We develop data-driven methods and mathematical models to study complex biological and physical systems, mostly defined through experimental dynamical data sets. These systems are typically nonlinear, multi-scale and chaotic, thus require new ideas to 1) best uncover the underlying causal mechanisms from their footprint on data, and 2) predict their behavior from the essential driving processes.
Morphogenesis & Active Matter
Coherent Structures
Flow Separation
Latest News
May 07, 2020
Our paper Dynamic morphoskeletons in development, just came out in PNAS
- Paper, featured on Harvard SEAS News and to appear in SIAM News
December 16, 2019
- Link
-Thanks to the Schmidt Science Fellows team for making this!
April 25, 2018
(in partnership with the Rhodes Trust) More about the fellowship
Press releases: PR Newswire, Forbes
