Jiayang Ren
Department of Chemical and Biological Engineering, University of Wisconsin–Madison
Postdoctoral Researcher
Co-advised by Prof. Styliani Avraamidou and Prof. Victor M. Zavala
About Me
Jiayang Ren joined the University of Wisconsin–Madison as a Postdoctoral Researcher in October 2026. He completed his doctoral research in Chemical and Biological Engineering at the University of British Columbia under the supervision of Prof. Yankai Cao. He received his M.S. in Control Engineering and B.S. in Automation from Zhejiang University, China.
His research focuses on mathematical optimization, interpretable machine learning, and control. His previous work includes developing scalable optimization algorithms for large-scale clustering, optimal decision trees, and learning-based control. His current research focuses on decision-focused surrogate-based optimization, particularly developing interpretable machine learning methods for bilevel optimization.
Outside of research, Jiayang enjoys hiking, swimming, playing tennis, and cooking, especially spicy dishes.
Research Interests
Global Optimization, Interpretable Machine Learning, Decision-Focused Optimization, Bilevel Optimization, Learning-Based Control
Selected Publications
[1] Ren, J., Osuna-Enciso, V., and Cao, Y. A Moving-Horizon Differential Evolution Algorithm for Training Deep Classification Trees on Large Datasets. Expert Systems with Applications, published online 2026, 2027 issue.
[2] Zou, C., Ren, J., Mao, Q., Liu, J., Lai, M., and Cao, Y. A Moving-Horizon Approximate Branch-and-Reduce Method for Deep Classification Trees. Transactions on Machine Learning Research (TMLR), August 2026.
[3] Ren, J., You, N., Hua, K., Ji, C., and Cao, Y. A Global Optimization Algorithm for K-Center Clustering of One Billion Samples. Management Science, 72(6), 5106–5130, 2026.
[4] Ren, J., Mao, Q., Zhao, T., and Cao, Y. Exact Learning of Linear Model Predictive Control Laws Using Oblique Decision Trees with Linear Predictions. IEEE Conference on Decision and Control (CDC), 2025.
[5] Mao, Q., Ren, J., Wang, Y., Zou, C., Zheng, J., and Cao, Y. Differentiable Decision Tree via ‘ReLU+Argmin’ Reformulation. Advances in Neural Information Processing Systems (NeurIPS), 2025. Spotlight Paper.
[6] Ren, J., Hua, K., and Cao, Y. Global Optimal K-Medoids Clustering of One Million Samples. Advances in Neural Information Processing Systems (NeurIPS), 2022.
[7] Hua, K., Ren, J., and Cao, Y. A Scalable Deterministic Global Optimization Algorithm for Training Optimal Decision Tree. Advances in Neural Information Processing Systems (NeurIPS), 2022.