Dr. Gan Yuan

Department of Biostatistics

City University of Hong Kong

Assistant Professor

Email: [email protected]

Qualifications

2024 PhD., Statistics, Columbia University, USA

2019 M.Phil, Risk Management Science, The Chinese University of Hong Kong, China

2017 B.S, Quantitative Finance and Risk Management, The Chinese University of Hong Kong, China

Research Interests

Theoretical Machine Learning

Representation Learning

Active Learning

Time Series Analysis

Publications (Selected）

1. Boeschoten, L. E., Ankori-Karlinsky, R., Arellano, G., Brown, A. J., Fang, D., Leitold, V., Morton, D., Yuan, G., Zheng, T., Zimmerman, J. K., Uriarte, M.. (2026). Tree Growth After a Major Hurricane Reflects Predisturbance Vigor Rather Than Canopy Damage. Proceedings of the National Academy of Sciences. 123(16): e2532451123.
2. Ankori-Karlinsky R., Jackson T., Yuan G., Zimmerman J., Morton D., Zheng T., Uriarte M. (2025), Chronic Wind Alters Tropical Tree Architecture, Reducing Risk of Wind Damage. New Phytologist. 247(4):1643-1654.
3. Yuan G., Xu M., Kpotufe S., Hsu D.. (2025). Efficient Estimation of the Central Mean Subspace via Smoothed Gradient. SIAM Journal on Mathematics of Data Science. 7(3): 1241-1264.
4. Yuan G., Zhao Y., Kpotufe S.. (2024). Regimes of No Gain in Multi-class Active Learning. Journal of Machine Learning Research, 25(129):1–31.
5. Kpotufe S., Yuan G., Zhao Y.. (2022). Nuances in Margin Conditions Determine Gains in Active Learning. AISTATS, Proceedings of Machine Learning Research. 151:8112-8126.
6. Tang C., Yuan G., Zheng T. (2021). Weakly Supervised Learning Creates a Fusion of Modeling Cultures. Observational Studies. 7(1):203-211.

Profile Details

https://scholar.google.com/citations?hl=zh-CN&user=6vh7yBUAAAAJ

https://orcid.org/0000-0002-4200-3811