Christopher Nemeth

Lecturer in Statistical Learning

Lancaster University

Biography

I am a Senior Lecturer (Associate Professor) in Statistics in the Department of Mathematics and Statistics and Lancaster University.

I currently hold an EPSRC fellowship on Scalable and Exact Data Science for Security and Location-based Data and I am a Co-I on the EPSRC-funded Data Science for the Natural Environment.

I work on problems related to Gaussian processes, Bayesian inference 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 Big Data and tools for online learning.

I am the vice-chair the Royal Statistical Society group on Computational Statistics and Machine Learning RSS-CSML. I am also currently a member of the EPSRC Mathematical Sciences Early Career Forum.

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

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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South, L.F., Karvonen, T., Nemeth, C., Girolami, M., Oates, C.J.. Semi-Exact Control Functionals From Sard's Method. ArXiv (in submission), 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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Nemeth, C., Fearnhead, P.. Stochastic gradient Markov chain Monte Carlo. Journal of the American Statistical Association (to appear), 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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Fairbrother, J., Nemeth, C., Rischard, M., Brea, J.. GaussianProcesses.jl: A Nonparametric Bayes package for the Julia Language. ArXiv (in submission), 2018.

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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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