时 间：2021年11月19日 10:00-11:00
地 点：腾讯会议：413 952 541
题 目：Advanced Data Modeling and Diagnosis For Intelligent Systems
主讲人：Andi Wang（助理教授）
主持人：项冬冬（副教授）
主 办：经济与管理学部统计学院
摘 要：
Intelligent manufacturing systems and the internet of things (IoT) generate big data, which enables unprecedented opportunities for process monitoring, root-cause diagnostics, and knowledge discovery. However, these data are usually complex in structure, large in volume, and multi-modal. These characteristics pose significant challenges in real-time data analytics and intelligent decision-making. My research aims at developing effective models and efficient computational algorithms based on engineering knowledge and machine learning to achieve predictive modeling, detection, and diagnosis and ultimately advance system intelligence.
In this talk, I will present multiple research efforts focused on new models and computational algorithms developed for intelligent and interconnected manufacturing systems: they are related to multi-stage process modeling, tensor decomposition and regression, retrospective analysis of event data, feature ranking, and so on.
报告人简介：
Dr. Andi Wang is an assistant professor in the School of Manufacturing Systems and Networks in Ira A. Fulton Schools of Engineering, Arizona State University. He is also a member of the graduate faculty of Industrial Engineering program.He received his B.S. in Statistics from Peking University and a Ph.D. in Hong Kong University of Science and Technology. He also received the M.S. in Computer Science and Engineering and another Ph.D. in Industrial Engineering (System Informatics and Control) from Georgia Institute of Technology.
Andi Wang’s research focuses on the intersection of data science and manufacturing systems. He applies machine learning, high-dimensional statistics, and advanced optimization techniques to the data generated from complex, interconnected, and intelligent systems to improve root-cause diagnostics, system monitoring, prediction, prognostics, and control. He is a recipient of Wayne Kay Scholarship from SME, a recipient of INFORMS 2019 Data Mining Best Paper Finalist Award, INFORMS 2020 Quality Reliability and Statistics Best Paper Finalist Award, IISE QCRE 2021 Best Paper Finalist Award.