Wallace Tan Gian Yion

Wallace Tan is a graduate student in chemical engineering and computational science and engineering, where he develops advanced stochastic control algorithms for complex chemical systems. His research focuses on stochastic model predictive control (SMPC) and uncertainty quantification using polynomial chaos expansions, particularly for systems governed by partial differential equations. Wallace designs control frameworks that preserve theoretical guarantees while enabling real-time decision-making under parametric and dynamic uncertainty. As a MathWorks Fellow, Wallace will use MATLAB to implement SMPC algorithms, simulate closed-loop control systems, and validate performance under stochastic disturbances. He leverages the Symbolic Math, PDE, and Optimization Toolboxes to prototype control architectures, perform uncertainty quantification, and solve large-scale optimization problems. Wallace’s work could significantly improve the robustness and scalability of control strategies in biomanufacturing, pharmaceutical production, and other industries where high-dimensional uncertainty poses a barrier to automation and quality control.