Projects
Personalising treatment in type 2 diabetes
Developing and testing a decision support tool for primary care to optimise the selection of glucose-lowering therapy for people with type 2 diabetes
Multimorbidity phenotyping in cardiovascular disease
Developing statistical machine learning tools to investigate longitudinal multi-disease trajectories
The Turing-RSS Health Data Lab
A working partnership between The Alan Turing Institute and Royal Statistical Society, providing independent research and support to the UK Health Security Agency
Shocks and resilience
Measuring policy impact in the COVID-19 crisis and building resilience against future shocks
Uncertainty quantification for black box models
Developing methods for the quantification of uncertainty in complex numerical models
Turing benchmarking framework
Developing a cross-architecture benchmarking framework for data science algorithms
Programmes
Research Engineering
Connecting research to applications, helping create usable and sustainable tools, practices and systems.
Urban analytics
Developing data science and AI focused on the process, structure, interactions and evolution of agents, technology and infrastructure within and between cities
Publications and software
The statistical finite element method for coherent synthesis of observation data and model predictions
The increased availability of observation data from engineering systems in operation poses the question...
Girolami, M., Febrianto, E., Yin, G., & Cirak, F. The statistical finite element method (statFEM) for coherent synthesis of observation data and model predictions. arXiv:1905.06391.
A fully data-driven approach to minimizing CVaR for portfolio of assets via SGLD with discontinuous updating
A new approach in stochastic optimization via the use of stochastic gradient Langevin dynamics...
A fully data-driven approach to minimizing CVaR for portfolio of assets via SGLD with discontinuous updating. Sabanis, Sotirios; Zhang, Ying. arXiv:2007.01672 [q-fin.PM]
Taming neural networks with TUSLA: Non-convex learning via adaptive stochastic gradient Langevin algorithms
Artificial neural networks (ANNs) are typically highly nonlinear systems which are finely tuned via...
Taming neural networks with TUSLA : Non-convex learning via adaptive stochastic gradient Langevin algorithms. Lovas, Attila; Lytras, Iosif; Rásonyi, Miklós; Sabanis, Sotirios. arXiv:2006.14514 [cs.LG]
Nonasymptotic analysis of Stochastic Gradient Hamiltonian Monte Carlo under local conditions for nonconvex optimization
Nonasymptotic analysis of Stochastic Gradient Hamiltonian Monte Carlo under local conditions for nonconvex optimization. Akyildiz, Ömer Deniz; Sabanis, Sotirios. arXiv:2002.05465 [math.OC]
Nonasymptotic estimates for Stochastic Gradient Langevin Dynamics under local conditions in nonconvex optimization.
Nonasymptotic estimates for Stochastic Gradient Langevin Dynamics under local conditions in nonconvex optimization. Zhang, Ying ; Akyildiz, Ömer Deniz ; Damoulas, Theo ; Sabanis, Sotirios. 2019. arXiv:1910.02008 [math.ST]
On stochastic gradient Langevin dynamics with dependent data streams: the fully non-convex case
We consider the problem of sampling from a target distribution which is not necessarily...
On stochastic gradient Langevin dynamics with dependent data streams: the fully non-convex case Chau, N. H.; Moulines, É.; Rásonyi, M.; Sabanis, S.; Zhang, Y. 2018. In Print, SIAM Journal on Mathematics of Data Science. arXiv:1905.13142 [math.ST]
Data Study Group Final Report: Amnesty
Monitoring online violence against high profile individuals
Data Study Group team. (2019, February 5). Data Study Group Final Report: Amnesty International. Zenodo. http://doi.org/10.5281/zenodo.2557805
Location-adjusted Wald statistics for scalar parameters
Inference about a scalar parameter of interest is a core statistical task that has...
Di Caterina, C. and Kosmidis, I. (2019). Location-adjusted Wald statistic for scalar parameters. Computational Statistics and Data Analysis, 138, 126-142
Mean and median bias reduction in generalized linear models
This paper presents an integrated framework for estimation and inference from generalized linear models...
Kosmidis, I., Kenne Pagui, E.C. & Sartori, N. (2019). Statistics and Computing. https://doi.org/10.1007/s11222-019-09860-6
Articles
The Alan Turing Institute and Royal Statistical Society to support Joint Biosecurity Centre COVID-19 response
The alliance will bolster the JBC's capabilities and support the government’s COVID-19 response
Wednesday 21 Oct 2020
Events
Interpretability, safety, and security in AI
Monday 13 Dec 2021 - Wednesday 15 Dec 2021
Time: 11:00 - 18:00
Methodological challenges and approaches in COVID-19 epidemiology
Friday 23 Apr 2021
Time: 16:00 - 17:00
The Turing Lectures: The changing role of national statistics offices in a data-driven future
Wednesday 07 Apr 2021
Time: 15:30 - 17:00
Uncertainty and risk
Wednesday 17 Mar 2021 - Friday 19 Mar 2021
Time: 14:00 - 17:00
Reliability and reproducibility in computational science
Friday 24 Jan 2020
Time: 09:30 - 17:00
Data science for physics and astronomy scoping workshop
Monday 02 Dec 2019 - Tuesday 03 Dec 2019
Time: 10:30 - 16:30
We need to talk about statistics
Monday 30 Sep 2019
Time: 16:00 - 19:00
Visualization for data science and AI
Friday 13 Sep 2019
Time: 09:45 - 18:30
Machine learning and data challenges in ransomware and the cloud
Friday 07 Jun 2019
Time: 09:30 - 17:00
Impact stories
Bridging the gap between physical and digital
The Turing’s data-centric engineering programme and its collaborators are unlocking insights into the world-first 3D printed steel bridge, using innovative data science techniques and ‘digital twin’ technology
Interest groups
Visualization
How can we gain insight into ever-growing datasets and see the reasoning behind automated AI decision-making?
Privacy-preserving data analysis
How can we enable personal data to be shared without compromising our privacy?
People
Professor Philip Dawid
Statistics and Law Fellow
Tugce Oruc
Enrichment Student
Xiaoqing Chen
Doctoral Student
Martyn Fyles
Doctoral Student
Augustinas Sukys
Doctoral Student
Georgia Tomova
Doctoral Student
Dominic Danks
Doctoral Student