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.

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.

Towards a Definition of Emergence

Various examples in the real world illustrate the emergence of structure over time. As these examples are quite diverse, e.g. structural assembly of a virus and muscle formation, identifying unifying principles is difficult. The purpose of this work is to investigate the common features of such systems with the goal of developing a systematic classification of properties associated with emergence.