Rate My Professor Hang Zhou

Zhou's research specializations include functional and longitudinal data analysis, statistical inference for object-valued data such as distributions, trees, and compositional data, learning theory including generalization bounds for deep neural networks, and model-agnostic methods with applications like anomaly detection. He has published extensively in leading statistical journals. Notable publications include 'Theory of functional principal component analysis for noisy and discretely observed data' (Annals of Statistics, 2025, with Dongyi Wei and Fang Yao), 'Conformal inference for random objects' (Annals of Statistics, 2025, with Hans-Georg Müller), 'Deep regression for repeated measurements' (Journal of the American Statistical Association, 2025, with Shunxing Yan and Fang Yao), 'Wasserstein-Fréchet Integration of Conditional Distributions' (Electronic Journal of Statistics, 2025, with Álvaro Gajardo and Hans-Georg Müller), 'Functional linear regression for discretely observed data: from ideal to reality' (Biometrika, 2023, with Fang Yao and Huiming Zhang), and 'Intrinsic Wasserstein Correlation Analysis' (Statistica Sinica, in press 2025+, with Zhenhua Lin and Fang Yao). Additional contributions appear in the Journal of Data-centric Machine Learning Research (2024) and IEEE Transactions on Pattern Analysis and Machine Intelligence (2024). Zhou has also presented at major conferences including ICML (2024), NeurIPS (2024), ACM KDD (2025), and ACM Multimedia (2025).