Efe Yarbasi is an Assistant Research Scientist in the Engineering Systems Group at the University of Michigan Transportation Research Institute (UMTRI). His research covers the safety of automated and driver-assistance systems and the electrification of transportation. He serves as principal investigator and co-principal investigator on projects sponsored by NHTSA, Toyota Motor North America, and the National Cooperative Highway Research Program. He co-leads the Low Altitude Airspace Working Group at M-Air.
He holds a PhD in Aerospace Engineering from Georgia Tech. There he worked in the Aerospace Systems Design Laboratory under Prof. Dimitri Mavris and developed methods to find and reduce the uncertainties that matter most in complex, multi-disciplinary designs. He is a member of AIAA and IEEE.
Students, please get in touch. I am looking for curious, motivated students to join my research. Projects range from crash analysis and AI for safety to EV charging and low-altitude airspace, and I mentor undergraduate and graduate students. If any of this sounds interesting, send me an email with a short note about your background and what you want to work on.
A two-phase multi-agent LLM framework that reconstructs pre-crash scenarios from crash reports, scene diagrams, and event data recorder records (277 CISS rear-end crashes).
A methodology for designing computational or physical experiments that mitigate system-level uncertainty, demonstrated on an early-stage Blended-Wing-Body aircraft concept.
A probabilistic reasoning approach using large language models to explore drivers' hazardous actions in two-vehicle crashes.
A systematic methodology to identify and mitigate epistemic uncertainty in complex, multi-disciplinary aircraft design.
A systems engineering approach to selecting appropriate physics models and fidelity levels for coupled multi-disciplinary simulations.
A structured process developed under NATO STO AVT-297 for defining physical experiment data requirements to validate computational models.
Identifying the critical system-level uncertainties in a coupled aerostructural analysis to decide which validation experiments to run.
Operational analysis and digital-twin modeling supporting the Living Building Challenge net-positive energy certification of Georgia Tech's Kendeda Building.
Modeling and evaluation of underground cistern water tanks as thermal energy storage for net-positive energy and water buildings.
A scalable thermal-fluid network model for simulating building-to-grid heat exchange across campus district cooling loops.