Learning tools for analysing land use Developing the ability to quantitatively understand the complex interplay between the scientific, economic, demographic and other factors that determine land use in the UK
Forecasting with large macroeconomic and financial datasets Improving the ability of widely used economic forecasting models to deal with volatile changes such as those caused by financial crises
Detecting anomalous connections on the internet Building statistical models to measure the likelihood of new connections being formed in a network, to help detect potentially malicious intruders
Sequential sampling methods for difficult problems Developing methods to address difficult problems by sampling from the random trajectories of collections of interacting 'particles'
Computational statistical inference for engineering and security (CoSInES) Creating a step change in the use of statistical methodology, motivated by challenges in modelling, computation, and statistical algorithms
Mapping genetic traits of cardiovascular disease Identifying new genetic risk factors for cardiovascular disease through the development of new learning tools, to aid treatment
Investigating industrial CT scanner damage Gathering community-sourced data on damage to x‑ray detectors in industrial CT scanners, to inform cost-effective maintenance
High performance, large-scale regression Investigating the effectiveness of distributed computer systems for running regression analysis, a process which determines the relationships between large numbers of variables
Adaptive optimisation algorithms Developing and improving the mathematical ‘machinery’ that will help optimisation algorithms be adaptable to diverse real world data
A blueprint for urban analytics research Thursday 11 Apr 2019 - Friday 12 Apr 2019 Time: 09:30 - 14:00 Dianna Smith Sarah Williams Stephen Law Alan Wilson Konstantin Klemmer Emmanouil Tranos Nick Malleson Nik Lomax Alison Heppenstall
Turing Lecture: Be prepared to show your working! Tuesday 11 Dec 2018 Time: 18:15 - 20:30 David Spiegelhalter
Workshop on probabilistic numerical methods Wednesday 11 Apr 2018 - Friday 13 Apr 2018 Time: 09:00 - 17:00
Turing Lecture: Statistics, decision-making and privacy Monday 05 Dec 2016 Time: 13:30 - 17:00 Cynthia Dwork
Median bias reduction in random-effects meta-analysis and meta-regression The reduction of the mean or median bias of the maximum likelihood estimator in... Kyriakou, Sophia, Kosmidis, Ioannis and Sartori, Nicola (2018) Median bias reduction in random-effects meta-analysis and meta-regression. Statistical Methods in Medical Research . doi:10.1177/0962280218771717 (In Press)
House of Lords Select Committee on Political Polling and Digital Media Call for Evidence More opinion polls than ever have been seen in recent elections...
How Deep Are Deep Gaussian Processes? Recent research has shown the potential utility of probability distributions designed through hierarchical constructions... Dunlop, Matthew & Girolami, Mark & M. Stuart, Andrew & Teckentrup, Aretha. (2017). How Deep Are Deep Gaussian Processes?