Yigit Yucesan
About Yigit Yucesan
Yigit Yucesan is a Postdoctoral Research Associate at Oak Ridge National Laboratory, where he integrates machine learning with physics to improve predictive maintenance of industrial systems. He holds a PhD in Mechanical Engineering from the University of Central Florida and has experience as a structural engineer and teaching assistant.
Work at Oak Ridge National Laboratory
Yigit Yucesan serves as a Postdoctoral Research Associate at Oak Ridge National Laboratory, a prominent research facility in Oak Ridge, Tennessee. He has held this position since 2021, focusing on integrating machine learning with physics to improve predictive maintenance of industrial systems. His research aims to develop innovative methods that enhance the reliability and efficiency of industrial equipment.
Education and Expertise
Yigit Yucesan has a strong educational background in engineering. He earned a Bachelor's degree in Aerospace, Aeronautical and Astronautical Engineering from Orta Doğu Teknik Üniversitesi, studying from 2010 to 2015. He further advanced his education by obtaining a Master's degree in Mechanical Engineering from TOBB Ekonomi ve Teknoloji Üniversitesi between 2016 and 2018. He completed his Doctor of Philosophy (PhD) in Mechanical Engineering at the University of Central Florida from 2018 to 2021.
Background in Engineering
Prior to his current role, Yigit Yucesan gained practical experience as a Structural Engineer at TUSAS - Turk Havacilik ve Uzay Sanayii A.S. (Turkish Aerospace Industries, Inc.) from 2016 to 2018. He also worked at the University of Central Florida as a Graduate Teaching Assistant from 2020 to 2021 and as a Graduate Research Assistant from 2018 to 2020. These roles contributed to his expertise in mechanical and aerospace engineering.
Research Focus and Contributions
Yigit Yucesan's research primarily focuses on developing physics-informed machine learning methods aimed at prognostics and health management of industrial equipment. His work integrates advanced computational techniques with physical principles to enhance the predictive capabilities of maintenance systems, thereby contributing to improved operational efficiency in industrial settings.