We work on problems in quantitative biology, non-equilibrium dynamics, and theoretical computer science. A frequent question is how collective dynamics in physical and biological systems can generalize past experiences, i.e., learn, and respond differently to future inputs.
Current work includes:
1. Biological learning and adaptation in changing environments
(in circadian clocks with the Rust lab, in gene regulation with the Tay lab,
in molecular evolution with the Wang and Ranganathan labs, in neural networks)
2. Learned behaviors in materials
(elastic materials, DNA systems, active materials).
Openings: If you are interested in working on such themes as a postdoc, graduate student or undergrad, contact email@example.com.
Funding: Our work is primarily supported by the NSF and a Simons Foundation Investigator award. Lab members have been supported by the James S. McDonnell Foundation and NSF fellowships.