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).


Armbruster, S. and Reinert, G. (2023) “COVID-19 incidence in the Republic of Ireland: A case study for network-based time series models.”
Limnios, S., Selvaraj, P., Cucuringu, M., Maple, C., Reinert, G. and Elliott, A. (2023) “SaGess: Sampling Graph Denoising Diffusion Model for Scalable Graph Generation.”
Mantziou, A., Cucuringu, M., Meirinhos, V. and Reinert, G. (2023) “The GNAR-edge model: A network autoregressive model for networks with time-varying edge weights.”
Lu, Y., Reinert, G. and Cucuringu, M. (2023) “Co-trading networks for modeling dynamic interdependency structures and estimating high-dimensional covariances in US equity markets.”
Xu, W. and Reinert, G. (2023) “AgraSSt: approximate graph Stein statistics for interpretable assessment of implicit graph generators”, Advances in Neural Information Processing Systems 35 (NeurIPS 2022).
Xu, W. and Reinert, G. (2023) “A kernelised Stein statistic for assessing implicit generative models”, Advances in Neural Information Processing Systems 35 (NeurIPS 2022).
He, Y., Permultter, M., Reinert, G. and Cucuringu, M. (2022) “MSGNN: a spectral graph neural network based on a novel magnetic signed Laplacian”, in Proceedings of the First Learning on Graphs Conference (LoG 2022). Journal of Machine Learning Research, pp. 40:1 – 40:39.
Fatima, A. and Reinert, G. (2022) “Stein’s Method for Poisson-Exponential Distributions.”
Clarkson, J., Cucuringu, M., Elliott, A. and Reinert, G. (2022) “DAMNETS: a deep autoregressive model for generating Markovian network time series”, in Proceedings of the First Learning on Graphs Conference. Journal of Machine Learning Research, pp. 23:1 – 23:19.

Contact Details

College affiliation: Keble College

Office: 2.07

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