Dr. Yarbasi’s expertise lies in uncertainty quantification for complex systems, verification and validation (V&V) processes, robotics, energy systems modeling within smart building infrastructures, and systems engineering. Prior to joining U-M, he collaborated extensively with government and industry on a range of projects, including the development of a surgical endoscopy robot and the energy optimization of a net-zero-energy building. He worked with international experts on a NATO STO project to create a validation database for simulation environments in air and sea vehicle design. His dissertation expanded this work by developing a comprehensive methodology to identify critical uncertainties in complex multidisciplinary designs and guide experimentation to mitigate these uncertainties.
Dr. Yarbasi’s research interests are born out of a deep passion for data and its potential to drive innovation in engineering. He leverages data-intensive methodologies to enhance the performance and safety of autonomous vehicles, smart city infrastructure, and Advanced Air Mobility (AAM) systems. By applying data analytics, machine learning, and optimization techniques, he aims to develop transportation systems that are efficient, reliable, sustainable, and responsive to real-time conditions.