Qinqin Zhu

Assistant Professor, Chemical Engineering

Research interests: Process Data Analytics; Machine Learning; Statistical Process Monitoring and Fault Diagnosis; Optimization and Control of Process and Energy Systems; Process Systems Engineering

Biography

Professor Qinqin Zhu is an assistant professor in the Department of Chemical Engineering at the University of Waterloo. She is also a faculty member in the Waterloo Institute for Nanotechnology (WIN), Waterloo Artificial Intelligence Institute (Waterloo.AI) and Waterloo Institute for Sustainable Energy (WISE). Professor Zhu received her PhD degree from the Chemical Engineering department at the University of Southern California. Prior to UWaterloo, She worked as a senior research scientist at Facebook Inc. in the United States.

Education

Doctoral Degree, Chemical Engineering, University of Southern California, 2017
Master's Degree, Computer Science, University of Southern California, 2016
Bachelor's Degree, Automation, Zhejiang University, 2013

Qinqin Zhu

Research

Professor Zhu's research mainly focuses on developing advanced statistical machine learning methods, process data analytics techniques and optimization algorithms in the era of big data with applications to statistical process monitoring and fault diagnosis. Her research addresses theoretical challenges and problems of practical importance in the area of process systems engineering. By leveraging the power of mathematical modeling and optimization, her group strives to develop advanced multivariate statistical analysis algorithms that enhance decision making in complex engineering systems in different areas, including chemical engineering, biomedical engineering, environmental engineering and food engineering.

Publications

Xu, B.* and Zhu, Q., 2021. Comprehensive monitoring with concurrent dynamic auto-regressive partial least squares. Submitted to Journal of Process Control.
Alkabbani, H.*, Ahmadian, A., Zhu, Q. and Elkamel, A., 2021. Machine learning and metaheuristic methods for renewable power forecasting: A recent review. Frontiers in Chemical Engineering, 3, p.14.
Qin, S.J., Dong Y., Zhu, Q., Wang, J. and Liu, Q., 2020. Bridging systems theory and data science: A unifying review of dynamic latent variable analytics and process monitoring. Annual Reviews in Control.
Zhu, Q., 2020. Auto-regressive modeling with dynamic weighted canonical correlation analysis. Journal of Process Control, 95, pp.32-44.
Zhu, Q., Qin, S.J. and Dong, Y., 2020. Dynamic latent variable regression for inferential sensor modeling and monitoring. Computers & Chemical Engineering, p.106809.
Zhu, Q., Liu, Q. and Qin, S.J., 2017. Concurrent quality and process monitoring with canonical correlation analysis. Journal of Process Control, 60, pp.95-103.

Please see Qinqin Zhu's Google Scholar profile for a current list of her peer-reviewed articles.