Ph.D. in Statistics, Stony Brook University (SUNY), USA (2021 – 2026)

M.S. in Statistics, Stony Brook University (SUNY), USA (2019 – 2021)

B.S. in Statistics, Shandong University, China (2015 – 2019)

Wenzhou-Kean University, China (2026 – Present):

Lecturer in Mathematical Sciences, College of Science, Mathematics and Technology

Dr. Jiayou Chao is a Lecturer in Mathematical Sciences in the College of Science, Mathematics and Technology at Wenzhou-Kean University. He received his Ph.D. and M.S. in Statistics from Stony Brook University (SUNY) and his B.S. in Statistics from Shandong University. Prior to joining WKU, he served as an Industrial Research Fellow and Data Scientist at Funding Metrics LLC in the United States, working on large-scale predictive modeling, machine learning pipelines, and financial data analytics. His research focuses on statistical machine learning, deep learning for time-series forecasting, generative AI, and

environmental/climate data modeling. His work has appeared in multiple peer-reviewed journals and conference proceedings across applied statistics and data-driven science. At Wenzhou-Kean University, Dr. Chao teaches courses in mathematics and statistics.

Time Series Analysis & Deep Learning: Spatiotemporal predictive modeling, non-linear dynamic forecasting, and neural network architectures for complex time-series data. Environmental & Climate Data Science: Statistical climate modeling, data-driven pathway analysis, regional sea-level rise projection, and extreme weather forecasting. Generative AI: Generative modeling and data augmentation techniques for complex, high-dimensional scientific and industrial datasets. Applied Machine Learning & Risk Analytics: Predictive credit risk modeling, survival analysis, and large-scale distributed data analytics.

Calculus I

Calculus for Business and Economics

Time Series Analysis & Forecasting

1. Chao, J., Tong, G., Liu, Z., Lin, W., Zhang, M., Ma, M., & Zhu, W. (2026). Mamba-Stormer v1.0: A Bidirectional Vision Mamba Backbone for Accurate and Scalable Global Weather Forecasting. EGUsphere / Geoscientific Model Development. https://doi.org/10.5194/egusphere-2026-3398

2. Chao, J., Tong, G., Zhong, Z., Lin, W., Zhang, M., & Zhu, W. (2026). Enhancing Regional Sea Level Predictions: A Unified Structural Equation Modeling Approach. Ocean-Land-Atmosphere Research.

3. Tong, G., Chao, J., Ma, W., Zhong, Z., Gupta, G., & Zhu, W. (2025). Leveraging Synthetic Data to Improve Regional Sea Level Predictions. Scientific Reports, 15(1), 3546. https://doi.org/10.1038/s41598-025-87770-0

4. Song, J., Tong, G., Chao, J., Chung, J., Zhang, M., Lin, W., Zhang, T., Bentler, P. M., & Zhu, W. (2023). Data-driven Pathway Analysis and Forecast of Global Warming and Sea Level Rise. Scientific Reports, 13(1), 5536. https://doi.org/10.1038/s41598-023-32432-8

5. Tong, G., Ma, W., Chao, J., Zhong, Z., Zhang, M., Lin, W., & Zhu, W. (2025). Enhancing Coastal Sea Level Predictions: A Hybrid Approach Combining TimeGAN-Augmented Data, and CNN-GRU Models. ACM SIGMETRICS Performance Evaluation Review, 53(2), 74–78. https://doi.org/10.1145/3708407.3708422

6. Chung, J., Tong, G., Chao, J., & Zhu, W. (2021). Path Analysis of Sea Level Rise and Its Impact. Stats, 5(1), 12–25. https://doi.org/10.3390/stats5010002