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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.

Erik is a senior interested in using machine learning to accelerate scientific discovery.

Programming cells, evolutionary dynamics, transcription network stability

Currently an Assistant Professor at IST Austria.

Design of biomaterials: combining analytics and computations to optimize self-assembly