Dr. Amanda Randles is an Assistant Professor in Biomedical Engineering at Duke University with secondary appointments in Mathematics, Computer Science, and Mechanical Engineering. She is also a member of the Duke Cancer Institute. In general, her work focuses on the design of large-scale parallel applications targeting biomedical questions. Her research goals are to both investigate fundamental questions related to fluid dynamics as well as extend the multiscale models to study cancer metastasis and vascular disease. In 2014, she was awarded the NIH Early Independence Award to support the development of models of cancer migration in the human vasculature. Randles received her Bachelor's Degree in both Computer Science and Physics from Duke University, her Master's Degree in Computer Science from Harvard University, and her Ph.D. in Applied Physics from Harvard University with a secondary field in Computational Science. From 2013-2015, She was a Lawrence Fellow at Lawrence Livermore National Laboratory. Before graduate school, she worked for three years as a software developer at IBM on the Blue Gene Development Team. Randles is a co-inventor on 115 US patents in the field of parallel computing. She won the ACM/IEEE-CS George Michael High Performance Computing Fellowship in 2010 and 2012 and was a finalist for the Gordon Bell Prize for achievement in high performance computing in 2010 and 2015.
Using HPC to develop patient-specific hemodynamic models
The recognition of the role hemodynamic forces have in the localization and development of disease has motivated large-scale efforts to enable patient-specific simulations. When combined with computational approaches that can extend the models to include physiologically accurate hematocrit levels in large regions of the circulatory system, these image-based models yield insight into the underlying mechanisms
driving disease progression and inform surgical planning or the design of next generation drug delivery systems. Building a detailed, realistic model of human blood flow, however, is a formidable mathematical and computational challenge. The models must incorporate the motion of fluid, intricate geometry of the blood vessels, continual pulse-driven changes in flow and pressure, and the behavior of suspended bodies such as red blood cells. In this talk, I will discuss recent advances in massively parallel hemodynamic modeling.