James Bagrow
Associate Professor, Department of Mathematics & Statistics
PRONOUNS he/him
Innovation Hall E426
82 University Place
Burlington, VT 05405
United States
- 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
Department of Mathematics & Statistics
Department of Computer Science
Vermont Complex Systems Center
BIO
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.
Courses
- CS/STAT 3870 - Data Science I
- CS/STAT 6870 - Data Science II
- MATH 3201 - Advanced Engineering Mathematics
Area(s) of expertise
Network Science, Complex Systems, Data Science and Machine Learning, Computational Social Science, Mathematical Modeling
Bio
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.
Courses
- CS/STAT 3870 - Data Science I
- CS/STAT 6870 - Data Science II
- MATH 3201 - Advanced Engineering Mathematics
Areas of Expertise
Network Science, Complex Systems, Data Science and Machine Learning, Computational Social Science, Mathematical Modeling
Publications
Selected Publications:
- 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 (2018) 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