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 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
Group members
Prof Hongsheng Dai (Group Leader)
Abdullah Aloufi (PGR)
Dr Marika Asgari
Dr Tathagata Basu
Dr James Bentham
Dr Rachel Binks
Thomas Boughen (PGR)
Prof Hongsheng Dai
Dr Lee Fawcett
Axel Finke
Matthew Fisher
Yuzhang Ge
Dr Colin Gillespie
Dr Dani Leonard
Dr Danielle Notice
Dr Jordan Oakley
Juliana Ruth Okodoa
Dr Vianey Palacios Ramirez
Dr Pete Philipson
Dr Markus Rau
Dr Aleksandra Svalova
Josephine Travaglini
Dr Giorgos Vasdekis
Prof Kevin Wilson
Jie Zheng