Jingyang Mao | Engineering | Best Researcher Award

Dr. Jingyang Mao | Engineering | Best Researcher Award

Lecturer Shanghai Institute of Technology China

🧑‍🏫 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

🎓 Ph.D. in Control Science and Engineering (2022)

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

✈️ Visiting Scholar (2019–2021)

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

Experience

👨‍💻 Lecturer (2022–Present)

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

Research Interests

🔍 Dr. Mao’s research interests lie in the fields of:

  • Cyber-physical systems 🌐
  • Multi-rate systems ⏱️
  • Joint recursive filtering 🔄
  • Unknown input estimation
  • Adaptive event-triggered mechanisms ⚙️

Awards

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

Publications Top Notes:

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

Event‐based reduced‐order H∞$H_{\infty }$ 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