Bio

Wentao Tang was born in Hunan Province, P.R. China. He was a process control engineer at Shell Global Solutions (U.S.) Inc., where he undertook multiple research projects for the development of Shell’s advanced process control software, prior to joining NC State University.

Area(s) of Expertise

Wentao Tang's current research focuses on developing data-driven control algorithms that integrate nonlinear control theory with machine learning techniques, which avoid detailed dynamic modeling procedures and can be more flexible for systems with complex dynamics. He is also interested in derivative-free algorithms for optimization problems without explicit algebraic models, especially in how the solution of large-scale problems can benefit from the identification of underlying network topology, decomposition of networks into constituent subsystems and adoption of acceleration schemes.

Publications

• A Decomposition-Based Data-Driven Branch-and-Bound Approach for Black-Box Global Optimization , arXiv (Cornell University) (2026)
• A constrained symbolic regression approach for Lyapunov function discovery , arXiv (Cornell University) (2026)
• Convex Hybrid Modeling: An Operator-Based Approach , arXiv (Cornell University) (2026)
• Data-Driven Domain of Attraction Estimation via Convergent Koopman-Zubov Approximation , arXiv (Cornell University) (2026)
• Koopman Modeling and Stabilization of Discrete-Time Nonlinear Control Systems: Bilinearity on a Reproducing Kernel Hilbert Space , arXiv (Cornell University) (2026)
• Koopman Observability of Nonlinear Systems and Data-Driven Koopman--Luenberger Observer , arXiv (Cornell University) (2026)
• Koopman-based Estimation of Lyapunov Functions: Theory on a Reproducing Kernel Hilbert Space , arXiv (Cornell University) (2026)
• Machine learning‐based optimal control for colloidal self‐assembly , AIChE Journal (2026)
• Neural Luenberger state observer for nonautonomous nonlinear systems , Journal of Process Control (2026)
• Stochastic MPC under Heavy-Tailed Disturbances: An Extreme Value Theory Approach , arXiv (Cornell University) (2026)