I am a Ph.D. student (2023-) in Machine Learning and Public Policy at Carnegie Mellon University, advised by Woody Zhu. Previously, I received my B.S. in Statistics at University of Science and Technology of China. My research interest lies in the intersection of machine learning, statistics, and operations research. I work on probabilistic machine learning, uncertainty quantification, sequential modeling, and causal inference, with a focus on their applications in the operations and management of power systems, disaster response, and other high-risk societal challenges.
[1] Conformalized Decision Risk Assessment [arXiv] [poster]
Wenbin Zhou, Agni Orfanoudaki, Shixiang Zhu
[2] Sequential Change Point Detection via Denoising Score Matching [arXiv] [code]
Wenbin Zhou, Liyan Xie, Zhigang Peng and Shixiang Zhu
[3] Hierarchical Spatio-Temporal Uncertainty Quantification for Distributed Energy Adoption [arXiv] [poster]
Wenbin Zhou, Shixiang Zhu, Feng Qiu and Xuan Wu
IEEE Power & Energy Society General Meeting (PESGM), 2025
🧑💻 Featured in AES Indiana’s 2025 Integrated Resource Plan (IRP). [EV] [PV] [slides]
[4] Recurrent Neural Goodness-of-Fit Test for Time Series [arXiv] [poster] [code]
Aoran Zhang, Wenbin Zhou, Liyan Xie and Shixiang Zhu
International Conference on Artificial Intelligence and Statistics (AISTATS), 2025
[5] Distance-Preserving Spatial Representations in Genomic Data [arXiv] [slides] [talk]
Wenbin Zhou and Jin-Hong Du
Submitted, under review
[6] Counterfactual Generative Models for Time-varying Treatments
[arXiv] [poster] [code] [media]
Shenghao Wu, Wenbin Zhou, Minshuo Chen, and Shixiang Zhu
ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2024
🏆 Spotlight, NeurIPS 2023’s Deep Generative Models for Health Workshop.
Powered by Jekyll and Minimal Light theme.