Jingyang Mao | Engineering | Best Researcher Award

Dr. Jingyang Mao | Engineering | Best Researcher Award

Lecturer Shanghai Institute of Technology China

šŸ§‘ā€šŸ« Dr. Jingyang Mao is a dedicated lecturer at the School of Electrical and Electronic Engineering, Faculty of Intelligence Technology, Shanghai Institute of Technology. With a Ph.D. in Control Science and Engineering from the University of Shanghai for Science and Technology (2022), he specializes in cutting-edge research on networked control systems and cyber-physical systems. His academic journey also includes a visiting scholar tenure at Louisiana State University, USA (2019–2021). Dr. Mao’s work bridges theoretical innovations with practical applications in modern engineering systems.

Profile

Orcid

Education

šŸŽ“ Ph.D. in Control Science and Engineering (2022)

  • University of Shanghai for Science and Technology, Shanghai, China

āœˆļø Visiting Scholar (2019–2021)

  • Department of Electrical and Computer Engineering, Louisiana State University, USA

Experience

šŸ‘Øā€šŸ’» Lecturer (2022–Present)

  • School of Electrical and Electronic Engineering, Shanghai Institute of Technology
  • Focus: Cyber-physical systems, networked control, and adaptive filtering

Research Interests

šŸ” Dr. Mao’s research interests lie in the fields of:

  • Cyber-physical systems 🌐
  • Multi-rate systems ā±ļø
  • Joint recursive filtering šŸ”„
  • Unknown input estimation ā“
  • Adaptive event-triggered mechanisms āš™ļø

Awards

šŸ† Award Nomination: Best Researcher Award
Recognized for groundbreaking contributions to the theory and application of cyber-physical systems.

Publications Top Notes:

šŸ“„ “Recursive filtering of multi-rate cyber-physical systems with unknown inputs under adaptive event-triggered mechanisms”

Event‐based reduced‐order Hāˆž$H_{\infty }$ estimation for switched complex networks based on T‐S fuzzy model

Recursive filtering of multi-rate cyber-physical systems with unknown inputs under adaptive event-triggered mechanisms

Event-Based Distributed Adaptive Kalman Filtering With Unknown Covariance of Process Noises