Yankai Cao

Associate 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

My research group 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. Our algorithms combine mathematical techniques and emerging high-performance computing hardware to achieve computational scalability.

The problems that we are addressing are of unprecedented complexity and defy the state-of-the-art. For example, in our recent work, we developed a novel global optimization algorithm capable of solving k-center clustering problems (an unsupervised learning task) with up to 1 billion samples, while state-of-the-art approaches in the literature can only address several thousand samples.

We are currently using our tools to address engineering and scientific questions that arise in diverse application domains, including optimal decision trees, optimal clustering, deep-learning-based control, optimal power system planning, AI for bioprocess operation, and optimal design of zero energy buildings.

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