Yining Dong received her Ph.D. degree in Electrical Engineering from University of Southern California in 2016, and her B.Eng. degree in Electronic Engineering from Tsinghua University in 2011. Before joining CityU, she was a post-doc in Electrical Engineering at Stanford University.
Her research interests are process data analytics, high-dimensional time series modeling, statistical machine learning, smart manufacturing and new material design.
Publications
- Dong, Y., & Qin, S. J. (2018). A novel dynamic PCA algorithm for dynamic data modeling and process monitoring. Journal of Process Control, 67, 1-11.
- Dong, Y., & Qin, S. J. (2018). Regression on dynamic PLS structures for supervised learning of dynamic data. Journal of Process Control, 68, 64-72.
- Dong, Y., & Qin, S. J. (2018). Dynamic latent variable analytics for process operations and control. Computers & Chemical Engineering, 114, 69-80.
- Dong, Y., Liu, Y., & Qin, S. J. (2019). Efficient Dynamic Latent Variable Analysis for High-Dimensional Time Series Data. IEEE Transactions on Industrial Informatics, 16(6), 4068-4076.
- Dong, Y., & Qin, S. J. (2020). New dynamic predictive monitoring schemes based on dynamic latent variable models. Industrial & Engineering Chemistry Research, 59(6), 2353-2365.
- Zhu, Q., Qin, S. J., & Dong, Y*. (2020). Dynamic latent variable regression for inferential sensor modeling and monitoring. Computers & Chemical Engineering, 137, 106809.
- Qin, S. J., Dong, Y., Zhu, Q., Wang, J., & Liu, Q. (2020). Bridging systems theory and data science: A unifying review of dynamic latent variable analytics and process monitoring. Annual Reviews in Control.
Recruitment
- I am looking for highly motivated PhD students. Please send me your CV and transcripts if you are interested.
Last update date :
15 Sep 2023
