Biomedical research has been transformed by techniques that generate a flood of high-dimensional data. Although extraordinary progress has been made in using statistical or machine-learning methods to reveal predictive patterns, it is the discovery of causal mechanisms that is the gold standard for scientific and medical progress.
To go from patterns to mechanisms, this research group combines physical models with multivariate statistical techniques, applying our methods to living systems at multiple scales, from cell biology to brain dynamics. Through a dialogue between biological questions, physical theory and statistical methods we show how the full potential of high-dimensional datasets can be realised.
Our research projects
Current PhD projects include:
- Inferring decision-making events in cell lineage trees (Caleb Lau)
- Extracting cell lineage trees from time-lapse microscopy (Khelina Fedorchuk)
- Probing brain dynamics using neural population models (Agus Hartoyo)
- Understanding the mechanisms of 1/f noise in MEG data (Rick Evertz).
We also have collaborations with the Walter and Eliza Hall Institute of Medical Research (biomolecular networks) and Peter MacCallum Cancer Centre (Single T cell analysis).