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

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

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

are college campuses superspreaders?

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

in the news

• can computational modeling help us understand Alzheimer's disease? the future of everything
• college campuses are COVID-19 superspreaders? US news & world report
• students develop computer models to test return-to-campus strategies stanford engineering
• the newfoundland story atlantic ctv news cbc radio canada 
• how effective are travel bans? swiss public radio welt 
• passionate scientist and triathlete interview
• stanford-led team simulates how alzheimer’s disease spreads through the brain stanford report