About
Dr. Esmaeil Ghorbani currently works as a Postdoctoral Research Associate in the Department of Civil and Environmental Engineering at Princeton University, focused on developing digital twins for complex systems. Before that he was working as postdoctoral fellow at Polytechnique Montréal, focusing on developing a data-driven digital twin for a hydro turbine, collaborating with Ph.D. and M.Sc. students. Previously, as a registered Professional Engineer (P.Eng.), he spent three years at KGS Group, a consulting firm in the hydro industry, contributing to projects across Canada for BCHydro, MBHydro, SaskPower, and OPG, with a focus on mech-structural engineering. Esmaeil earned his PhD in structural engineering from the University of Manitoba in 2021, where he specialized in data-driven methods for damage quantification of civil infrastructures. During his PhD, he was honored with the University of Manitoba Graduate Fellowship (UMGF) for three consecutive years, one of the university’s most prestigious awards. Before his PhD, he worked at TurboTech Company for four years, gaining experience in vibration analysis of rotating machinery. He holds both a master’s and bachelor’s degree in mechanical engineering.
News
Research Presented at the University of Vermont
Research Interests
Data-driven modeling for mechanical, structural, and electromechanical systems
Structural dynamics, vibration, and structural health monitoring
Teaching
With B.Sc. and M.Sc. degrees in Mechanical Engineering and a Ph.D. in Civil Engineering, together with his teaching and professional engineering experience, Dr. Ghorbani is prepared to teach a wide range of courses in engineering design, numerical modeling, computational analysis, and simulation. He is also developing new courses that bring machine learning, data-driven modeling, uncertainty quantification, and system-of-systems concepts into engineering education, helping students connect classical engineering principles with modern computational and data-based tools.
Media
Video 0: Kalman Filtering Overview And Plan
Video 1: History of Kalman Filter
Video 2: The Kalman Filter Algorithm
Video 3: Linear Kalman Filter for State Estimation
Video 4: Nonlinear Kalman Filters
Video 5: The Unscented Kalman Filter
Latest on LinkedIn
Accepted a postdoctoral position at Princeton University
I am very happy to share that I’ve accepted a postdoctoral research position at Princeton University, where I’ll expand my work on digital twins for critical energy infrastructure in collaboration with an amazing team.
Read on LinkedIn