Yankai Cao

Assistant Professor

Office: CHBE 237

Email: yankai.cao@ubc.ca

Website: https://optimal.chbe.ubc.ca

Research Summary

Machine Learning, Large-scale Optimization, Energy Systems, Process Control


University of Wisconsin Madison, 2018, Postdoctoral Associate

Purdue University, 2015, Ph.D.

Zhejiang University, 2010, B.E.

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, global optimal AI, optimal power system planning, AI-based design of biomass-based carbon removal processes, image classification for contaminant detection, optimal design of zero energy buildings, and modelling of battery systems.

Full Publications Link

Selected publications + presentations

K. Hua, J. Ren, and Y. Cao. “A Scalable Deterministic Global Optimization Algorithm for Training Optimal Decision Tree on Large Datasets.” Conference on Neural Information Processing Systems (NeurIPS), 2022.

Y. Li, K. Hua, and Y. Cao. “Using stochastic programming to train neural network approximation of nonlinear MPC laws.” Automatica, 146, 110665, 2022. https://doi.org/10.1016/j.automatica.2022.110665

M. Shi, K. Hua, J. Ren, and Y. Cao. “Global Optimization of K-Center Clustering.” International Conference on Machine Learning (ICML), 2022. https://proceedings.mlr.press/v162/shi22b.html

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