报告人简介：王启华，中国科学院数学与系统科学研究院研究员，博士生导师，国家杰出青年基金获得者，教育部长江学者奖励计划特聘教授，中科院“百人计划”入选者。曾在北京大学、香港大学任教，先后访问加拿大、美国、德国及澳大利亚10多所世界一流大学。主要从事复杂数据经验似然统计推断、缺失数据分析、高维数据统计分析、大规模数据分析等方面的研究，出版专著三部，在The Annals of Statistics, JASA及Biometrika等国际重要刊物发表论文140余篇,部分工作已产生持久的学术影响。曾主持国家杰出青年基金项目、重点项目、多项面上项目，作为核心骨干成员先后参加了两项国家自然科学基金创新群体项目及一项国家重点研发计划项目。是高维统计分会理事长，中国现场统计研究会常务理事，中国概率统计学会常务理事，一些国际国内刊物及一些丛书的编委。
报告简介：Information from multiple data sources is increasingly available. However, some data sources may produce biased estimates due to biased sampling, data corruption, or model misspecification. This calls for robust data combination methods with biased sources. In this paper, a robust data fusion-extraction method is proposed. In contrast to existing methods, the proposed method can be applied to the important case where researchers have no knowledge of which data sources are unbiased. The proposed estimator is easy to compute and only employs summary statistics, and hence can be applied to many different fields, e.g., meta-analysis, Mendelian randomization, and distributed systems. The proposed estimator is consistent even if many data sources are biased and is asymptotically equivalent to the oracle estimator that only uses unbiased data. Asymptotic normality of the proposed estimator is also established. In contrast to the existing meta-analysis methods, the theoretical properties are guaranteed even if the number of data sources and the dimension of the parameter diverges as the sample size increases. Furthermore, the proposed method provides a consistent selection for unbiased data sources with probability approaching one. Simulation studies demonstrate the efficiency and robustness of the proposed method empirically. The proposed method is applied to a meta-analysis data set to evaluate the surgical treatment for moderate periodontal disease and to a Mendelian randomization data set to study the risk factors of head and neck cancer.