We are interested in the understanding of complex, interconnected and dynamic biological systems, and specifically the problems of inference and design connecting single cell identities and interactions to global structures and collaborative cellular ecosystems. We use methods and ideas from computer science and physics (e.g. optimization, statistical physics, nonlinear dynamics, graph theory, and machine learning), and in addition, use biological processes as inspiration for developing new methods and approaches (e.g. in the fields of biologically-inspired graphical models, network reconstruction, and structured compressed sensing).
We have an interdisciplinary group of students and postdocs with backgrounds in Computer Science, Physics, Computational Biology, Cognitive Sciences, and Mathematics.
We are looking for excellent graduate students and postdocs to join our group.