Our Research
Our group develops strategies to study complex biophysical processes across long timescales.
Despite dramatic advances in both experiment and computation, our quantitative understanding of biological function at the molecular and cellular level
remains limited. Simulations can resolve details inaccessible to experiment but rely on empirical models, while experiments yield indirect structural
and dynamical data that cannot easily be integrated into a coherent model. Bridging this gap between computation and experiment remains a
central challenge. We design multiscale models, adaptive sampling approaches, and data analysis tools
that allow exploring large regions of a system's free energy landscape.
We use data-driven methods for systematic
coarse-graining of macromolecular systems, to bridge molecular and cellular scales. We work on a theoretical
formulation to exploit the complementary information that can be obtained in simulation and experiment, to combine
the approximate but high-resolution structural and dynamical information from computational models with the “exact”
but lower resolution information available from experiments.