research
machine learning in biomedicine
Fueled by breakthrough technology developments, the biomedical sciences are now collecting more data than ever before. There is a critical need for time- and cost-efficient strategies to analyze and interpret these data to advance human health. We integrate multiscale modeling and machine learning to provide robust simulation tools that exploit the underlying physics, infer model parameters, and predict system dynamics.
multiphysics of the human brain
The brain is the most complex organ of the human body, and, at the same time, the least well understood. Our lab uses engineering concepts of stress, stretch, and strain to provide new insights into the form and function of the brain. We use multiscale multiphysics modeling to integrate knowledge across the scales and calibrate and validate our models with cell- and tissue-level testing, histological, and clinical data to understand living human brain.
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multiphysics of the human heart
Heart disease is the leading cause of death in developed countries, claiming more than 16 million lives worldwide each year. We thrive to provide an in-depth understanding of cardiac physiology and improve treatment strategies for heart disease by integrating multiscale modeling and machine learning.
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data-driven modeling of COVID-19
The COVID-19 pandemic has caused more than 50 million cases and an unprecedented amount of data. Yet, the precise role of mathematical modeling in understanding COVID-19 remains a topic of ongoing debate. We use data-driven modeling to integrate classical epidemiology modeling and machine learning to infer critical disease parameters—in real time—from the reported data to make informed predictions and guide political decision making.
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