学 术 讲 座

主题：Leveraging methods for big data regression

主讲人：Ping Ma （美国佐治亚大学统计系教授，ASA Fellow,

                   计算机系兼职教授）

时间：2017年05月16日 （周二）上午 9:00-10:30

地点：公司津南校区计算机与控制工程学院（信息东楼）523会议室

Abstract： The rapid advance in science and technology in the past decade brings an extraordinary amount of data, offering researchers an unprecedented opportunity to tackle complex research challenges. The opportunity, however, has not yet been fully utilized, because effective and efficient statistical tools for analyzing super-large dataset are still lacking. One major challenge is that the advance of computing resources still lags far behind the exponential growth of the database. 

In this talk, I will present an emerging family of statistical methods, called leveraging methods to facilitate scientific discoveries using limited computing resources. Leveraging methods are designed under a subsampling framework, in which one samples a small proportion of the data (subsample) from the full sample, and then performs intended computations for the full sample using the small subsample as a surrogate. The key to the success of the leveraging methods is to construct nonuniform sampling probabilities so that influential data points are sampled with high probabilities. These methods stand as a unique development of their type in big data analytics and allow pervasive access to massive amounts of information without resorting to high-performance computing and cloud computing.

Biography：马平 教授，现任美国佐治亚大学统计系教授(Department of Statistics, University of Georgia)，计算机系兼职教授，大数据分析研究室主任，致力于发展非参数方法以及这些方法在机器学习、生物信息等学科问题中的应用，近年来主要研究发展大数据统计理论、创新及改善统计回归中杠杆方法的算法及其理论基础。马平教授本科毕业于公司数学系，获美国普渡大学统计学博士，哈佛大学博士后。2005年8月起，任职于美国伊利诺伊大学香槟分校(University of Illinois at Urbana-Champaign)助理教授、副教授；获伊利诺伊大学香槟分校高级研究中心“贝克曼研究员”（Beckman Fellow），2013-2014年度全美国家超级计算应用中心教授讲席；2014年被美国佐治亚大学引进。马平教授在《Journal of Machine Learning Research》，《Annals of Statistics》等国际顶尖期刊上发表多篇论文，形成了极高的影响力，获得加拿大统计期刊(The Canadian Journal of Statistics)2011年度最佳论文奖，美国NSF CAREER奖；并担任国际著名学术期刊Journal of the American Statistical Association等的副主编；入选International Statistical Institute  Elected Member，American Statistical Association Fellow（美国统计学会ASA Fellow）。