George Cantwell

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. Model inference for ranking from pairwise comparisons
    Daniel Sánchez Catalina and George T. Cantwell
    arXiv preprint (2025) [arXiv]
  2. Embedding networks with the random walk first return time distribution
    Vedanta Thapar, Renaud Lambiotte, and George T. Cantwell
    arXiv preprint (2025) [arXiv]
  3. The threshold and quasi-stationary distribution for the SIS model on networks
    George T. Cantwell and Cristopher Moore
    arXiv preprint (2025) [arXiv]
  4. 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]
  5. Valence and interactions in judicial voting
    Edward D. Lee and George T. Cantwell
    Phil. Trans. R. Soc. A, 382:20230140 (2024) [code]
  6. Heterogeneous message passing for heterogeneous networks
    George T. Cantwell, Alec Kirkley, and Filippo Radicchi
    Phys. Rev. E 108, 034310 (2023) [code] [arXiv]
  7. Approximate sampling and estimation of partition functions using neural networks
    George T. Cantwell
    arXiv preprint (2022) [code]
  8. Belief propagation for permutations, rankings, and partial orders
    George T. Cantwell and Cristopher Moore
    Phys. Rev. E 105, L052303 (2022) [code] [arXiv]
  9. 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]
  10. Belief propagation for networks with loops
    Alec Kirkley*, George T. Cantwell*, and M. E. J. Newman
    Science Advances 7(17), eabf1211 (2021) [arXiv]
  11. 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]
  12. 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]
  13. 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]
  14. 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]
  15. 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]
  16. Mixing patterns and individual differences in networks
    George T. Cantwell and M. E. J. Newman
    Phys. Rev. E 99, 042306 (2019) [code] [arXiv]
  17. Balance in signed networks
    Alec Kirkley, George T. Cantwell, and M. E. J. Newman
    Phys. Rev. E 99, 012320 (2019) [arXiv]
  18. 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]
  19. 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]
  20. 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