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

September 01, 2019

Our Dynamic Morphoskeleton work to appear in SIAM News

January 31, 2019

Our Polar Vortex work on Forbes

December 19, 2018

ETH Medal Award for outstanding Doctoral Thesis

April 25, 2018

(in partnership with the Rhodes Trust)  More about the fellowship 

Press releases: PR Newswire,  Forbes 

 

 

 

 

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