Research interests + projects
Dr. Cao’s research focuses on the design and implementation of large-scale local and global optimization algorithms to tackle problems that arise in diverse decision-making paradigms such as machine learning, stochastic optimization, and optimal control. His algorithms combine mathematical techniques and emerging high-performance computing hardware to achieve computational scalability. His goal is also to make these developments accessible to academic and industrial users by implementing algorithms on easy-to-use and extensible software libraries.
Furthermore, Dr. Cao has applied the algorithms and tools to address engineering and scientific questions that arise in diverse application domains, including deep-learning-based control, optimal power system planning, AI-based modelling for biorefining, optimal design of zero energy buildings, image classification for contaminant detection, conflict resolution in energy system design, and predictive control of wind turbines.
Selected publications + presentations
K. Hua, M. Shi, and Y. Cao. “A Scalable Deterministic Global Optimization Algorithm for Clustering Problems.” International Conference on Machine Learning (ICML), 2021. http://proceedings.mlr.press/v139/hua21a.html
M. Mehrtash, and Y. Cao. “A New Global Solver for Transmission Expansion Planning with AC Network Model.” IEEE Transactions on Power Systems, 37(1), 282 – 293, 2021. https://doi.org/10.1109/TPWRS.2021.3086085
Y. Cao, S. B. Lee, V. R. Subramanian, and V. M. Zavala. “Multiscale Model Predictive Control of Battery Systems for Frequency Regulation Markets using Physics-Based Models.” Journal of Process Control, 90, 46-55, 2020. https://doi.org/10.1016/j.jprocont.2020.04.001
Y. Cao and V. M. Zavala. “A Scalable Global Optimization Algorithm for Stochastic Nonlinear Programs.” Journal of Global Optimization, 75, 393-416, 2019 https://doi.org/10.1007/s10898-019-00769-y
Y. Cao, H. Yu, N. L. Abbott, and V. M. Zavala. “Machine Learning Algorithms for Liquid Crystal-Based Sensors.” ACS Sensors 3(11), 2237-2245, 2018. https://doi.org/10.1021/acssensors.8b00100