My research combines statistical physics, differentiable simulation and machine learning to uncover fundamental limits and design principles governing self-assembly, information flow, and molecular computation.
Ryan is a fourth-year PhD student in Applied Math working on inverse design for biomolecules
Differentiable physics models to learn collective behaviors of cells in developmental & synthetic biology
Nicholas is our resident experimentalist. He experimentally studies many soft systems: buckling of thin shells, hydrogel friction, and helical colloids. Nicholas is co-advised by Shmuel M. Rubinstein and Zvonimir Dogic.
Developing neural network-based surrogate model for Monte Carlo; Characterizing phase-change material with simulation and statistical mechanics
RNA free energy landscapes; post-translational protein oscillators; self-assembly of protein multimers
Statistical mechanics framework for design problems in Naval Engineering and self-assembly, tensor network methods for classical statistical physics.
