معرفی
Eunshin Byon is a Professor and Richard Wilson Faculty Scholar in Industrial and Operations Engineering at the University of Michigan, serving as Director of Masters Programs. Her expertise spans reliability engineering, data-driven decision-making, and stochastic systems optimization for critical infrastructure including renewable energy grids and manufacturing processes.
Education:
- Ph.D. in Industrial and Systems Engineering, Texas A&M University
- M.S. in Industrial and Systems Engineering, KAIST
- B.S. in Industrial and Systems Engineering, KAIST
Dr. Byon's research pioneers digital twin calibration and uncertainty quantification methodologies, integrating stochastic simulation with big data analytics to solve complex reliability challenges. Her work bridges theoretical advances in sequential computer experiments with practical applications in energy sustainability and predictive maintenance, emphasizing variance reduction techniques and operational decision frameworks for interconnected systems.
Recent publications reveal a concentrated focus on parameter calibration innovations and computational efficiency in stochastic modeling, demonstrating consistent advancement in digital twin technologies for energy systems. These works increasingly incorporate machine learning elements while maintaining rigorous statistical foundations for uncertainty propagation.
Scientific Awards:
- teaching award from the QCRE division of IISE (2024)
- Richard Wilson Faculty Scholar (2023)
- 2022 Best Associate Editor award, INFORMS Journal on Data Science
- 2021 Department Faculty Award
Dr. Byon has mentored multiple doctoral candidates to successful completion, with students placing at Clemson University and Zhejiang University. Her research is sustained by significant external funding including an NSF grant for digital twin calibration (2023-2025) and Ford Motor Company research support (2023-2025), focusing on real-world implementation of stochastic optimization frameworks.
She leads a specialized research group developing next-generation reliability assessment tools, collaborating with industry partners on renewable energy integration challenges while advancing fundamental methodologies in stochastic simulation and data analytics for complex engineered systems.
