DE BUYL Sophie
Vrije Universiteit Brussel
Applied Physics Research Group
Pleinlaan 2, 1050 Brussels, Belgium
Sophie de Buyl obtained her PhD in theoretical physics in 2006 under the supervision of Prof. M. Henneaux at the Université Libre de Bruxelles (ULB). She brought various contributions to the understanding of the mathematical structure of Big-Bang like singularities, the gauge/gravity correspondence and the black hole entropy problem during her PhD and post-doctoral stays at the Institut des Hautes Etudes Scientifiques in Bures-sur-Yvette (2006-08), the University of California Santa Barbara (Marie Curie Fellowship, 2008-11 with return phase at ULB) and Harvard University (2011-13). During her last post-doc, she became interested in questions about living systems and connecting her theoretical work directly to experiments. After obtaining a Belspo return grant to come back to Belgium, she has joined the VUB in an interdisciplinary environment, the Applied Physics Research Group (APHY), as an Assistant Professor. She find great motivation in identifying general laws in data coming from biological systems and in the interplay between theory and experiments. Her lab is interested in all biological systems that can benefit from mathematical models and by addressing questions about robustness, fine-tuning and network architecture of these models.
Sophie de Buyl onderzoekt de dynamica en voorspelbaarheid van biologische netwerken op verschillende schalen. Samen met haar team probeert ze functionele structuren te identificeren in biologische netwerken, door modellen op te stellen op basis van grote hoeveelheden gegevens. Daarvoor gebruikt ze niet-lineaire dynamische systemen, statistische fysica en informatietheorie.
Biological systems rely on complex interaction networks at various scales. Using published datasets or working in collaboration with experimentalists, I am building models to understand emergent phenomena in biology as well as predict their temporal evolution. I am particularly interested by decision making in early embryogenesis and in building predictive dynamical models for microbial communities. All my projects rely on the theory of non-linear dynamics, statistical learning and statistical physics techniques.