Suzanne Tamang is a postdoctoral fellow at Stanford University, where she is part of the Center for Biomedical Informatics Research and a member of the Shah Lab. Her work focuses on the development and application of methods to mine digital health data for health system improvement.

High-value healthcare requires an organizational capacity to achieve the best outcomes at the lowest cost. Relative to other developed countries, US healthcare spending is considerably more per capita, but our high costs do not correspond with better outcomes. To enable high-value transformation, Suzanne’s work aims to provide a better understanding of the complex relationships among those that receive, provide and finance care, and to broaden the definitions of health and value. Her interests include medical language processing, health outcomes research, and temporal modeling.

Suzanne received a Ph.D. in Computer Science from the City University of New York. Her thesis was on unsupervised learning methods for modeling chronic diseases dynamics. As a graduate student, she received a NSF fellowship, the Earnest Malve Student Leadership Award, and was a Provost’s Digital Innovation Grant recipient. She has developed top ranking system submissions for NIST sponsored challenges on automated knowledge base population from a large-scale document collection, and her research has been published at various computer and health science conferences including KDD, ICML, AAAI, ACL, SIGIR, Academy Health and APHA.

You can find out more about Suzanne on her website

Professional Education

  • Doctor of Philosophy, C.U.N.Y. Graduate School & Univ Center, Computer Science (2013)
  • Bachelor of Science, Brooklyn College, Biology

Stanford Advisors

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