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Supervisor of Master's Candidates

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Date of Employment:2024-07-30

School/Department:Hangzhou International Innovation Institute of Beihang University

Administrative Position:Associate Research Fellow

Business Address:No.166, Shuanghongqiao Street, Pingyao Town, Yuhang District, Hangzhou

Gender:Male

Contact Information:yang_hu@buaa.edu.cn

Status:Employed

Alma Mater:Politecnico di Milano

Discipline:Mechanical Engineering
Electronic Science and Technology
Computer Science and Technology
Transportation Engineering
Control Science and Engineering

Hu Yang

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Gender:Male

Alma Mater:Politecnico di Milano

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Current position: Home / Personal Profile

I earned my Ph.D. in 2015 from Politecnico di Milano, Italy, with dissertation titled "Development of Prognostics and Health Management (PHM) Methods for Engineering Systems Operating in Evolving Environments". Now I’m serving as an associate research fellow in Hangzhou International Innovation Institute of Beihang University. As a principal investigator or co-investigator, I have led 18 research projects, including those funded by National Natural Science Foundation of China, "Young Elite Scientists Sponsorship Program" by the China Association for Science and Technology, and Science and Technology Innovation Project of the Central Goverment's Science and Technology Commission. My contributions to the field have been recognized with several awards, including a second-class prize from the Chinese Society of Aeronautics and Astronautics and two second-class prizes for Scientific and Technological Progress. I have also a published over 20 papers as first author or corresponding author in renowned international journals and conferences, with an H-index of 16. I hold 9 national invention patents and have supervised 3 postdoctoral researchers and co-supervised 2 doctoral students.

 

My research interests include the development of intelligent support systems for complex aviation systems, PHM framework design, hybrid diagnostics/prognostics approaches, and the application of deep learning and smart optimization algorithms for PHM in engineering systems.