The biological research of the lab concerns applications of algebraic-topological methods to the analysis of biological data.
Given the highly interdisciplinary nature of this approach, the lab collaborates with a diverse range of statisticians, machine-learning researchers, and computational and wet-lab biologists. Biological applications and research directions include the following.

1. Single-cell sequencing. Our work uses persistent cohomology to identify salient geometric structures in RNA transcriptomic space. We use these geometric features to infer the expression dynamics of periodic features and cascading gene sequences in biological functions.

2. Biomolecular classification and property prediction. Our lab has produced a variety of work on simplicial neural networks. In particular, recent work in the lab using machine learning and differential forms which has been applied to analyse and predict properties of biomolecular data.

3. Neuroscience: We apply topological methods to analyzing structure and function of networks of neurons and to the study of neuron morphology. The methods developed for networks of neurons should prove applicable to other sorts of biological networks, such as metabolic networks, protein networks, etc.