Our lab focuses on understanding the dynamics of genome organization in human cells, and how these dynamics shape cell fate in differentiation, cellular reprogramming, and cancer. We have a dual approach of generating time series data in-house using human cells and by using mathematics to identify patterns in data and fill in major unknowns.

Emergence of function. Recently we built a highly idealized mathematical foundation that combines genome (within cell) and diffusion (between cell) dynamical forces. The trade-off between these forces gives rise to the emergence of function in a tissue.

A mathematical theory of Learning guided by the Immune System. Our hope is that by gaining a better understanding of the immune system and the exact way that it identifies and counteracts antigens, we will be able to improve or possibly even create novel forms of machine learning. This will be done by formulating the mathematical framework of learning in the context of the immune system which will be generalizable to any other setting.