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welcome to the living matter lab!

we integrate physics-based modeling with machine learning and create interactive simulation tools to understand, explore, and predict the dynamics of living systems

integrating bayesian inference, neural networks, and physics

we integrate data, physics, and uncertainties by combining neural networks, physics informed modeling, and bayesian inference to improve the predictive potential of traditional neural network models

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sequence of steps towards personalized cardiac medicine

precision medicine in human heart modeling

with a view towards precision medicine, we integrate human heart electrophysiology, solid mechanics, and fluid dynamics and explore clinical applications in drug development, pacing lead failure, heart failure, ventricular assist devices, and mitral valve repair

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bayesian analysis of misfolded tau

amyloid-beta drives tau pathology

we personalize a network diffusion model using longitudinal tau pet data of 76 subjects and apply bayesian inference with a hierarchical prior to infer personalized tau diffusion and production rates

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are college campuses superspreaders

are college campuses superspreaders?

we integrate a classical epidemiology model and bayesian learning to show that the first two weeks of campus opening present a high-risk period for outbreaks and that these outbreaks tend to spread into the neighboring communities

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effect of drugs

sex matters!

we integrate multiscale modeling and machine learning to gain mechanistic insight into the sex-specific origin of drug-induced cardiac arrhythmias and show that sex differences in ion channel activity, tissue conductivity, and heart dimensions put females at higher arrhythmogenic risk than males

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in the news

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