Faculty Finder

Shan Liu

Assistant Professor
Industrial & Systems Engineering

Biography

Assistant Professor 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 evaluation of new medical technologies and healthcare interventions. Her research integrates techniques such as cost-effectiveness analysis, dynamic systems modeling, and optimization under uncertainty. She develops decision theory and applied mathematical models for the detection and treatment of chronic disease when there is rapid technological development. She is more broadly interested in developing methods and tools to support complex technology adoption and policy decisions surrounding the prevention and management of diseases. She has developed decision-analytic models to assess the screening and treatment guidelines for chronic hepatitis C virus infection in the US. Her current research involves how to best incorporate uncertainties about future technology change into medical decision making methodology, and develops methods to answer questions about optimal technology adoption in the presence of uncertainties about cost, health outcomes, and technological progress. One current collaborative research is focused on depression management. Mitigating depression has become a national health priority as it affects 1 out of 10 American adults and is the most common mental illness seen in primary care. The emerging use of electronic health record (EHR) provides an unprecedented information infrastructure to understand depression trajectories. The project aims to design smart personalized monitoring algorithm for major depression onset on the patient level and cost-effective monitoring strategies on the population level. This research will have broad impact in disease trajectory modeling from EHR data, individual disease onset prediction, and population-level cost-effective screening and monitoring strategy design.

Dr. Liu has work experience and/or on-going collaborations with the Stanford Center for Primary Care and Outcomes Research, the Veteran Affairs Palo Alto Health Care System, the United Nations Industrial Development Organization, the MIT Microphotonics Center, HP Labs China, and the Cable Television Laboratories Inc. She is a member of INFORMS (Institute of Operations Research and the Management Science), the Society for Medical Decision Making (SMDM), Institute of Industrial & Systems Engineers (IISE), and the Tau Beta Pi Engineering Honor Society.

Select publications

  1. Ho T, Liu S, Zabinsky ZB. A multi-fidelity rollout algorithm for dynamic resource allocation in population disease management. In press at Healthcare Management Science, Sept 2018.
  2. Lin Y, Liu S, Huang S. Selective sensing of a heterogeneous population of units with dynamic health conditions. In press at IIE Transactions, Apr 2018.
  3. Lin Y, Huang S, Simon GE, and Liu S. 2018. Data-based decision rules to personalize depression follow-up. Scientific Reports 8(1), 5064.
  4. 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.
  5. Lin Y, Huang S, Simon GE, Liu S. 2016. Analysis of depression trajectory patterns using collaborative learning. Mathematical Biosciences 282(2016);191-203.
  6. Liu S, Brandeau M, Goldhaber-Fiebert JD. Optimizing patient treatment decisions in an era of rapid technological advances. Healthcare Management Science. Published first online July 19, 2015.
  7. 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.
  8. 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.
  9. 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.
  10. Fuchs E, Kirchain R, and Liu S. 2011. The future of silicon photonics – not so fast? Insights from 100G ethernet LAN transceivers. IEEE/OSA Journal of Lightwave Technology. 29(15): 2319-2326.