Vollmer Research Group currently focuses on several major streams:
- AI for healthcare and public policy
- The research group has applied machine learning methods to develop tools to generate actionable insights to inform the British government’s response to Covid-19, perform treatment selection for diabetes and predict emergency admissions in Scotland.
- Bayesian methodology and Monte Carlo methods
- The development of novel methodologies and the extension of established theoretical works pertaining to machine learning.
- Event analysis
- The interconnected problems of measuring and predicting individual and societal health and well-being, through the use of longitudinal surveys, time series and survival data to model both directly observable and latent temporal states, such as adverse health events.
- Machine Learning in Julia
- Development of the software package Machine Learning in Julia (MLJ), a modelling toolbox providing a common interface and meta-algorithms for selecting, tuning, evaluating and building composite models.
- Responsible AI
- Modern technologies introduce novel challenges in algorithmic fairness, data privacy, and scientific reproduciblity. We are developing methods to detect and mitigate ethical issues in AI-assisted processes.
- Data Science for Social Good
- We use our knowledge in these areas to champion new ways of delivering impact, through short term collaborations with industrial, nonprofit and academic partners to address real-world challenges in programs that combine training and delivery.