We are an interdisciplinary research group interested in the development of new inference and machine learning methods for developmental biology.

For biologists: Building on an expertise encompassing the study of multiple model organisms (sea urchin, c. elegans, drosophila, zebrafish, mouse), we aim at developing new methods bridging the study of morphogenesis with the study of differentiation trajectories to understand the regulation of cellular dynamics in space and time.

For computer scientists: Developmental biology brings multiple new challenges to computer science by providing data that is multi-scale, time-dependant and variable by nature. Moreover, biological systems are studied with a wide range of measurement techniques, each of which bringing out unique features. To explore this wealth of data, we are developing new data exploration and integration methods, as well as learned predictive models. Our tools are developed within graph and machine learning theory.