Jun Liu
Scholar

Jun Liu

Google Scholar ID: Ytr7YQ0AAAAJ
Professor of Applied Mathematics, University of Waterloo
systems and control theoryformal methodsoptimization and learningroboticstrustworthy AI
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Citations & Impact
All-time
Citations
2,026
 
H-index
24
 
i10-index
55
 
Publications
20
 
Co-authors
0
 
Contact
Emailj.liu@uwaterloo.caTwitterOpen ↗
Publications
6 items
Adaptive multi-resolution Gaussian processes: Scalable exact inference with naturally data-sparse covariance matrices
2026
Cited
0
Set-Based Value Function Characterization and Neural Approximation of Stabilization Domains for Input-Constrained Discrete-Time Systems
2026
Cited
0
Verifiable Error Bounds for Physics-Informed Neural KKL Observers
2026
Cited
0
Verifiable Error Bounds for Physics-Informed Neural Network Solutions of Lyapunov and Hamilton-Jacobi-Bellman Equations
2026
Cited
0
Rigorous Error Certification for Neural PDE Solvers: From Empirical Residuals to Solution Guarantees
2026
Cited
0
Safe and Robust Domains of Attraction for Discrete-Time Systems: A Set-Based Characterization and Certifiable Neural Network Estimation
2026
Cited
0
Resume
Background
  • Professor of Applied Mathematics at the University of Waterloo
  • Canada Research Chair in Hybrid Systems and Control
  • Director of the Hybrid Systems Lab at the University of Waterloo
  • Main research interests in the theory and applications of hybrid systems and control
  • Focuses on rigorous computational methods for control design with applications in cyber-physical systems and robotics
  • Also works on optimization and learning theory with applications in robotics, data science, and AI