I am a data scientist with a PhD in industrial engineering. I am strongly motivated of applying state-of-the-art optimization and machine learning algorithms to quantifying uncertainty and controlling risk. I have extensive on-hand experience in processing large datasets, detect patterns in stochastic models, building agent-based models and discrete-event models to solve real problems.
I receive Ph.D. in Industrial Engineering at University of Washington. My research interests are applying methods of artificial intelligence to healthcare problems. My work focuses on building clinical decision support (CDS) systems, the computer assistant to the diagnosis of diseases.
Publications
- Gong, J., & Liu, S. (2019). Optimizing personalized treatment selection for partially observable chronic conditions. [University of Washington Libraries]. link
- Gong J, Simon GE, Liu S (2019) Machine learning discovery of longitudinal patterns of depression and suicidal ideation. PLOS ONE 14(9): e0222665. https://doi.org/10.1371/journal.pone.0222665
- Gong, J., & Liu, S. (2023). Partially observable collaborative model for optimizing personalized treatment selection, European Journal of Operational Research, Volume 309,Issue 3, Pages 1409-1419, ISSN 0377-2217, https://doi.org/10.1016/j.ejor.2023.03.014.
Patents
- Wray, A. J., Pedersen, K. O. P., Cai, X., & Gong, J. (2024). Predictive learner score (U.S. Patent No. 11,922,332). U.S. Patent and Trademark Office.
- Wray, A. J., Pedersen, K. O. P., Cai, X., & Gong, J. (2024). Predictive learner recommendation platform (U.S. Patent No. 11,928,607). U.S. Patent and Trademark Office.