Saeid Bayat

Education:
Ph.D. Industrial & Enterprise Systems Engineering, University of Illinois at Urbana-Champaign (2019-Now)
M.S. Mechanical Engineering, Sharif University of Technology, Tehran, Iran (2015-2018)
B.S. Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran (2011-2015)

Research interests:
Control Co-Design · Optimal Control · Wind and Ocean Energy Systems · Multidisciplinary Design Optimization · Surrogate-Based Optimization · Machine Learning ·  Robotics

Research:
My research focuses on optimizing complex engineering systems using advanced design methodologies and optimal control. I aim to enhance the design process by leveraging interdisciplinary insights and developing computational tools. By uncovering hidden synergies in system performance and benefits, I strive to improve design outcomes without being constrained by design heritage.

One of the primary influences on active engineering systems is control. I have worked with various controllers, including open-loop optimal control and model predictive control, to enhance the control and design of engineering systems such as floating offshore wind turbines, vehicle suspensions, and fluid-based thermal management systems. Additionally, I have experience with reinforcement learning and other machine learning methods to extract knowledge from optimization data. Utilizing machine learning for knowledge extraction allows for the creation of radically new designs, free from the limitations of design heritage.

As I approach my graduation in the summer of 2024, I am actively seeking postdoctoral or job opportunities to further apply my expertise. If you are interested in my work or have potential opportunities to collaborate, please feel free to reach out. Your insights and connections are invaluable as I continue to explore and contribute to the world of engineering design and optimization.

Links:
[Google Scholar Profile]
[GitHub]