报告人简介：

黄彪，IEEE Fellow、加拿大工程院院士、加拿大化学化工学会会士及中国自动化学会会士。1997年获得加拿大阿尔伯塔大学过程控制博士学位，1986年获得北京航空航天大学自动控制硕士学位，1983获得北京航空航天大学自动控制学士学位。于1997年加入阿尔伯塔大学化工与材料工程系，目前是阿尔伯塔大学正教授。现任 IFAC Journal Control Engineering Practice 主编、Journal of the Franklin Institute 主题编辑、Journal of Process Control 副主编。

报告摘要：

Modern industries are awash with a large amount of data. Extraction of information and knowledge discovery from data for process design, control and optimization, from day-by-day routine process operating data, is especially interesting but also challenging. Process data analytics is an emerging area of great interest among data scientists and practicing engineers to extract meaningful features that represent data and their underlying processes. Neural network learning-based approaches typically extract powerful features but without clear physical meanings, while most statistical feature extractors have physical interpretations. This presentation will give a historical overview of process data analytics and machine learning and illustrative examples of popular statistical and machine learning feature extraction methods.