Professor Christopher Nemeth

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

• 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 StatisticalSociety (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).

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.