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.