刘军，美国哈佛大学统计系终身教授，兼任哈佛生物统计系教授，斯坦福大学统计系终身教授，北京大学数学学院长江讲座教授，清华大学数学系访问教授。美国数理统计学会和美国统计学会会士、国际计算生物学会会士、美国国家科学院院士。创建清华大学统计学研究中心并任名誉主任至2024年,以筹建发展委员会主任身份在清华大学创建统计与数据科学系。主要从事于贝叶斯统计理论、蒙特卡洛方法、统计机器学习、状态空间模型和时间序列、生物信息学、计算生物学等方向的研究。曾获考普斯会长奖、晨兴应用数学金奖、泛华统计协会许宝騄奖。在各类国际顶尖学术杂志（Science，Nature，Cell，JASA，JMLR等）发表论文300余篇和专著，被引用9万余次。指导40多位博士生、30多位博士后。

报告摘要：Mirror statistic (or knockoff statistic) is a key component for most p-value-free feature selection methods. However, it is unclear how to choose the best statistic when additional prior information or covariate information is available. In this paper, we first describe a large class of possible choices of mirror statistics and derive an optimal form of mirror statistic inspired by the two-stage formula proposed in Li and Fithian (2021). Theoretically, we demonstrate the power advantage of this optimal form by considering the Rare/Weak signal model. With prior information, evenly splitting the data into two halves is no longer the most efficient way.  Building upon the optimal form of the mirror statistic, we investigate how the splitting ratio affects the power of a feature selection procedure and introduce the Adaptive-Data-Splitting (ADS) approach. Both simulations and real data examples show that ADS performs significantly better than the original equal-splitting.