Wentao Gao

Research Interests

Time-Series Analysis Climate change science Climatological hazards (e.g. extreme temperatures, drought and wildfires)

Wentao Gao

Higher Degree by Research Candidate

School of Computer Science and Information Technology

College of Engineering and Information Technology

I am a PhD researcher in Data Science working on machine learning and causal methods for time series modelling. My research interests include representation learning, deconfounding, and generative models, with applications to environmental and climate data. I have published first-author work at IJCAI and AJCAI, and contributed to research presented at AAAI and ICML.
 
I also have strong interests in teaching and supervision, particularly in data analytics, machine learning, and experimental design, and I enjoy integrating research ideas into student learning and projects.

Year Citation
2025 Chen, X., Li, J., Liu, J., Liu, L., Peters, S., Le, T. D., . . . Walsh, A. (2025). Diffusion Models for Attribution. In T. Walsh, J. Shah, & Z. Kolter (Eds.), Proceedings of the Aaai Conference on Artificial Intelligence Vol. 39 (pp. 2266-2274). US: ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE.
DOI
2025 Gao, W., Li, J., Cheng, D., Liu, L., Liu, J., Le, T., . . . Zhao, Y. (2025). Deconfounding Multi-Cause Latent Confounders: A Factor-Model Approach to Climate Model Bias Correction. In Ijcai International Joint Conference on Artificial Intelligence (pp. 9638-9646). Canada: International Joint Conferences on Artificial Intelligence Organization.
DOI
2025 Du, X., Li, J., Cheng, D., Liu, L., Gao, W., Chen, X., & Xu, Z. (2025). Telling Peer Direct Effects from Indirect Effects in Observational Network Data. In Proceedings of the 42nd International Conference on Machine Learning, PMLR 267, 2025. (pp. 1-17). US: ICML.
2025 Du, X., Li, J., Cheng, D., Liu, L., Gao, W., Chen, X., & Xu, Z. (2025). Telling Peer Direct Effects from Indirect Effects in Observational Network Data. In Proceedings of Machine Learning Research Vol. 267 (pp. 14562-14578).
2025 Gao, W., Li, J., Liu, L., Le, T. D., Chen, X., Du, X., . . . Chen, Y. (2025). From Noise to Precision: A Diffusion-Driven Approach to Zero-Inflated Precipitation Prediction. In Frontiers in Artificial Intelligence and Applications Vol. 413 (pp. 1107-1114). IOS Press.
DOI
2025 Yu, W., Li, W., Gao, W., Wu, W., Du, S., & Li, J. (2025). PCFNet: Enhancing Time Series Forecasting Through Preserving Constant Frequency. In Frontiers in Artificial Intelligence and Applications Vol. 413 (pp. 3226-3233). IOS Press.
DOI
2024 Cheng, D., Xu, Z., Li, J., Liu, L., Liu, J., Gao, W., & Le, T. D. (2024). Instrumental Variable Estimation for Causal Inference in Longitudinal Data with Time-Dependent Latent Confounders. In M. Wooldridge, J. Dy, & S. Natarajan (Eds.), Proceedings of the Aaai Conference on Artificial Intelligence Vol. 38 (pp. 11480-11488). US: ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE.
DOI Scopus9 WoS6

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