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data-driven biophysics

proteins and gene regulatory networks regulate homeostasis, response to stress, and the emergence of complex traits, and their structure and dynamics encode function. we use machine learning and simulations to probe this relationship across scales, from single molecules to whole transcriptomes across millions of single cells

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statistical-mechanics and stochastic control

rare behavior in noisy complex biophysical systems can be inferred and regulated by tunable control knobs. we use ai models to parameterize these knobs in simulations to study molecular and evolutionary processes far from equilibrium 

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evolution

resistant cancer cells undergo clonal expansion to proliferate in new environments and even in the presence of targeted drugs. we learn how to steer these processes and the groups of genes that cause them

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