Guoqing Hu

Optimization Researcher at Cornell University

Formulated and solved nonlinear optimization problems for complex dynamic systems using model predictive control and gradient-based solvers; integrated physics-informed models into optimization frameworks to enhance accuracy and convergence stability; benchmarked solver formulations and implemented adaptive constraint strategies; developed and validated simulation frameworks against high-fidelity data.

Optimization Engineer at Cornell University

Designed and implemented robust and stochastic optimization frameworks for multi-variable dynamic systems under uncertainty; applied machine-learning-based uncertainty modeling to construct data-driven optimization constraints; performed solver performance benchmarking and sensitivity analysis; achieved up to 40% reduction in computational time through model reformulation and sparse-matrix techniques.

Modeling & Simulation Engineer at Cornell University

Developed hybrid physics–data models to support optimization-based control of multi-zone systems; built and validated simulation environments for dynamic state estimation and control testing in MATLAB/Simulink; integrated optimization routines into simulation workflows to improve solver convergence and real-time control accuracy.

Optimization Researcher at Cornell University

Formulated and solved nonlinear optimization problems for complex dynamic systems using model predictive control (MPC) and gradient-based solvers (CVXPy, IPOPT). Integrated physics-informed models into optimization frameworks to enhance accuracy and convergence stability. Benchmarked solver performance across different formulations (LP, QP, NLP) and implemented adaptive constraint strategies to improve computational efficiency. Developed and validated simulation frameworks to assess optimization quality against high-fidelity experimental and synthetic datasets.

Optimization Engineer at Cornell University

Designed and implemented robust and stochastic optimization frameworks for multi-variable dynamic systems under uncertainty. Applied machine learning-based uncertainty modeling (PCA, KDE) to construct data-driven optimization constraints. Conducted performance evaluation and sensitivity analysis on solver behavior using MATLAB and Python. Achieved up to 40% reduction in computational time through model reformulation and sparse matrix techniques.