深大管院20周年院庆系列学术讲座(第52期)暨深圳大学管理学院工商管理系启真论坛(第30期)

来源: 发布时间:2017-11-30 16:17:29 浏览量:
    “深大管院工商管理系启真论坛”是管理学院工商管理系创办的学术论坛系列,论坛本着“前沿、学术、求知、探真”的宗旨,邀请来自工商管理各领域学有专长的学者就重大理论与实践问题进行报告、对话和沟通,以期能拓展思路、开阔视野、深化思考。
 
报告主题:Data Science: Statistically and Numerically Efficient Independence Test
报告人:Xiaoming Huo(Georgia Institute of Technology)
 
报告人简介:Xiaoming Huo is a professor at the Stewart School of Industrial & Systems Engineering at Georgia Tech. Dr. Huo's research interests include statistical theory, statistical computing, and issues related to data analytics. He has made numerous contributions on topics such as sparse representation, wavelets, and statistical problems in detectability. His papers appeared in top journals, and some of them are highly cited. He is a senior member of IEEE since May 2004. He won the Georgia Tech Sigma Xi Young Faculty Award in 2005. His work has led to an interview by Emerging Research Fronts in June 2006 in the field of Mathematics - every two months, one paper is selected. Huo is a fellow of ASA and an AE for Technometrics. 
 
报告摘要:The big data is a well-known phenomenon in the modern world. The emerging discipline of data science has inspired a lot of discussion and debate in the scientific research communities, including the mathematical and statistical science community. Contributing to this discussion, in the first part of this talk, I will present a discussion as well as a selective survey on the landscape of data science, as it is forming its foundation. I will describe some of my recent activities towards building a foundation of data science. On the second part of this talk, I will present one of my specific research, which addresses a particular issue in the enormous spectrum of data science. More specifically, we study how to generate a statistical inference procedure that is both computational efficient and having theoretical guarantee on its statistical performance. We present numerical comparisons with contemporary approaches to demonstrate its advantages. 
 
 
报告时间:2017年12月08日(周五)10:00 am
报告地点:MBA教室H3-400
 
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