Sam Zhang

Assistant Professor, Department of Mathematics and Statistics

PRONOUNS he/him

sam.zhang@uvm.edu
Sam Zhang
sam.zhang@uvm.edu

Innovation E409
Burlington, VT 05405
United States
Website
Pronouns he/him
Alma mater(s)
  • Ph.D., Applied Mathematics, University of Colorado - Boulder
  • B.A., Computer Science and Psychology, Swarthmore College
Affiliated Department(s)

Vermont Complex Systems Institute

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Areas of expertise

Computational social science, data science, applied statistics, scientific careers, inequality and biases in academia, peer review.

BIO

Sam Zhang conducts research on the dynamics of science, inequality in academia, and the broader social impacts of data and networks. At the Vermont Complex Systems Institute, Zhang and his lab develop and apply methods drawing on applied mathematics, network science, and data science. Their work examines how institutions shape scientific careers, how biases and hierarchies propagate through systems, and how quantitative approaches can inform questions of human rights and policy, helping to reveal both structural drivers and actionable insights. His research has appeared in journals such as Nature, PNAS, and Science Advances, and has been applied in contexts ranging from science policy to climate action.

Courses

  • STAT/CS/CSYS 5870 - Data Science I: Experience

Publications

Google Scholar

Awards and Achievements

US Research Software Sustainability Institute Early-Career Fellow

Bio

Sam Zhang conducts research on the dynamics of science, inequality in academia, and the broader social impacts of data and networks. At the Vermont Complex Systems Institute, Zhang and his lab develop and apply methods drawing on applied mathematics, network science, and data science. Their work examines how institutions shape scientific careers, how biases and hierarchies propagate through systems, and how quantitative approaches can inform questions of human rights and policy, helping to reveal both structural drivers and actionable insights. His research has appeared in journals such as Nature, PNAS, and Science Advances, and has been applied in contexts ranging from science policy to climate action.

Courses

  • STAT/CS/CSYS 5870 - Data Science I: Experience

Publications

Google Scholar

Awards and Achievements

US Research Software Sustainability Institute Early-Career Fellow