2020-01-09 | Di Wang: Data Mining and Artificial Intelligence of Complex Engineering and Service Systems



Due to the rapid development of sensing technologies, sensor data have been widely collected to reflect the status of engineering and service systems. Data mining and artificial intelligence (DM&AI) technologies create an unprecedented opportunity to better understand the systems by analyzing the sensor data. This talk will introduce two DM&AI technologies of complex engineering and service systems. One is a spatiotemporal prediction approach for a 3D thermal field, and the other is a generic indirect deep learning approach for multisensor degradation modeling.






Di Wang is a Ph.D. candidate in the Department of Industrial Engineering and Management at Peking University, and also a joint Ph.D. student at University of Wisconsin-Madison. Her research interests focused on data mining and artificial intelligence of complex engineering and service systems. Her research excellence has been evidenced by the Best Paper Award in the Data Mining Section of INFORMS Annual Meeting, the Best Application Paper Award of IISE Transactions, the Best Paper Award of SII Conference, the Best Student Paper Finalist Award of IISE Annual Conference, and the feature article of ISE Magazine.