数学大讲堂

Statistical Challenges, Opportunities, and Strategies in Large-Scale Medical Studies

About the speaker:

朱宏图教授为德克萨斯大学MD Anderson癌症中心生物统计系教授,美国统计学会图像统计方向八个创始人之一。朱宏图教授多年来一直致力于医学图像统计分析、结构方程模型、统计诊断、变量选择、删失数据分析、函数型数据分析、流形数据统计分析等几个方面的研究,在统计学和生物统计学等多个领域作出了杰出贡献。朱宏图教授现担任国际统计界顶级学术期刊Annals of Statistics和Journal of the American Statistical Association副主编,同时还担任Statistica Sinica和Statistics and its Inference副主编。先后被选为美国统计学会(American Statistical Association)终身会员,国际数理统计学会(Institute of Mathematical Statistics,IMS)终身会士。

Abstract:

With the rapid growth of modern technology, many biomedical studies have collected data across different sources (e.g., imaging, genetics, and clinical) in an unprecedented scale. The integration of such ultrahigh-dimensional data raises many statistical challenges, rendering most existing statistical methods and old data platform no longer suitable and thus underscoring the great need for methodological developments from a rigorous perspective. To address these challenges, I will highlight several key statistical opportunities and strategies  in big data integration and analysis through several interrelated projects.