automated model discovery for soft matter

we challenge the general belief that neural networks can teach us nothing about the physics of a material. we reverse engineer a new family of constitutive artificial neural networks that have the potential to induce a paradigm shift in constitutive modeling, from user-defined model selection to automated model discovery

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.

automated model discovery for human brain

we propose a new strategy to simultaneously discover both model and parameters that best characterize the behavior of human gray and white matter tissue. our discovered model outperforms existing models in tension, compression, and shear tests

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 hierarchical priors to identify correlations between amyloid beta and tau and 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

can we reverse engineer an elephant trunk?

we explore the active filament theory that efficiently correlates fiber stretch and orientation to the intrinsic curvature of slender structures to robustly solve inverse problems in soft robotics inspired by natural manipulators such as the elephant trunk

in the news

• AI offers paradigm shift in study of brain injury stanford | news
• this week in AI TechCrunch
• 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