2022 car, and designed the 2021 car's heat shield in Siemens NX.
Cloud-backed database and web frontend for managing collaborative welding robot parameters, built during the Miller Welding Automation internship.
First AI robotics experiment write-up lands here. Check back, or reach out if you want the early version.
Open to AI robotics roles, collaborations, and conversations with people building physical intelligence. The fastest way to reach me is email.