Our group develops theory that describes how living and artificial systems process information and organize to generate function. Research spans three broad themes: cognition and intelligence, collective behavior in multicellular systems, and mapping genotypes to phenotypes. Across these areas, we are interested in how complex systems discover simpler structure through learning, and how this structure enables robust behavior. Please visit the Research tab for further details. 

Much of our work sits at the interface of physics, biology, and computation. We hope to provide an intellectual home to those who wish to think about questions at this interface. A key scientific goal is to identify common principles of learning by combining insights from across biology and modern artificial intelligence. Our research philosophy is rooted in physics: we use data-driven theory to describe quantitative phenomena in complex systems. We are “methods-agnostic” and approach problems using a combination of mathematical theory, numerical experiments and experimental data analysis. 

We are passionate about training a new generation of diverse scientists who have both a strong theoretical foundation rooted in physics and an appreciation for asking interesting scientific questions. 

Please feel free to reach out to greddy AT princeton.edu if you're interested in joining/collaborating with us.

If you are a non-Princeton undergrad, please apply to the Princeton Physics and/or Biophysics graduate programs. Please reach out to the above email if you are a Princeton physics undergrad interested in senior thesis research.