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Christopher Nemeth supervises 9 postgraduate research students. If these students have produced research profiles, these are listed below:

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Professor Christopher Nemeth

Professor in Statistics, Research Student

Christopher Nemeth

Fylde College



Research overview

  • Computational statistics
  • Markov chain Monte Carlo
  • Sequential Monte Carlo
  • State-space modelling
  • Gaussian processes

PhD supervision

I would be happy to supervise a PhD student who is interested in computational methods for Bayesian inference. In particular, the development of new MCMC and SMC algorithms for big data and intractable likelihood problems.


My research is in the areas of computational statistics and statistical machine learning, specifically Markov chain Monte Carlo, sequential Monte Carlo, Gaussian processes and approximate Bayesian computation for intractable likelihoods. Currently, I am working on the problem of efficient Bayesian inference for big data problems via distributed computing and data sub-sampling. My research has an impact in a variety of application areas including target tracking, ecology and econometrics and I am currently collaborating extensively with a number of climate scientists on environmental data science challenges.

Web Links


Research Grants

  • Probabilistic Algorithms for Scalable and Computable Approaches to Learning (PASCAL) - UKRI-EPSRC Turing AI Acceleration Fellowship (£1.1M), 2021-2026.

  • Detecting soil degradation and restoration through a novel coupled sensor and machine learning framework - NERC Signals in the Soil grant (£799K), 2020-2022.

  • NE/T004002/1: Explainable AI for UK agricultural land use decision-making - NERC Landscape decision-making grant (£43K), 2019-2020.

  • EP/S00159X/1: Scalable and Exact Data Science for Security and Location-based Data - UKRI EPSRC Innovation Fellowship (£524K), 2018-2021.

  • EP/R01860X/1: Data Science of the Natural Environment - EPSRC New approaches to Data Science grant (£2.7M), 2018-2023.

My Role

  • Data Science Theme Lead, Centre of Excellence in Environmental Data Science (2019 - 2021)

  • Computer Intensive Research Committee member (2019 - present)

  • STOR-i CDT Executive Committee (2015-2019)

  • STOR-i CDT Admissions Tutor (2016-2018)

  • Convener for the STOR-i CDT National Associates Network (2015-2019)

External Roles

  • Associate Editor, Journal of Data-Centric Engineering (2021 - present).
  • Chair of the Computational Statistics and Machine Learning group of the Royal Statistical
    Society  (2021 - present).

  • Vice-Chair of the Statistical Computing Section of the Royal Statistical Society (2018 - 2020).

  • Committee Member of the EPSRC Mathematical Sciences Early Career Forum (2018 - present).

  • EPSRC Associate College Member (2018 - present).

  • UKRI Future Leaders Fellowship Peer Review College Member (2018 - present).

PhD Supervisions Completed

Jack Baker - Stochastic gradient algorithms for scalable Markov chain Monte Carlo (2015-2018).

Kathryn Turnbull - Advancements in latent space network modelling (2016-2019).

PhDs Examined

  • Henry Moss - General-purpose Information-theoretical Bayesian Optimisation. Lancaster University (2021).

  • Juan Manuel Escamilla Mólgora - Statistical modelling of species distributions on the tree of life using presence-only data. Lancaster University (2020).

  • Sean Malory - Bayesian Inference for Stochastic Processes. Lancaster University (2020).

  • Kjartan Kloster Osmundsen - Essays in Statistics and Econometrics. University of Stravanger, Norway (2020).

  • Michael Bertolacci - Hierarchical Bayesian mixture models for spatiotemporal data with non-
    standard features. University of Western Australia, Australia (2020).

  • Anthony Ebert - Dynamic Queuing Networks: Simulation, Estimation and Prediction. Queensland University of Technology, Australia (2019).

  • Gernot Roetzer - Efficient and Scalable Inference for Generalized Student-t Process Models. Trinity College Dublin, Ireland (2019).

  • Reinaldo A. G. Marques - On Monte Carlo Contributions for Real-time Probabilistic Inference. University of Oslo, Norway (2018).

  • Terry Huang - Data Conditioned Simulation and Inference. Lancaster University (2016).

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