Prof. Li Zeng received her B.E. in Precision Instruments and M.S. in Optical Engineering from Tsinghua University, and M.S. in Statistics and Ph.D. in Industrial Engineering from University of Wisconsin-Madison. Before joining CityU, she was an Associate Professor in the Wm Michael Barnes '64 Department of Industrial and Systems Engineering at Texas A&M University.
Prof. Zeng's research interests are statistical machine learning and quality engineering, with applications in manufacturing and biomedical engineering. Her research integrates data science and domain science for better modelling and prediction, with the goal of knowledge discovery and quality improvement.
Awards and Achievements
- 2017 “Best Paper Award” IISE Transactions.
- 2016 “Best Application Award” IISE Transactions.
- 2014 “Best Paper in the Healthcare Systems Track” 4th IEOM Conference.
Previous Experience
- Sep 2019 - Jul 2021, Associate Professor, Texas A&M University.
- Sep 2015 - Aug 2019, Assistant Professor, Texas A&M University.
- Sep 2010 - Jul 2015, Assistant Professor, University of Texas at Arlington.
Selected Publications
- Wu, Q., Deng, X., Wang, S., and Zeng, L., 2021, “Constrained Varying-coefficient Model for Time-Course Experiments in Soft Tissue Fabrication”, Technometrics, 63(2):249-262.
- Choi, D., and Zeng, L., 2020, “Robust Logistic Regression Tree for Subgroup Identification in Healthcare Outcome Modeling”, IISE Transactions on Healthcare Systems Engineering, 10(3):184-199.
- Wu, Q., Wang, X., Liu, H., and Zeng, L., 2020, “Learning Hemodynamic Effect of Transcranial Infrared Laser Stimulation Using Longitudinal Data Analysis”, IEEE Journal of Biomedical and Health Informatics, 24(6):1772-1779.
- Zeng, L., Deng, X., and Yang, J., 2018, “Constrained Gaussian Process with Application in Tissue-engineering Scaffold Biodegradation”, IISE Transactions, 50(5):1-17.
- Zeng, L., Deng, X., and Yang, J., 2016, “Constrained Hierarchical Modeling of Degradation Data in Tissue-engineered Scaffold Fabrication”, IIE Transactions, 48(1):16-33.
Openings
- I am looking for highly motivated PhD students with a background of engineering and statistics/data analysis. Interested students please send your CV, transcripts and publications (if any) to me for consideration.
Last update date :
05 Jul 2023
