We are a group of physicists interested in understanding how living and artificial systems learn and solve goal-oriented tasks. Interests span a diverse set of problems in animal behavior, neuroscience, evolution and machine learning. We develop novel physics-inspired theory and tools to (a) build phenomenological models of learning and decision-making in collaboration with experimental biologists and (b) use machine learning, namely, deep learning models as ‘experimental systems’ to guide the development of new theoretical frameworks. Please visit the Research tab for further details. 

Our research philosophy is rooted in physics: we use data-driven theory to describe quantitative phenomena in complex systems. 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 biological complexity that will equip them to ask and address impactful biological questions.

We officially start January 2024! Please feel free to reach out to greddy AT princeton.edu if you're interested in joining our lab or collaborating with us.

We have positions available for postdoctoral fellows and Princeton graduate students. If you are a non-Princeton undergrad, please apply to the Princeton Physics and/or Biophysics graduate programs. Please reach out if you are a Princeton Physics undergrad interested in summer, junior thesis or senior thesis research.