Associate Professor

James Bagrow is an Associate Professor of Mathematics & Statistics at the University of Vermont and a member of the Vermont Complex Systems Center. Before joining Vermont, he was a postdoctoral researcher at the Center for Complex Networks Research at Northeastern University and a research assistant professor at Northwestern University. Professor Bagrow received his Ph.D. in Physics from Clarkson University in 2008. He is interested in understanding the underlying rules and organizing principles of complex physical and social systems. His work combines mathematical models with large-scale data analysis to better understand these systems, with a particular emphasis on network science and human dynamics. Other interests include data science, stochastic and nonlinear dynamics, dynamical systems, and novel optimization and machine learning methods.


Link communities reveal multiscale complexity in networks (2010)
YY Ahn, JP Bagrow, S Lehmann,
Nature 466 (7307), 761–764

Collective response of human populations to large-scale emergencies (2011)
JP Bagrow, D Wang, AL Barabasi
PloS one 6 (3), e17680

Understanding the group dynamics and success of teams (2016)
M Klug, JP Bagrow
Royal Society open science 3 (4), 160007

Efficient crowd exploration of large networks: The case of causal attribution
D Berenberg, JP Bagrow
Proceedings of the ACM on Human-Computer Interaction 2 (CSCW), 24

The quoter model: A paradigmatic model of the social flow of written
information (2018)
JP Bagrow, L Mitchell
Chaos: An Interdisciplinary Journal of Nonlinear Science 28 (7), 075304

Information flow reveals prediction limits in online social activity (2019)
JP Bagrow, X Liu, L Mitchell
Nature human behaviour 3 (2), 122

Areas of Expertise and/or Research

Network Science, Complex Systems, Data Science and Machine Learning, Computational Social Science, Mathematical Modeling


  • Ph.D., Physics, Clarkson University
  • M.S., Physics, Clarkson University
  • B.S., Physics with Great Distinction, Clarkson University
  • A.S., Liberal Arts & Sciences, SUNY Cobleskill, Cobleskill, NY, USA


Office Location:

Innovation Hall E426

Courses Taught

  • STAT/CS 287 - Data Science I
  • STAT/CS 387 - Data Science II
  • MATH 271 - Advanced Engineering Mathematics