Christopher Nemeth

Professor of Statistics

Lancaster University

Biography

I am a Professor of Statistics in the Department of Mathematics and Statistics at Lancaster University.

I am interested in problems pertaining to Bayesian machine learning and computational statistics, with a focus on Markov chain Monte Carlo and sequential Monte Carlo techniques. My current research is focused on Bayesian methods for large-scale datasets and tools for online learning. I have also worked on problems related to Gaussian processes and network modelling.

I currently hold a UKRI-EPSRC Turing AI Fellowship on Probabilistic Algorithms for Scalable and Computable Approaches to Learning (PASCAL) and formerly an EPSRC Innovation Fellowship on Scalable and Exact Data Science for Security and Location-based Data. I am also a Co-I on the EPSRC-funded Data Science for the Natural Environment and the NERC-funded Signals in the Soil project.

I am the Chair of the Royal Statistical Society Section on Computational Statistics and Machine Learning RSS-CSML and also a member of the EPSRC Mathematical Sciences Early Career Forum. I am currently an Associate Editor for the journal Data-Centric Engineering.

Interests

Bayesian Inference
Computational Statistics
Monte Carlo methods
Machine Learning

Education

PhD in Statistics and Operational Research, 2014

Lancaster University
MRes in Statistics and Operational Research, 2011

Lancaster University
BSc in Mathematics, 2006

University of Manchester

Publications

Vyner, C., Nemeth, C., Sherlock, C.. SwISS: A Scalable Markov chain Monte Carlo Divide-and-Conquer Strategy. ArXiv (in submission), 2022.

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Bolt, G., Lunagomez, S., Nemeth, C.. Modelling Populations of Interaction Networks via Distance Metrics. ArXiv (in submission), 2022.

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Bolt, G., Lunagomez, S., Nemeth, C.. Distances for Comparing Multisets and Sequences. ArXiv (in submission), 2022.

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Coullon, J., Nemeth, C.. SGMCMCJax: a lightweight JAX library for stochastic gradient Markov chain Monte Carlo algorithms. Journal of Open Source Software, 7(72):4113, 2022.

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Turnbull, K., Nemeth, C., Nunes, M., McCormick, T.. Sequential Estimation of Temporally Evolving Latent Space Network Models. ArXiv (in submission), 2021.

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Pinder, T., Turnbull, K., Nemeth, C., Leslie, D.. Gaussian Process on Hypergraphs. ArXiv (in submission), 2021.

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South, L.F., Karvonen, T., Nemeth, C., Girolami, M., Oates, C.J.. Semi-Exact Control Functionals From Sard's Method. Biometrika (to appear), 2021.

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Coullon, J., South, L.F., Nemeth, C.. Stochastic Gradient MCMC with Multi-Armed Bandit Tuning. ArXiv (in submission), 2021.

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Cabezas, A., Battiston, M., Nemeth, C.. Robust Bayesian Nonparametric Variable Selection for Linear Regression. ArXiv (in submission), 2021.

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Pinder, T., Hollaway, M., Nemeth, C., Young, P., Leslie, D.. A Probabilistic Assessment of the COVID-19 Lockdown on Air Quality in the UK. ArXiv (in submission), 2021.

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Fairbrother, J., Nemeth, C., Rischard, M., Brea, J., Pinder, T.. GaussianProcesses.jl: A Nonparametric Bayes package for the Julia Language. Journal of Statistical Software (to appear), 2021.

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Nemeth, C., Fearnhead, P.. Stochastic gradient Markov chain Monte Carlo. Journal of the American Statistical Association, 116(533):433-450, 2021.

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Pinder, T., Nemeth, C., Leslie, D.. Stein Variational Gaussian Processes. ArXiv (in submission), 2020.

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Verjans,V., Leeson, A.A., Nemeth, C., Stevens, C.M., Kuipers Munneke, P., Noël, B., van Wessem, J.M.. Bayesian calibration of firn densification models. The Cryosphere, 14(9):3017-3032, 2020.

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Baker, J., Fearnhead, P., Fox, E.B., Nemeth, C.. sgmcmc: An R Package for Stochastic Gradient Markov Chain Monte Carlo. Journal of Statistical Software, 91(3):1-27, 2019.

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Nemeth, C., Lindsten, F., Filippone, M., Hensman, J.. Pseudo-extended Makov chain Monte Carlo. NeurIPS, 2019.

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Turnbull, K., Lunagómez, S., Nemeth, C., Airoldi, E.. Latent Space Representations of Hypergraphs. ArXiv (in submission), 2019.

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Aicher, C., Putcha, S., Nemeth, C., Fearnhead, P., Fox, E.B.. Stochastic Gradient MCMC for Nonlinear State Space Models. ArXiv (in submission), 2019.

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Baker, J., Fearnhead, P., Fox, E.B., Nemeth, C.. Control Variates for Stochastic Gradient MCMC. Statistics and Computing, 29:599-615, 2018.

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Baker, J., Fearnhead, P., Fox, E.B., Nemeth, C.. Large-Scale Stochastic Sampling from the Probability Simplex. NeurIPS, 2018.

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Nemeth, C., Sherlock, C.. Merging MCMC Subposteriors through Gaussian-Process Approximations. Bayesian Analysis 13(2):507-530, 2018.

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Nemeth, C., Fearnhead, P., Mihaylova, L.. Particle approximations of the score and observed information matrix for parameter estimation in state space models with linear computational cost. Journal of Computational and Graphical Statistics, 25(4):1138-1157, 2016.

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Nemeth, C., Sherlock, C., Fearnhead, P.. Particle Metropolis-adjusted Langevin algorithm. Biometrika, 103(3):701-717, 2016.

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Nemeth, C.. Parameter Estimation for State Space Models using Sequential Monte Carlo Algorithms. PhD Thesis, 2014.

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Nemeth, C., Fearnhead, P., Mihaylova, L.. Sequential Monte Carlo Methods for State and Parameter Estimation in Abruptly Changing Environments. IEEE Transactions on Signal Processing, 62(5):1245-1255, 2014.

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Nemeth, C., Fearnhead, P., Mihaylova, L.. Bearings-only tracking with particle filtering for joint parameter learning and state estimation. 15th International Conference on Information Fusion, Singapore, pp.824-831, 2012.

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Nemeth, C., Fearnhead, P., Mihaylova, L.. Particle learning methods for state and parameter estimation. 9th IET Data Fusion and Target Tracking Conference, London, U.K., 2012.

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