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
December 16, 2019
- Link
-Thanks to the Schmidt Science Fellows team for making this!
September 01, 2019
Our Dynamic Morphoskeleton work to appear in SIAM News
April 25, 2018
(in partnership with the Rhodes Trust) More about the fellowship
Press releases: PR Newswire, Forbes
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