James Bagrow
Associate Professor, Department of Mathematics and Statistics
Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).
Selected Publications
A review and framework for modeling complex engineered system development processes
IEEE Transactions on Systems, Man, and Cybernetics, April 11, 2022
Contrasting social and non-social sources of predictability in human mobility
Nature Communications, April 8, 2022
Sleep during travel balances individual sleep needs
Nature Human Behaviour, Feb. 24, 2022
Flexible environments for hybrid collaboration: Redesigning virtual work through the four orders of design
Design Issues, Jan. 2, 2022
Recovering lost and absent information in temporal networks
Preprint, July 22, 2021
Which contributions count? Analysis of attribution in open source
2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR), May 18, 2021
An information-theoretic, all-scales approach to comparing networks
Applied Network Science, July 16, 2019
Information flow reveals prediction limits in online social activity
Nature Human Behaviour, Jan. 21, 2019
Human language reveals a universal positivity bias
Proceedings of the National Academy of Sciences, Feb. 9, 2015
Selected Press
Study suggests travel can help balance out sleep hours
Medical Xpress, March 2, 2022
Short on sleep? Taking a trip could actually help
Nature, March 1, 2022
Open source ecosystems need equitable credit across contributions
Nature Computational Science, Jan. 14, 2021
UVM gets $1M from Google for open source research
WCAX, Jan. 13, 2020
UVM Complex Systems Center to advance open source research with support from Google
UVM Today, Jan. 10, 2020
Think your data is private because you're not on social media? Think again.
Forbes, Jan. 23, 2019
People can predict your tweets—even if you aren't on Twitter
Science, Jan. 21, 2019