报告人：姚方 教授（北京大学）

时间：2026年05月15日 14:30-

地址：数统学院LD402

摘要：This talk focuses on the integration of statistics, deep learning, and differential equations. In many scientific applications, data generation and evolution are governed by physical mechanisms that are often described by differential equations, while real-world complexity introduces additional effects that are better captured by data-driven tools such as deep learning. We study statistical modeling, estimation, and inference for such data generating mechanisms. We first develop a framework for semiparametric M-estimation with overparameterized deep neural networks, establishing optimal convergence rates and parametric efficiency. We then introduce semiparametric differential equation models that combine interpretable mechanism-based components with flexible perturbation terms, together with a profiling-based estimation and inference procedure. Finally, we apply the proposed methodology to radiative transfer and atmospheric profile retrieval using FY-4B/GIIRS hyperspectral observations. The resulting hybrid model improves forward radiance reconstruction and enhances temperature profile retrieval accuracy, demonstrating the practical value of our framework.

邀请人：统计与数据科学团队