Yuhan Tang

Yuhan Tang is a graduate student in transportation tackling operational efficiency in transit systems through integer optimization, deep reinforcement learning, and urban mobility modeling. He developed a joint bus scheduling and vehicle assignment framework using MATLAB’s Optimization Toolbox to formulate mixed-integer quadratic programs that achieve 10-50x computational speedup while maintaining solutions within 2% of global optimality, meaningful improvements for cities managing thousands of buses daily. For real-time adaptive control, Yuhan leverages Simulink to build physics-informed environments incorporating macroscopic fundamental diagram surrogates, enabling efficient training of decentralized reinforcement learning agents that reduce passenger wait times by 13% on real-world data. As a MathWorks Fellow, Yuhan will further develop a unified computational framework that links optimization-based scheduling, data-driven operational models, and learning-based control. The framework will draw on MATLAB’s optimization, machine learning, and data-analysis capabilities together with Simulink to prototype, test, and evaluate decision-support tools for transit and ride-hailing systems under realistic, time-varying operating conditions. His work could help translate operational data, technical system specifications, and human planning objectives into scalable algorithms that improve the reliability and efficiency of urban mobility systems.