Professor Christopher Nemeth
Professor in StatisticsProfile
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
http://www.lancaster.ac.uk/~nemeth
Research Overview
- Computational statistics
- Markov chain Monte Carlo
- Sequential Monte Carlo
- State-space modelling
- Gaussian processes
Research Grants
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Probabilistic Algorithms for Scalable and Computable Approaches to Learning (PASCAL) - UKRI-EPSRC Turing AI Acceleration Fellowship (£1.1M), 2021-2026.
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Detecting soil degradation and restoration through a novel coupled sensor and machine learning framework - NERC Signals in the Soil grant (£799K), 2020-2022.
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NE/T004002/1: Explainable AI for UK agricultural land use decision-making - NERC Landscape decision-making grant (£43K), 2019-2020.
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EP/S00159X/1: Scalable and Exact Data Science for Security and Location-based Data - UKRI EPSRC Innovation Fellowship (£524K), 2018-2021.
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EP/R01860X/1: Data Science of the Natural Environment - EPSRC New approaches to Data Science grant (£2.7M), 2018-2023.
My Role
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Data Science Theme Lead, Centre of Excellence in Environmental Data Science (2019 - 2021)
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Computer Intensive Research Committee member (2019 - present)
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STOR-i CDT Executive Committee (2015-2019)
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STOR-i CDT Admissions Tutor (2016-2018)
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Convener for the STOR-i CDT National Associates Network (2015-2019)
External Roles
- Associate Editor, Journal of Data-Centric Engineering (2021 - present).
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Chair of the Computational Statistics and Machine Learning group of the Royal StatisticalSociety (2021 - present).
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Vice-Chair of the Statistical Computing Section of the Royal Statistical Society (2018 - 2020).
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Committee Member of the EPSRC Mathematical Sciences Early Career Forum (2018 - present).
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EPSRC Associate College Member (2018 - present).
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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
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Henry Moss - General-purpose Information-theoretical Bayesian Optimisation. Lancaster University (2021).
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Juan Manuel Escamilla Mólgora - Statistical modelling of species distributions on the tree of life using presence-only data. Lancaster University (2020).
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Sean Malory - Bayesian Inference for Stochastic Processes. Lancaster University (2020).
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Kjartan Kloster Osmundsen - Essays in Statistics and Econometrics. University of Stravanger, Norway (2020).
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Michael Bertolacci - Hierarchical Bayesian mixture models for spatiotemporal data with non-standard features. University of Western Australia, Australia (2020).
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Anthony Ebert - Dynamic Queuing Networks: Simulation, Estimation and Prediction. Queensland University of Technology, Australia (2019).
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Gernot Roetzer - Efficient and Scalable Inference for Generalized Student-t Process Models. Trinity College Dublin, Ireland (2019).
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Reinaldo A. G. Marques - On Monte Carlo Contributions for Real-time Probabilistic Inference. University of Oslo, Norway (2018).
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Terry Huang - Data Conditioned Simulation and Inference. Lancaster University (2016).
PhD Supervision Interests
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.
DSI: Probabilistic AI: Massive Scale Linking in AI Powered Knowledge Bases
01/10/2023 → 31/03/2027
Research
DSI: STOR-i : Bayesian inverse modelling and data assimilation of atmospheric emissions
01/10/2022 → 30/09/2025
Research
DSI : STOR-i - Optimising In-Store Price Reductions - Katie Howgate
01/05/2022 → 30/04/2025
Research
DSI: Turing AI Fellowship: Probabilistic Algorithms for Scalable and Computable Approaches to Learning (PASCAL)
01/01/2021 → 31/12/2025
Research
EPSRC Core Equipment 2020
01/11/2020 → 30/04/2022
Research
Detecting soil degradation and restoration through a novel coupled sensor and machine learning framework
31/01/2020 → 30/01/2023
Research
Explainable AI for UK agricultural land use decision-making
01/12/2019 → 30/11/2020
Research
Explainable AI for UK agricultural land use decision-making
01/12/2019 → 30/11/2020
Research
STORi: Learning to Group Research Profiles through Online Academic Services
01/10/2019 → 31/03/2023
Research
STORi: Statistical Analysis of Large-scale Hypergraph Data
01/10/2019 → 31/03/2023
Research
DSI: Scalable and Exact Data Science for Security and Location-based Data
29/06/2018 → 28/04/2022
Research
DSI: Data Science of the Natural Environment
16/04/2018 → 15/04/2024
Research
DSI: Bayesian Latent Space Modelling for Chemical Interactions
13/04/2018 → 12/08/2018
Research
Bayesian and Computational Statistics, Statistical Artificial Intelligence, STOR-i Centre for Doctoral Training
Bayesian and Computational Statistics, Statistical Artificial Intelligence
Statistical Artificial Intelligence
Bayesian and Computational Statistics, STOR-i Centre for Doctoral Training
Statistical Artificial Intelligence
Bayesian and Computational Statistics, STOR-i Centre for Doctoral Training
Bayesian and Computational Statistics, STOR-i Centre for Doctoral Training
Statistical Artificial Intelligence, STOR-i Centre for Doctoral Training
Bayesian and Computational Statistics, STOR-i Centre for Doctoral Training
- Bayesian and Computational Statistics
- Centre of Excellence in Environmental Data Science
- DSI - Foundations
- Statistical Artificial Intelligence
- STOR-i Centre for Doctoral Training