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Shan Liu

Faculty Photo

Associate Professor
Industrial & Systems Engineering

Adjunct Associate Professor


Dr. Shan Liu joined the department in 2013. 


  • 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. 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.
  2. 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.
  3. 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.
  4. Lin Y, Huang S, Simon GE, and Liu S. 2018. Data-based decision rules to personalize depression follow-up. Scientific Reports 8(1), 5064.
  5. Cipriano LE, Goldhaber-Fiebert JD, Liu S, Weber TA. 2018. Optimal information collection policies in a Markov Decision Process framework. MDM 38(7):797-809.
  6. 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.
  7. Lin Y, Huang S, Simon GE, Liu S. 2016. Analysis of depression trajectory patterns using collaborative learning. Mathematical Biosciences 282(2016);191-203.
  8. Liu S, Watcha D, Holodniy H, Goldhaber-Fiebert JD. 2014. Sofosbuvir-based treatment regimens for chronic, genotype 1 hepatitis C infections in US incarcerated populations: a cost-effectiveness analysis. Annals of Internal Medicine 161:546-553.
  9. Liu S, Cipriano LE, Holodniy M, and Goldhaber-Fiebert JD. 2013. Cost-effectiveness analysis of risk-factor guided and birth-cohort screening for chronic hepatitis C infection in the United States. PLoS One 8(3): e58975.
  10. 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.