應yl7703永利官網邀請，美國Walter Reed 研究院高級研究員及喬治華盛頓大學兼職教授李元章教授將于近期訪問我校并作系列學術報告。

     報告一：Analysis of high dimensional data
     時   間：6月25日（星期一）上午9：30
     地   點：齊云樓911報告廳
     摘   要：Identification of disease and high-risk populations provides a useful resource for studying common diseases and their component traits. Analysis of biomarkers frequently involves regression of high dimensional data. This is problematic when the number of observations is limited. The dependency among various biomarkers cannot be avoided and the collinearity makes unbiased and stable conclusions difficult. We propose a three-step approach: 1. Decomposing the sample space; 2. Finding an orthogonal base including the most significant linear combination of biomarkers, which can be used to identify the cases most efficiently; and 3. General linear regression based on the vectors generated in step 2 to evaluate the multiple biomarker associations with the disease and identify the new cases. Numerical results demonstrate that the proposed Decomposition Gradient Regression (DGR) approach can accomplish significant dimension reduction with higher biomarker sensitivity for disease detection.

     報告二：Modeling for Pharmacodynamics and Bioassay Studies
     時  間：6月25日（星期一）下午3：00
     地  點：齊云樓911報告廳
     摘  要：Dose-response relationships are fundamental to the life sciences, particularly drug safety and toxicity. Increasingly, scientists are encountering dose-response relationships that are not well-characterized by classical models. Traditional dose-response models depend on monotonic data and often fail when applied to non-monotonic data. Assessment of dose response should be an integral part of establishing the safety and efficacy of any drug. The objective of this topic is to develop a novel approach applicable to general pharmacologic, toxicological, or other biomedical data, that exhibit a non-monotonic dose-response relationship for which traditional parametric models fail. Software will be developed to analyze dose-response relationships using both monotonic and non-monotonic data.

     報告三：The Present and Future of Statistics:  Challenges and Opportunities
     時   間：6月26日（星期二）下午4：30
     地   點：榆中校區(qū)天山堂C302
     摘   要：Statistics is one of the fastest-growing degrees in the US and many other developed countries, but the growth may not be enough to satisfy the high demand for statisticians in technology, consumer products, health care, government, manufacturing and other areas of the economy. This lecture discusses the history and nature function of statistics and introduce basic concepts in statistics and how to become a successful statistician.

     歡迎廣大師生光臨!

報告人簡介

     李元章教授于1990年在美國Nebraska – Lincoln 大學獲得統(tǒng)計學博士學位。現(xiàn)為美國Walter Reed 研究院的高級研究員、喬治華盛頓大學（George Washington University）兼職教授，主要從事生物統(tǒng)計、應用統(tǒng)計、高維數(shù)據(jù)統(tǒng)計分析等領域的理論和應用研究，已完成6部著作，發(fā)表論文70余篇，主持多個生物統(tǒng)計的研究項目。李教授多次應邀回國到數(shù)所大學及研究院、醫(yī)院講座及授課，講授過離散數(shù)據(jù)回歸分析，不完全數(shù)據(jù)分析，隨機數(shù)據(jù)模型，回歸模型與SAS,統(tǒng)計分析計算方法等課程。
                       
                   

應用數(shù)學與復雜系統(tǒng)省級重點實驗室

yl7703永利官網

蘭州大學萃英學院

大數(shù)據(jù)科學研究中心