Applied Statistics and Machine Learning
The Applied Statistics and Machine Learning group promotes collaboration to share expertise, develops new statistical methodologies and applications, and engages with partners across the University and industry.
Research group focus
The Applied Statistics and Machine Learning group aims to share statistical knowledge and expertise among its members, leading to development of new methodologies and statistical applications within various statistical research domains and collaboration with members across the University and industry partners.
Research impact
We deliver impactful and interdisciplinary research. Our contributions include supporting climate resilience through projects such as CReDo and advancing sustainable food systems via Holifood. Our group members collaborate with NHS trusts and other academic partners on a trial of a new ECG device for use with vulnerable patients. Research activities also extend to the application of advanced Bayesian methods to complex scientific challenges, such as the analysis of cosmological data. Through strong partnerships with industry, including PoleStar Global (vessel tracking), Croud (digital marketing), and One Utility Bill (energy usage forecasting), we translate cutting-edge statistical research into practical solutions, driving innovation and real-world impact across diverse sectors. Specific research areas include:
- Bayesian statistics and its applications
- prior elicitation
- experimental design and analysis
- extremes
- graphical models and causal inference
- machine learning
- risk and reliability analysis
- spatial statistics
- survival and longitudinal analysis
- statistical methods for sports performance (or, more generally, sports statistics),
- statistical epigenomics
- medical and health statistics