Turing AI Fellowship: Probabilistic Algorithms for Scalable and Computable Approaches to Learning (PASCAL)

Lead Research Organisation: Lancaster University
Department Name: Mathematics and Statistics


We are living in an unprecedented age where vast quantities of our personal data are continually recorded and analysed, for example, our travel patterns, shopping habits and fitness routines. Our daily lives are now tied into this evolving loop of data collection, leading to data-based automated decisions, that can make recommendations and optimise our routines. There is tremendous economic and societal value in understanding this deluge of unstructured disparate data streams. A key challenge in Artificial Intelligence (AI) research is to extract meaningful value from these data sources to make decisions that can be trusted and understood to improve society.

The PASCAL research programme is focused on developing an end-to-end framework, from data to decisions, that naturally accounts for data uncertainty and provides transparent and interpretable decision-making tools. The algorithms developed throughout this research project will be generally-applicable in a wide range of application domains and appropriate for modern computer hardware infrastructure. All of the research and associated algorithms will be widely available through high-quality open-source software that will ensure the widest possible uptake of this research within the international AI research community.

PASCAL will focus on two primary applications areas: cybersecurity and transportation, which will stimulate and motivate this research and ensure wide-spread impact within these sectors. To drive through the impact and uptake of this research within these sectors, we will work closely with committed strategic partners, GCHQ, the Heilbronn Institute of Mathematical Research, Transport Research Laboratory, the University of Washington and the Alan Turing Institute.

Cybersecurity - The proliferation of computers and mobile technology over the last few decades has led to an exponential increase in recorded data. Much of this data is personally, economically and nationally sensitive and protecting it is a key priority for any government or large organisation. Threats to data security exist on a global scale and identifying potential threats requires cybersecurity experts to evaluate and extract critical intelligence from complex and evolving data sources. In order to model and understand the intricate patterns between these data sources requires complex mathematical models. The PASCAL programme will develop new algorithms that maintain the richness of these mathematical models and use them to provide interpretable and transparent decision recommendations.

Autonomous vehicles (AV) - The transition to AVs will be the most significant global change in transportation for the past century. The economic benefit and successful implementation of this technology within the UK requires a thorough understanding of the risks posed by driverless vehicles and what new procedures are required to ensure human safety. Through PASCAL, we will develop a framework to artificially-generate realistic traffic scenarios to test AVs under a wide range of road conditions and create criteria to safely accredit AV vehicles in the UK.


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Coullon J (2021) Ensemble sampler for infinite-dimensional inverse problems in Statistics and Computing

Description Collaboration with Dr Leah South 
Organisation Queensland University of Technology (QUT)
Country Australia 
Sector Academic/University 
PI Contribution My team and I are meeting with Dr South on a weekly basis to prepare a research project for academic publication.
Collaborator Contribution Dr Leah South is advising our project on the application of Stein's method within the context of stochastic gradient MCMC
Impact Currently in development
Start Year 2020
Description Statistical network modelling for populations of networks 
Organisation Elsevier
Department Elsevier UK
Country United Kingdom 
Sector Private 
PI Contribution Developing a tool to cluster researchers who use Elsevier's platforms.
Collaborator Contribution Elsevier has provided data and technical expertise which has allowed us to make methodological developments on this project.
Impact Ongoing
Start Year 2019
Title SGMCMCJax 
Description The software provides a toolbox of algorithms for stochastic gradient Markov chain Monte Carlo (MCMC). The package builds on the Jax library to offer users automatic differentiation tools that can be used to create gradient-based MCMC samplers. 
Type Of Technology Software 
Year Produced 2021 
Open Source License? Yes  
Impact The software has been used in publications and is part of a new book on probabilistic machine learning written by Kevin Murphy. 
URL https://github.com/jeremiecoullon/SGMCMCJax
Description Presentation at the Royal Statistical Society 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Professional Practitioners
Results and Impact Gave a presentation at an RSS workshop on Bayesian computation for Stein's method.
Year(s) Of Engagement Activity 2021
URL https://rss.org.uk/training-events/events/events-2021/sections/rss-applied-probability-and-computati...