Bing Si received her PhD in industrial engineering from Arizona State University in 2018. She received her BS in mathematics from the University of Science and Technology of China and an MS in industrial engineering from ASU in 2012 and 2014, respectively. She is a recipient of multiple awards and scholarships, such as Grace Hopper Faculty Scholarship (2020), Dean’s Dissertation Award from Ira A. Fulton Schools of Engineering at ASU (2017), Outstanding Emerging Fulton Student Organization Leader (2017), IISE Conference Healthcare Student Best Paper 3rd Place (2017) and ASU Doctoral Fellowship (2016). She is a member of IISE, INFORMS, IEEE and SRS.
Si’s research focuses on developing novel statistical modeling and machine learning methods that leverage multi-source, multi-modal, multi-level heterogeneous data to facilitate healthcare decision-making in patient care cycle, including screening, diagnosis, prognosis, monitoring, care and system-level decision-making. Her research has been applied to a number of disease domains and health conditions such as Alzheimer’s disease, migraines, traumatic brain injury, sleep disorders, and cardiometabolic health, in collaboration with Mayo Clinic, Harvard Medical School, University of Rhode Island and others. Si’s research is sponsored by both industry and federal agencies including NIH and AHRQ with R03, R21 and R01.
- PhD, Industrial Engineering, Arizona State University, 2018
- MS, Industrial Engineering, Arizona State University, 2014
- BS, Mathematics, University of Science and Technology of China, 2012
- Statistical machine learning (sparse learning, transfer learning, unsupervised learning)
- Data fusion (high-dimensional, multi-modal, multi-level, heterogeneous data)
- Precision medicine (facilitate disease diagnosis, prognosis, monitoring)
- Population health (mitigate health disparities, improve care delivery, promote public health)