【专家简介】：李挺，南方科技大学统计与数据科学系研究员，副教授。主持国家自然科学基金青年项目1项，获得国家级高层次人才项目（青年）本科毕业于浙江大学竺可桢学院数学英才班，博士毕业于香港科技大学数学系，于耶鲁大学生物统计系从事博士后研究。曾在香港理工大学担任助理教授。长期从事复杂数据的统计研究以及大模型与生成模型，主要包括网络结构数据研究，复杂表型的基因相关性分析以及人脑图像数据分析。已在Annals of Statistics、JASA、JMLR、Annals of Applied Statistics、Genome Research、Human Brain Mapping、ICML 等统计、生物统计核心期刊和机器学习顶会上发表论文20篇。
【报告摘要】：Data in various domains, such as neuroimaging and network data analysis, often come in complex forms without possessing a Hilbert structure. The complexity necessitates innovative approaches for effective analysis. We propose a novel measure of heterogeneity, ball impurity, which is designed to work with complex non-Euclidean objects. Our approach extends the notion of impurity to general metric spaces, providing a versatile tool for feature selection and tree models. The ball impurity measure exhibits desirable properties, such as the triangular inequality, and is computationally tractable, enhancing its practicality and usefulness. Extensive experiments on synthetic data and real data from the UK Biobank validate the efficacy of our approach in capturing data heterogeneity. Remarkably, our results compare favorably with state-of-the-art methods in metric spaces, highlighting the potential of ball impurity as a valuable tool for addressing complex data analysis tasks.
【报告时间】：2026年04月14日15:00-16:00
【报告地点】：位育楼417
