Wenbin Zhou

Welcome!

I am a Ph.D. student in Machine Learning and Public Policy at Carnegie Mellon University, advised by Woody Zhu. I received my B.S. in Statistics at University of Science and Technology of China in 2023.

I am interested in developing tools for analyzing complex data and guiding practical decision-making. My research covers topics including generative AI, uncertainty quantification, spatio-temporal modeling, and causal inference. Recently, my work has focused on applications in sustainability and health data science.

Papers

[1] Hierarchical Spatio-Temporal Uncertainty Quantification for Distributed Energy Adoption [arXiv] [poster]
Wenbin Zhou, Shixiang Zhu, Feng Qiu and Xuan Wu

[2] Recurrent Neural Goodness-of-Fit Test for Time Series [arXiv] [poster]
Aoran Zhang, Wenbin Zhou, Liyan Xie and Shixiang Zhu

[3] Distance-Preserving Generative Modeling of Spatial Transcriptomics [arXiv] [slides] [talk]
Wenbin Zhou and Jin-Hong Du
Short version accepted by the 23rd International Workshop on Data Mining in Bioinformatics (BIOKDD), 2024

[4] Counterfactual Generative Models for Time-varying Treatments [arXiv] [poster] [talk]
Shenghao Wu, Wenbin Zhou, Minshuo Chen, and Shixiang Zhu
ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2024

News

• [Oct 2024] Gave a talk and presented poster at 2024 INFORMS Annual Meeting.
• [Aug 2024] Gave an online talk on our accepted paper at BIOKDD 2024.
• [May 2024] Interned at Argonne National Laboratory.
• [March 2024] Served as a session co-chair for flash talks & posters at YinzOR 2024.
• [October 2023] Our paper got accepted in the Deep Generative Models for Health Workshop (spotlight) and the Causal Representation Learning Workshop at NeurIPS 2023.
• [August 2023] Won first place for poster presentation in poster competition at YinzOR 2023.

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