应兰州大学数学与统计学院邀请，美国Walter Reed 研究院高级研究员及乔治华盛顿大学兼职教授李元章教授将于近期访问我校并作系列学术报告。
报告一：Analysis of high dimensional data
时 间：6月25日（星期一）上午9：30
地 点：齐云楼911报告厅
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.
报告二：Modeling for Pharmacodynamics and Bioassay Studies
时 间：6月25日（星期一）下午3：00
地 点：齐云楼911报告厅
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.
报告三：The Present and Future of Statistics: Challenges and Opportunities
时 间：6月26日（星期二）下午4：30
地 点：榆中校区天山堂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.
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报告人简介：
李元章教授于1990年在美国Nebraska－Lincoln 大学获得统计学博士学位。现为美国Walter Reed 研究院的高级研究员、乔治华盛顿大学（George Washington University）兼职教授，主要从事生物统计、应用统计、高维数据统计分析等领域的理论和应用研究，已完成6部著作，发表论文70余篇，主持多个生物统计的研究项目。李教授多次应邀回国到数所大学及研究院、医院讲座及授课，讲授过离散数据回归分析，不完全数据分析，随机数据模型，回归模型与SAS,统计分析计算方法等课程。
应用数学与复杂系统省级重点实验室
兰州大学数学与统计学院
兰州大学萃英学院
大数据科学研究中心
二〇一八年六月二十日