报告题目：Manifold learning for noisy and high-dimensional biomedical datasets: challenges and some solutions

报告人：丁秀才 教授 加州大学戴维斯分校

报告时间：2025年12月11日 (星期四)  14:00-15:00

报告地点：正新楼105

报告摘要：

Manifold learning theory has garnered considerable attention in the modeling of expansive biomedical datasets, showcasing its ability to capture data essence more effectively than traditional linear methodologies. Nevertheless, prevalent algorithms are primarily designed for low-dimensional and clean datasets, whereas contemporary biomedical datasets tend to be high-dimensional and noisy. This presentation addresses the adaptation of these algorithms to effectively accommodate the challenges posed by high dimensionality and noise in modern datasets.

报告人简介：

Xiucai Ding is currently an associate professor of statistics at UC Davis. Previously, he was a postdoc in Duke. He obtained his PhD from the University of Toronto. His main research interest includes applied probability methods (random matrix theory, random graph theory and Riemann-Hilbert approach) to high dimensional statistics, manifold learning and deep learning theory, as well as nonstationary time series analysis.