Sam Andersson

My research focuses on developing and applying statistical and machine learning methods to understand complex behavioural and health-related data. I am particularly interested in probabilistic modelling, uncertainty quantification, latent-state models, temporal dynamics, and interpretable machine learning.

Much of my work involves the study of behavioural processes over time. I am interested in understanding what predictive models reveal about underlying systems, how uncertainty can be represented and quantified, and how statistical models can be used to generate scientific insight rather than prediction alone.

Sam Andersson — Computational Statistician

My research interests include:

• Statistical learning and machine learning

• Computational statistics

• Probabilistic modelling and uncertainty quantification

• Bayesian methods

• Hidden-state and latent-variable models

• Time-series and longitudinal data analysis

• Stochastic processes and dynamical systems

• Behavioural data science

• Public health analytics

• Mathematical statistics

• Mathematical foundations of machine learning

• Applied mathematics for complex systems