Amanda Randles
Center Director, Alfred Winborne Mordecai and Victoria Stover Mordecai Associate Professor of Biomedical Engineering
Department: Biomedical Engineering
Home School: Pratt School of Engineering
Email Address: amanda.randles@duke.edu
Phone Number: 919-660-6962
Research Overview:
My research in biomedical simulation and high-performance computing focuses on the development of new computational tools that we use to provide insight into the localization and development of human diseases ranging from atherosclerosis to cancer.
Publications:
- An Interpretable Machine Learning Model to Classify Coronary Bifurcation Lesions
- Computational Fluid Modeling to Understand the Role of Anatomy in Bifurcation Lesion Disease
- Enhancing Adaptive Physics Refinement Simulations Through the Addition of Realistic Red Blood Cell Counts
- High Performance Adaptive Physics Refinement to Enable Large-Scale Tracking of Cancer Cell Trajectory
- Three-dimensional bioprinting of aneurysm-bearing tissue structure for endovascular deployment of embolization coils
- Balloon-Mounted Stents for Treatment of Refractory Flow Diverting Device Wall Malapposition
- Impact of diversity of morphological characteristics and Reynolds number on local hemodynamics in basilar aneurysms
- Hemodynamic and morphological characteristics of a growing cerebral aneurysm
- Investigating the Role of VR in a Simulation-Based Medical Planning System for Coronary Interventions
- The role of extended reality for planning coronary artery bypass graft surgery
- Harvis: an interactive virtual reality tool for hemodynamic modification and simulation
- HarVI: Real-Time Intervention Planning for Coronary Artery Disease Using Machine Learning
- The importance of side branches in modeling 3D hemodynamics from angiograms for patients with coronary artery disease
- Non-invasive characterization of complex coronary lesions
- Evaluation of intracoronary hemodynamics identifies perturbations in vorticity
- Global Sensitivity Analysis For Clinically Validated 1D Models of Fractional Flow Reserve
- Analysis identifying minimal governing parameters for clinically accurate in silico fractional flow reserve
- Diagnostic Performance of Coronary Angiography Derived Computational Fractional Flow Reserve
- Establishing the longitudinal hemodynamic mapping framework for wearable-driven coronary digital twins
- Cloud Computing to Enable Wearable-Driven Longitudinal Hemodynamic Maps
- Designing a GPU-Accelerated Communication Layer for Efficient Fluid-Structure Interaction Computations on Heterogeneous Systems
- Performance Evaluation of Heterogeneous GPU Programming Frameworks for Hemodynamic Simulations
- Optimizing Cloud Computing Resource Usage for Hemodynamic Simulation