• Computational statistics
  
  • Markov chain Monte Carlo
  • Sequential Monte Carlo
  • State-space modelling
  • Statistical network analysis
• Probabilistic machine learning
  • Gaussian processes
  • Bayesian neural networks
  • Intersections between sampling and optimisation algorithms

I would be happy to supervise a PhD student who is interested in computational methods for Bayesian inference or probabilistic machine learning. In particular, the development of new MCMC and SMC algorithms for big data and intractable likelihood problems. Or projects which explore the intersection of sampling and optimisation algorithms.

My research is in the areas of computational statistics and probabilistic machine learning, specifically Markov chain Monte Carlo, sequential Monte Carlo, Gaussian processes and approximate Bayesian computation. Currently, as part of my UKRI fellowship, I am developing probabilistic AI algorithms for large-scale learning, with a focus on the mathematical foundations of these algorithms.

Applications of my research have an impact in a variety of areas, including target tracking, environmental science and econometrics. 

http://www.lancaster.ac.uk/~nemeth

• ProbAI: A Hub for the Mathematical and Computational Foundations of Probabilistic AI - UKRI-EPSRC Mathematical and Computational Foundations of Artificial Intelligence (£9M), 2024-2029.

• EP/V022636/1: Probabilistic Algorithms for Scalable and Computable Approaches to Learning (PASCAL) - UKRI-EPSRC Turing AI Acceleration Fellowship (£1.1M), 2021-2026.

• NE/T012307/1: 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.

• Turing University Academic Liason (2023-).

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

• Deputy Foundations Theme Lead, Data Science Institute (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).

• Associate Editor, ACM Transactions on Probabilistic Machine Learning (2023-).
• Associate Editor, Journal of Data-Centric Engineering (2021 - 2024).
• N8CIR Machine Learning Theme Lead (2021-).
• Chair of the Computational Statistics and Machine Learning group of the Royal Statistical Society  (2021 - 2024).
• 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).

• Thomas Pinder - Scalable Gaussian processes for modelling air quality data (2019-2022).
• Rachael Duncan - Data science approaches to projecting future global-to-local air quality and climate (2019-2023).
• George Bolt - Statistical Methods for Samples of Interaction Networks (2019-2023).
• Srshti Putcha - Scalable Monte Carlo in the general big data setting (2018-2022).
• Callum Vyner - Parallel Monte Carlo methods for Big Data (2016-2022).
• Kathryn Turnbull - Advancements in latent space network modelling (2016-2019).
• Jack Baker - Stochastic gradient algorithms for scalable Markov chain Monte Carlo (2015-2018).

• Anja Stein - Sequential Inference with Mallows Model (2023). 
• Ed Austin - Novel Methods for the Detection of Emergent Phenomena in Streaming Data (2022).
• 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).