Dr Christopher NemethLecturer in Statistics
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.
- Computational statistics
- Markov chain Monte Carlo
- Sequential Monte Carlo
- State-space modelling
- Gaussian processes
- 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.
Data Science Theme Lead, Centre of Excellence in Environmental Data Science (2019 - present)
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)
Chair of the Computational Statistics and Machine Learning group of the Royal StatisticalSociety. (2017 - present).
Vice-Chair of the Statistical Computing Section of the Royal Statistical Society. (2018 - present).
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).
Anthony Ebert - Dynamic Queuing Networks: Simulation, Estimation and Prediction. Queens-land 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.
Explainable AI for UK agricultural land use decision-making
01/12/2019 → 30/11/2020
DSI: Scalable and Exact Data Science for Security and Location-based Data
29/06/2018 → 28/10/2021
DSI: Data Science of the Natural Environment
16/04/2018 → 15/04/2024
DSI: Bayesian Latent Space Modelling for Chemical Interactions
13/04/2018 → 12/08/2018
Bayesian and Computational Statistics, Statistical Learning, STOR-i Centre for Doctoral Training
- DSI - Foundations
- Statistical Learning
- STOR-i Centre for Doctoral Training