7月6日:Hulin Wu | Big Data Challenges and Opportunities for Statisticians: Experience from Two Biomedical Big Data Research Projects

时间:2018-06-28   浏览:56

时间:201876日上午9:30-10:30

地点:法商南楼135(闵行校区)

报告人:Hulin Wu,

报告题目:Big Data Challenges and Opportunities for Statisticians: Experience from Two Biomedical Big Data Research Projects

Abstract:The newly emerging Big Data and Data Science have made a very big splash among academic world, industries and governments. In particular, it has a big impact to the statistical community, since statisticians are traditionally considered as a unique profession to deal with, especially analyze data. There is a potential threat that the emerging professionals of Data Scientists may replace statisticians’ job in the near future. In this talk, I will use two Big Data research projects as examples to share our experience in dealing with Big Data from biomedical research. One example is based on a public genetic database, GEO and another is based on the electronic health record (EHR) Big Data. Through these examples, I’ll discuss the new challenges and new opportunities for statisticians in this new Big Data era. Traditional statistical thinking and culture may need to be changed in order to meet the needs of Big Data analytics.   

报告人简介:

武虎林教授,于1994弗罗里达州立大学博士毕业。目前为the University of Texas Health Science Center at Houston生物统计与数据科学系系主任和The Betty Wheless Trotter教授。武教授是美国统计学会会士,美国国家卫生研究院(NIH)基金项目评审委员会常任委员。武教授的研究兴趣包括生物医学和健康科学大数据分析,复杂的高维数据分析、微分方程模型的统计方法和理论,计算系统生物学以及生物信息学在免疫学和传染病上的应用。武教授已在生物统计、生物信息、计算生物学、免疫学及传染病预测等研究领域发表了100多篇研究论文和两本专著,是微分方程和动力学模型的统计方法领域先驱者和开创者之一。在过去十几年来候选人从美国国家卫生研究院(NIH)作为项目负责人获得的研究开发基金总计超过3千万美元(约人民币2亿元),是美国生物统计学领域中获得美国国家资助大数据相关科研经费支持最多的教授之一。武教授的团队已研究开发了多个大数据分析预测软件和数据库,并有效地应用于生物信息和医学研究当中。特别是研究出的处理各种复杂数据的模型和算法,包括首创的基于海量数据高维微分方程基因网络模型,改进的分布式基因演变非线性优化算法,贝叶斯高维状态空间预测模型和算法等等,在国际上都处于领先水平。近期研发的基于大数据的高维动态网络预测模型,可用于复杂管理系统优化,动态系统预测及多维信息资源整合。