A mathematical model

Statistical Learning

Projects

LMS Research School on Rigidity, Flexibility and Applications
18/07/2022 → 22/07/2022
Research

DSI: Turing AI Fellowship: Probabilistic Algorithms for Scalable and Computable Approaches to Learning (PASCAL)
01/01/2021 → 31/12/2025
Research

EPSRC Core Equipment 2020
01/11/2020 → 30/04/2022
Research

Future Places: A Digital Economy Centre on Understanding Place Through Pervasive Computing
01/10/2020 → 30/09/2025
Research

Google Faculty Research Award
01/06/2020 → 31/03/2025
Research

Detecting soil degradation and restoration through a novel coupled sensor and machine learning framework
31/01/2020 → 30/01/2023
Research

Explainable AI for UK agricultural land use decision-making
01/12/2019 → 30/11/2020
Research

Explainable AI for UK agricultural land use decision-making
01/12/2019 → 30/11/2020
Research

Support of collaborative research with professor Peter Bartlett at University College Berkeley, USA.
01/11/2019 → 30/04/2020
Research

DSI: Multi-armed Bandits
03/10/2019 → 02/01/2020
Research

STORi: Learning to Group Research Profiles through Online Academic Services
01/10/2019 → 31/03/2023
Research

STORi: Statistical Analysis of Large-scale Hypergraph Data
01/10/2019 → 31/03/2023
Research

DSI: Multi-armed bandit workshop
01/09/2019 → 30/11/2019
Research

DeepMind Sponsorship: Lancaster University Workshop
01/07/2019 → 31/07/2019
Research

DSI: Research supervision for Prowler.io - contract renewal
01/03/2019 → 29/02/2020
Research

STOR-i : Detailed Telematics Data Analysis
01/10/2018 → 31/03/2022
Research

Stor-i: Amazon Donation
01/10/2018 → 30/09/2022
Research

DSI: Scalable and Exact Data Science for Security and Location-based Data
29/06/2018 → 28/04/2022
Research

DSI: Data Science of the Natural Environment
16/04/2018 → 15/04/2024
Research

DSI: Bayesian Latent Space Modelling for Chemical Interactions
13/04/2018 → 12/08/2018
Research

Adobe Systems
01/12/2017 → 30/11/2018
Research

DSI : Contextual Bandits for Retail Pricing
01/03/2017 → 31/12/2018
Research

Large Scale Statistics
01/04/2016 → …
Research

Intractable Likelihood: New Challenges From Modern Applications (iLike)
01/01/2013 → 30/06/2018
Research

LETS: Locally stationary energy time series
01/09/2011 → 30/04/2016
Research

Research Activity

The world's most successful companies collect and use data as never before: Amazon's product suggestions are tailored to each individual, Google's advertising is targeted at specific individuals, and Tesco's Clubcard offers are made for a shopper's particular situation. The computer methods used to do so are usually called learning algorithms since they "learn" from data about the environment and the users.

Statistical learning analyses these algorithms, and provides new innovations, from the perspective of statistical theory, using various techniques of pure mathematics. In particular, functional analysis, probability theory and combinatorics have had a profound impact on the analysis and development of learning algorithms. With ever more data and computational power available, the field of statistical learning is in high demand in industry, and is at the forefront of research that will undoubtedly impact every individual's life in the years to come.

The statistical learning group in Lancaster has strong links with industry, particularly in the area of so-called "bandit algorithms". Each time a company has an opportunity to display an advert, it may choose one of several possible adverts to display. Each display opportunity can be used to exploit an advert currently believed to be the best, or to explore an option about which insufficient information is yet known. Managing this exploration-exploitation trade-off is a cornerstone of bandit research, and Lancaster researchers have made fundamental contributions to the area.

Furthermore, in modern applications of statistical learning, any method must be sufficiently computationally tractable to run in real time. Both for these bandit algorithms, and for statistical learning algorithms more generally, our research also focusses on how to make algorithms' computation scale well to large-data applications whilst still retaining a high statistical efficiency.