Wentao Gao

Research Interests

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

Mr 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 several first-author work at IJCAI, ECAI, AAAI and WWW.
 
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
2026 Gao, W., Chen, X., Du, X., Yu, W., Bernal, A. M. C., & Xu, Z. (2026). Deep Extreme Transformer: Tackling Zero-Inflated Time Series for Precipitation Prediction. In S. Koenig, C. Jenkins, & M. E. Taylor (Eds.), Proceedings of the Aaai Conference on Artificial Intelligence Vol. 40 (pp. 38478-38486). SINGAPORE: ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE.
DOI
2026 Gao, W., Du, X., Chen, X., Guo, Y., Cifuentes-Bernal, A. M., Luo, R., & Xu, Z. (2026). Energy-Efficient Training-Free Zero-Inflation Correction for Rainfall Forecasting with Time-Series Foundation Models. In Www 2026 Proceedings of the ACM Web Conference 2026 (pp. 8840-8850). ACM.
DOI
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 Scopus2 WoS2
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 (ICML 2025), as puiblished in Proceedings of Machine Learning Research Vol. 267 (pp. 14562-14578). US: Proceedings of Machine Learning Research (PMLR).
Scopus1
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 I. Lynce, N. Murano, M. Vallati, S. Villata, F. Chesani, M. Milano, . . . M. Dastani (Eds.), In published processings need to add Proceedings of the 28th European Conference on Artificial Intelligence, including 14th Conference on Prestigious Applications of Intelligent Systems, as published in Frontiers in Artificial Intelligence and Applications Vol. 413 (pp. 1107-1114). Netherlands: 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 I. Lynce, N. Murano, M. Vallati, S. Villata, F. Chesani, M. Milano, . . . M. Dastani (Eds.), Frontiers in Artificial Intelligence and Applications Vol. 413 (pp. 3226-3233). ITALY, University of Bologna, Bologna: IOS PRESS.
DOI Scopus1 WoS1
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 Scopus19 WoS15

Connect With Me

Other Links