TZstats Lab - Selected Recent Publications

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Tian Zheng, Subashree Venkatasubramanian, Shawn Li, Amy Braverman, Xinyi Ke, Zhewen Hou, Peter Jin, Sammy Agrawal (2026). Machine Learning Workflows in Climate Modeling: Design Patterns and Insights from Case Studies. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

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Jerry Lin, Zeyuan Hu, Tom Beucler, Katherine Frields, Hannah Christensen, Walter Hannah, Helge Heuer, Peter Ukkonen, Laura a Mansfield, Tian Zheng (2026). Crowdsourcing the frontier: Advancing hybrid physics-ML climate simulation via a $50,000 Kaggle competition. Journal of Advances in Modeling Earth Systems.

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Candace M. Agonafir, Tian Zheng (2026). Structured exploration of machine learning model complexity for spatio-temporal forecasting of urban flooding. Urban Water Journal.

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Zhewen Hou, Jiajin Sun, Subashree Venkatasubramanian, Peter Jin, Shuolin Li, Tian Zheng (2025). Calibrating Geophysical Predictions under Constrained Probabilistic Distributions. arXiv preprint arXiv:2512.03081.

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Nabila El-Bassel, James David, Others, Eric Aragundi, More Others, Tian Zheng (2025). The Practical, Robust Implementation and Sustainability (PRISM)-capabilities model for use of Artificial Intelligence in community-engaged implementation science research. Implementation Science.

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Ahmed Jaber, Wangshu Zhu, Karthick Jayavelu, Justin Downes, Sameer Mohamed, Candace Agonafir, Linnia Hawkins, Tian Zheng (2025). AutoClimDS: Climate Data Science Agentic AI -- A Knowledge Graph is All You Need. arXiv.

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Tian Zheng (2025). AI Education in Higher Education: A Taxonomy for Curriculum Reform and the Mission of Knowledge. arXiv.

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Shuolin Li, Tian Zheng, Alban Farchi, Marc Bocquet, Pierre Gentine (2025). Probabilistic data assimilation for ensemble distribution projections with generative machine learning: A Lorenz '96 proof-of-concept. Geophysical Research Letters.

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Owen Ward, Anna Smith, Tian Zheng (2025). Bayesian Modeling for Aggregated Relational Data: A Unified Perspective. arXiv.

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Siyeon Kim, Juan Nathaniel, Zhewen Hou, Tian Zheng, Pierre Gentine (2024). Spatiotemporal upscaling of sparse air-sea pCO2 data via physics-informed transfer learning. Scientific Data.

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Chenyang Zhong, Shouxuan Ji, Tian Zheng (2024). Graph-Enabled Fast MCMC Sampling with an Unknown High-Dimensional Prior Distribution. arXiv preprint arXiv:2408.02122.

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Timothy Jones, Owen Ward, Yiran Jiang, John Paisley, Tian Zheng (2024). Scalable Community Detection in Massive Networks Using Aggregated Relational Data. Statistical Sinica.

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Chengliang Tang, Nathan Lenssen, Ying Wei, Tian Zheng (2024). Wasserstein Distributional Learning via Majorization-Minimization. AISTATS 2023.

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Sungduk Yu, Others, Tian Zheng, More Others, Mike Pritchard (2024). ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators. NeurIPS 2023.

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Tamara Broderick, Andrew Gelman, Rachael Meager, Anna Smith, Tian Zheng (2023). Toward a Taxonomy of Trust for Probabilistic Machine Learning. Science Advances.

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Anna Smith, Tian Zheng, Andrew Gelman (2023). Prediction scoring of data-driven discoveries for reproducible research. Statistics and Computing.

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Owen Ward, Jing Wu, Tian Zheng, Anna Smith, James P. Curley (2022). Network Hawkes Process Models for Exploring Latent Hierarchy in Social Animal Interactions. Journal of the Royal Statistical Society Series C: Applied Statistics.

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Owen Ward, Zhen Huang, Andrew Davison, Tian Zheng (2021). Next Waves in Veridical Network Embedding. Statistical Analysis and Data Mining: The ASA Data Science Journal.

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Jing Wu, Anna Smith, Tian Zheng (2020). Diagnostics and Visualization of Point Process Models for Event Times on a Social Network. Applied Modeling Techniques and Data Analysis 1: Computational Data Analysis Methods and Tools, Volume 7.

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Yuting Ma, Tian Zheng (2017). Stabilized Sparse Online Learning for Sparse Data. Journal of Machine Learning Research.

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Lu Meng, Tian Zheng (2016). Phase-Aligned Spectral Filtering for Decomposing Spatiotemporal Dynamics. arXiv.

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Herman Chernoff, Shaw-Hwa Lo, Tian Zheng (2009). Discovering influential variables: A method of partitions. The Annals of Applied Statistics.

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