Biography

Dr. Shan Liu joined the department in 2013. 

Education

• Ph.D. in Management Science & Engineering, Stanford University, 2013
• M.S. in Technology and Policy, Massachusetts Institute of Technology, 2008
• B.S. in Electrical Engineering, The University of Texas at Austin, 2006

Research Statement

Dr. Liu’s research interests focus on the design and evaluation of healthcare interventions to improve patients’ health and enable cost-effective care delivery. She develops methods in sequential decision making, decision analytics, and systems modeling to optimize healthcare systems. Her work advances mathematical modelling methods to support complex technology adoption and policy decisions surrounding the prevention and management of diseases. She has received multiple grants from the National Science Foundation to design an integrated decision framework for depression monitoring, and optimize vaccination incentives to prevent infectious disease outbreaks.

Dr. Liu has collaborated with the UW Medicine and Global Health, the Stanford Center for Primary Care and Outcomes Research, the Veteran Affairs Palo Alto Health Care System, and the Kaiser Permanente Washington Health Research Institute in Seattle. She is a member of the Institute for Operations Research and the Management Science (INFORMS), the Society for Medical Decision Making (SMDM), Institute of Industrial and Systems Engineers (IISE), and the Tau Beta Pi Engineering Honor Society.

Select publications

1. Wang Y, Wagner A, Liu S, Kingwara L, Oyaro P, Brown E, Karauki E, Yongo N, Patel R. 2024. Using queueing models as a decision support tool for policymakers to allocate point-of-care viral load testing machines in Kenya. Health Policy and Planning, 39(1): 44-55.
2. Sun X, Liu S, Mock C, Vavilala M, Bulger E, Maine R. 2024. Unsupervised clustering analysis of trauma/non-trauma centers using non-surgical care and surgical care features. PLoS ONE, 19(8): e0306299.
3. Gong J, and Liu S. 2023. Partially observable collaborative model for optimizing personalized treatment selection. European Journal of Operational Research (EJOR), 309(3): 1409-1419.
4. Ho T, Zabinsky ZB, Fishman PA, Liu S. 2022. Prevention of seasonal influenza outbreak via healthcare insurance. IIE Transactions on Healthcare Systems Engineering 13(9):1-37.
5. Lee S, Zabinsky ZB, Wasserheit J, Kofsky SM, Liu S. 2021. COVID-19 pandemic response simulation in a large city: impact of non-pharmaceutical interventions on reopening society. MDM 41(4): 419-429.
6. Lin Y, Huang S, Simon GE, Liu S. 2019. Cost-effectiveness analysis of prognostic-based depression monitoring. IIE Transactions on Healthcare Systems Engineering 9(1):41-54.
7. Ho T, Liu S, Zabinsky ZB. 2019. A multi-fidelity rollout algorithm for dynamic resource allocation in population disease management. Healthcare Management Science 22(4):727-755.
8. Lin Y, Liu S, Huang S. 2018. Selective sensing of a heterogeneous population of units with dynamic health conditions. IIE Transactions 50 (12):1076-1088.
9. Lin Y, Huang S, Simon GE, and Liu S. 2018. Data-based decision rules to personalize depression follow-up. Scientific Reports 8(1), 5064.
10. Liu S, Brandeau M, Goldhaber-Fiebert JD. 2017. Optimizing patient treatment decisions in an era of rapid technological advances. Healthcare Management Science 20(1):16-32.
11. Lin Y, Huang S, Simon GE, Liu S. 2016. Analysis of depression trajectory patterns using collaborative learning. Mathematical Biosciences 282(2016);191-203.
12. Liu S, Cipriano LE, Holodniy M, Owens DK, and Goldhaber-Fiebert JD. 2012. New protease inhibitors for the treatment of chronic hepatitis C: A cost-effectiveness analysis. Annals of Internal Medicine 156: 279-290.

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