Professor Gesine Reinert

Professor of Statistics

Biographical Sketch

  • Research Professor, Department of Statistics, Oxford (2014 - present)
  • University Lecturer, Department of Statistics, Oxford, and Fellow at Keble College, Oxford (2000 – 2014)
  • Senior Research Fellow, King’s College, Cambridge (1998 – 2000)
  • Adjunct Assistant Professor, Department of Mathematics, UCLA, Los Angeles (1996 – 1998)
  • Lecturer, Department of Mathematics, USC, Los Angeles (1994 – 1996)
  • Ph.D. in Mathematics, University of Zurich, Title: A weak law of large numbers for empirical measures via Stein’s method. Advisor: Prof. A.D. Barbour, D.Phil (1994)

Research Interests

  • Applied probability
  • Computational biology
  • Stein’s method
  • Networks
  • Word count statistics

Have you heard about the phenomenon that everyone is six handshakes away from the President? The six degrees of separation hypothesis relates to a model of social interactions that is phrased in terms of a network – individuals are nodes, and two individuals are linked if they know each other. Networks pop up in a variety of contexts, and recently much attention has been given to the randomness in such networks. My main research interest at the moment are network statistics to investigate such networks in a statistically rigorous fashion. Often this will require some approximation, and approximations in statistics are another of my research interests. It turns out that there is an excellent method to derive distances between the distributions of random quantities, namely Stein’s method, a method I have required some expertise in over the years. The general area of my research falls under the category Applied Probability and many of the problems and examples I study are from the area of Computational Biology (or bioinformatics, if you prefer that name).


Xu, W. and Reinert, G. (2022) “A Kernelised Stein Statistic for Assessing Implicit Generative Models”, in Advances in Neural Information Processing Systems.
He, Y., Perlmutter, M., Reinert, G. and Cucuringu, M. (2022) “MSGNN: A Spectral Graph Neural Network Based on a Novel Magnetic Signed Laplacian”, in Proceedings of Machine Learning Research.
Temčinas, T., Nanda, V. and Reinert, G. (2021) “Multivariate Central Limit Theorems for Random Clique Complexes.”
Gaunt, R. and Reinert, G. (2021) “Bounds for the chi-square approximation of Friedman’s statistic by Stein’s method.”
He, Y., Reinert, G., Wang, S. and Cucuringu, M. (2021) “SSSNET: Semi-Supervised Signed Network Clustering.”
He, Y., Reinert, G. and Cucuringu, M. (2021) “DIGRAC: Digraph Clustering Based on Flow Imbalance”, in.
Xu, W. and Reinert, G. (2021) “A Stein goodness-of-test for exponential random graph models”, pp. 415–423.

Contact Details

College affiliation: Keble College

Office: 2.07

Graduate Students

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