George Cantwell
Assistant Professor
Probabilistic Systems, Information, and Inference group
Department of Engineering
University of Cambridge
gtc31@cam.ac.uk

I am an Assistant Professor of Innovative Computational Methods at the University of Cambridge, where I am a member of the Probabilistic Systems, Information, and Inference (Ψ²) group. I received a PhD in Physics from the University of Michigan and then completed a postdoctoral fellowship at the Santa Fe Institute.

A physicist by training, I now research in the areas of network science, complex systems, and statistical inference. I am particularly interested in the computational aspects of these areas.

If you are interested in studying for a PhD at the University of Cambridge in networks, complex systems, or statistical inference, please feel free to contact me before applying.

Papers
  1. The threshold and quasi-stationary distribution for the SIS model on networks
    George T. Cantwell and Cristopher Moore
    arXiv preprint (2025) [arXiv]
  2. Approximation for return time distributions of random walks on sparse networks
    Erik Hormann, Renaud Lambiotte, and George T. Cantwell
    Phys. Rev. E 111, 064306 (2025) [arXiv]
  3. Valence and interactions in judicial voting
    Edward D. Lee and George T. Cantwell
    Phil. Trans. R. Soc. A, 382:20230140 (2024) [code]
  4. Heterogeneous message passing for heterogeneous networks
    George T. Cantwell, Alec Kirkley, and Filippo Radicchi
    Phys. Rev. E 108, 034310 (2023) [code] [arXiv]
  5. Approximate sampling and estimation of partition functions using neural networks
    George T. Cantwell
    arXiv preprint (2022) [code]
  6. Belief propagation for permutations, rankings, and partial orders
    George T. Cantwell and Cristopher Moore
    Phys. Rev. E 105, L052303 (2022) [code] [arXiv]
  7. The friendship paradox in real and model networks
    George T. Cantwell, Alec Kirkley, and M. E. J. Newman
    Journal of Complex Networks 9(2), cnab011 (2021) [arXiv]
  8. Belief propagation for networks with loops
    Alec Kirkley*, George T. Cantwell*, and M. E. J. Newman
    Science Advances 7(17), eabf1211 (2021) [arXiv]
  9. Bayesian inference of network structure from unreliable data
    Jean-Gabriel Young, George T. Cantwell, and M. E. J. Newman
    Journal of Complex Networks 8(6), cnaa046 (2021) [code] [arXiv]
  10. Inference, Model Selection, and the Combinatorics of Growing Trees
    George T. Cantwell, Guillaume St-Onge, and Jean-Gabriel Young
    Phys. Rev. Lett. 126, 038301 (2021) [code] [arXiv]
  11. Thresholding normally distributed data creates complex networks
    George T. Cantwell*, Yanchen Liu, Benjamin F. Maier*, Alice C. Schwarze, Carlos A. Serván, Jordan Snyder, and Guillaume St-Onge
    Phys. Rev. E 101, 062302 (2020) [code] [arXiv]
  12. Improved mutual information measure for clustering, classification, and community detection
    M. E. J. Newman, George T. Cantwell, and Jean-Gabriel Young
    Phys. Rev. E 101, 042304 (2020) [code] [arXiv]
  13. Message passing on networks with loops
    George T. Cantwell and M. E. J. Newman
    Proc. Natl. Acad. Sci. U.S.A. 116, 23398-23403 (2019) [code] [arXiv]
  14. Mixing patterns and individual differences in networks
    George T. Cantwell and M. E. J. Newman
    Phys. Rev. E 99, 042306 (2019) [code] [arXiv]
  15. Balance in signed networks
    Alec Kirkley, George T. Cantwell, and M. E. J. Newman
    Phys. Rev. E 99, 012320 (2019) [arXiv]
  16. Efficient method for estimating the number of communities in a network
     Maria A. Riolo, George T. Cantwell, Gesine Reinert, and M. E. J. Newman
    Phys. Rev. E 96, 032310 (2017) [code] [arXiv]
  17. Perceptual category learning and visual processing: an exercise in computational cognitive neuroscience
    George Cantwell, Maximilian Riesenhuber, Jessica L. Roeder, and F. Gregory Ashby
    Neural Netw. 89: 31–38. (2017) [pdf]
  18. Multiple stages of learning in perceptual categorization: evidence and neurocomputational theory
    George Cantwell, Matthew J. Crossley, and F. Gregory Ashby
    Psychon. Bull. Rev. 22: 1598-1613 (2015) [pdf]

* denotes equal contribution

Thesis