报告时间：2018年8月24日上午10:00-11:00
报告地点：X7503
主持人：周正春教授
Title: Detecting Changes across Many Data Streams
Abstract:The problem of sequentially detecting changes in parallel data streams is formulated and investigated. Each data stream may have its own change point at which the underlying probability distribution of its data changes, and the decision maker needs to declare, sequentially, which data streams have passed their change points. With a large number of parallel data streams, the error metric is the false discovery rate (FDR), which is the expected ratio of the number of falsely declared data streams to the total number of declared data streams. A data stream is falsely declared if the detected change point is ahead of its actual change point. Decision procedures which are guaranteed to control the FDR level are developed, and it is also shown that the average decision delays (ADD) of these decision procedures do not grow with the number of data streams. Numerical simulations and case studies are conducted to corroborate the analytical results, and to illustrate the utility of the decision procedures.
报告人简介:张文逸自2010年至今担任中国科学技术大学教授。主要研究方向包括无线通信与网络、信息论与统计推断。他于2001年在清华大学自动化系取得学士学位，2006年在美国University of Notre Dame取得博士学位，并曾在美国University of Southern California和Qualcomm Research Center任职。入选中国科学院“百人计划”、国家自然科学基金优秀青年基金、教育部“长江学者”资助计划青年学者。
