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Livia is a 3rd year PhD student working on self-assembly optimization and inverse design.

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

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.

Programming cells, evolutionary dynamics, transcription network stability

I am interested in the extent to which molecular systems can be engineered to perform complex computations.

RNA free energy landscapes; post-translational protein oscillators; self-assembly of protein multimers

Computational materials design, crystal nucleation 

Developing neural network-based surrogate model for Monte Carlo; Characterizing phase-change material with simulation and statistical mechanics

Statistical mechanics framework for design problems in Naval Engineering and self-assembly, tensor network methods for classical statistical physics.