Statistical Artificial Intelligence

An artistic rendition of a brain surrounded by circuits

What is Statistical AI?

The world's most successful companies have revolutionized their operations by collecting and utilising data like never before. Companies such as Amazon, Google, and Tesco have harnessed the power of data to enhance their services and offerings. For instance, Amazon's personalized product suggestions cater to the unique preferences of each individual customer. Google's advertising campaigns are targeted at specific individuals, ensuring that the right ads reach the right audience. Tesco's Clubcard program tailors its offers based on a shopper's particular purchases. These remarkable capabilities are made possible through the application of learning algorithms, which have the ability to "learn" from data about the environment and user behaviour.

Statistical learning is a field that analyses and advances these algorithms by leveraging statistical theory and various techniques from the wider mathematics literature. It draws upon disciplines such as functional analysis, probability theory, and combinatorics to deepen our understanding of learning algorithms and develop new innovations. These mathematical foundations have profoundly influenced the analysis and evolution of learning algorithms, allowing researchers and practitioners to uncover patterns, make predictions, and gain insights from data.

As the availability of data and computational power continues to grow exponentially, the demand for statistical learning in industry is skyrocketing. Within Lancaster’s Statistical Learning group, we work with a number of leading industries to advance the applications of our research, with partners including Amazon, Microsoft, Shell and Tesco.

Current areas of research

  • Bandit algorithms - companies face the challenge of selecting the most effective advertisement to display from a pool of several possibilities. Each display opportunity presents an opportunity to exploit an advert that is currently believed to be the best or explore a new option for which limited information is available. The management of this exploration-exploitation trade-off lies at the core of bandit research, and the researchers at Lancaster have made fundamental contributions to this field.
  • Inference at scale - Bayesian inference is a statistical framework that allows us to update our beliefs about uncertain quantities based on observed data. It provides a principled way to incorporate prior knowledge and update it with new evidence. However, traditional Bayesian inference methods can become computationally intractable when dealing with massive datasets or complex models. Researchers at Lancaster have made significant contributions to the area of stochastic gradient Markov chain Monte Carlo algorithms for machine learning, developing the underpinning theoretical convergence for these algorithms and creating open-source software to support the use of these algorithms.

Academics in the Statistical Learning group supervise a number of PhD students in the department and within the STOR-i CDT. Through the MSc Statistics and MSc Data Science, dissertation projects on topics of statistical learning are offered to PGT students. At the undergraduate level, Statistical Learning techniques are taught in the 3rd Year Machine Learning course.

Projects

Enhancements to design and testing of Body Aspect's breast volume measurement software (A4I)
01/09/2023 → 31/01/2024
Research

Quality Control Test efficiency
01/03/2023 → 30/09/2023
Research

Sub-millimetre accuracy of 3D measurements and texture analysis of Diabetic Foot Ulcers
01/03/2023 → 31/08/2023
Research

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

DSI : STOR-i - Optimising In-Store Price Reductions - Katie Howgate
01/05/2022 → 30/04/2025
Research

Was that Change Real? Quantifying Uncertainty for Change Points
11/10/2021 → 10/10/2024
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
14/11/2020 → 13/05/2022
Research

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

DSI:STORi: Anomaly Detection for Real-time Condition Monitoring
01/08/2020 → 31/03/2026
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 → 16/09/2024
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

DSI: CoSInES: COmputational Statistical INference for Engineering and Security
01/10/2018 → 30/09/2024
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 → 30/09/2021
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

New Approaches to Bayesian Data Science: Tackling Challenges from the Health Sciences
01/04/2018 → 31/07/2024
Research

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

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

Statscale: Statistical Scalability for streaming data
01/06/2016 → 31/05/2023
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