Dr. Jingui Xie is an associate professor of Business Analytics since 2020 at the TUM School of Management, TUM Campus Heilbronn. Data availability and advancement in machine learning techniques make accurate predictions of the future a foreseeable reality. Prof. Xie’s research aims to efficiently incorporate the predictive information into the decision-making through a joint estimation and optimization framework. In particular, he is interested in using big data and analytics to improve healthcare worldwide.
Jingui Xie earned his Ph.D. in Management Science and Engineering in 2010 at Tsinghua University. His dissertation topic dealt with priority design in service systems. In the course of his Ph.D. studies, Jingui Xie was a visiting scholar at Columbia University. After earning his Ph.D., he was a research fellow at the National University of Singapore. His research has been featured in various Analytics and Operations Journals, as well as Public Health and Medicine Journals.
COVID-19 Data Analytics: The Coronavirus (COVID-19) has become a severe public health problem globally. This project aims to determine whether temperature, air pollution, social mobility, etc. are essential factors in the infection caused by this novel coronavirus. The goal is to efficiently allocate limited medical resources even when the prediction with data and model is highly inaccurate.
ICU Data Analytics: The project is to develop a sequential and dynamic decision process with predictions to support decisions on medical treatment continuation and apply the model to the mechanical ventilator extubation problem in an intensive care unit (ICU). Using patient-level data, we compare the performance of different policies and demonstrate that incorporating predictive information can reduce ICU length of stay and decrease the failure rate of ventilated patients, especially for patients with poor initial conditions.
