From the nervous to the immune system many of the most awe-inspiring feats of biological information processing emerge from the collective dynamics of a large number of specialized cells. My long term goal is to understand the physics of such emergent multicellular information processing.

I am building towards quantitative theories, which give insight into cellular mechanisms of regulation and link them to computational properties at the population scale. The principal questions that motivate my research are the following: How is the organization of biological systems shaped by considerations of efficient information transduction? How can complex information processing algorithms be implemented in self-organized cellular dynamics? More practically, my research is guided by questions such as: Are there simple phenomenological laws that describe collective behavior on the population scale despite cellular scale complexity and diversity? Can we leverage ideas and tools from statistical physics to bridge scales? What is the right language of description which allows our models to make contact with experiments?

To make progress towards my long-term goal I work on concrete biological examples of multicellular information processing many of which are taken from immunology. In immunology increasingly quantitative experimental data call for the development of a more quantitative theory of immune dynamics. To develop such a theory I am working concurrently on projects at both the mechanistic and computational level of analysis. I have also started pursuing similar questions in different biological systems. Most concretely, I have become interested in olfaction – another example of the problem of chemical sensing with many interesting similarities and differences with immunology–; and in principled approaches to understanding the diversity of cell types, which recent single-cell technologies continue to reveal at unprecedented granularity.

Common to all projects is the use of a set of tools from theoretical physics from statistical mechanics, nonlinear dynamics, and information theory to the theory of stochastic processes.