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Alison L Marsden

from Stanford, CA
Age ~48

Alison Marsden Phones & Addresses

  • 829 Pine Hill Rd, Stanford, CA 94305
  • 2298 Oberlin St, Palo Alto, CA 94306
  • Temecula, CA
  • La Jolla, CA
  • 2312 Heather Ct, Mountain View, CA 94043
  • Livermore, CA
  • Riverside, CA
  • San Diego, CA

Resumes

Resumes

Alison Marsden Photo 1

Professor

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Location:
4238 Rickeys Way, Palo Alto, CA 94306
Industry:
Higher Education
Work:
Plos Computational Biology 2016 - 2017
Associate Editor

University of California, San Diego Jul 2013 - Jun 2015
Associate Professor

University of California, San Diego Jul 2007 - Jul 2013
Assistant Professor

Journal of Biomechanical Engineering Jul 2007 - Jul 2013
Associate Editor

Stanford University Jul 2007 - Jul 2013
Professor
Education:
Stanford University 2000 - 2005
Doctorates, Doctor of Philosophy, Philosophy, Mechanical Engineering
Stanford University 1998 - 2000
Master of Science, Masters, Mechanical Engineering
Princeton University 1994 - 1998
Bachelors, Mechanical Engineering
Skills:
Numerical Analysis
Matlab
Mathematical Modeling
Simulations
Fluid Mechanics
Cfd
Biomedical Engineering
Latex
Research
Finite Element Analysis
Fluid Dynamics
Mathematica
Optimization
Numerical Simulation
Medical Devices
Heat Transfer
Experimentation
Bioengineering
Computational Fluid Dynamics
Coronary Artery Disease
Cardiothoracic Surgery
Applied Mathematics
Expert Witness
Pediatric Cardiology
Ventricular Assist Devices
Interests:
Medical Device Design
Computational Fluid Dynamics
Cardiothoracic Surgery
Pediatric Cardiology
Biomechanics
Cardiovascular Simulation
Alison Marsden Photo 2

Alison Marsden

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Alison Marsden Photo 3

Alison Marsden

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Publications

Us Patents

Hemodynamic And Morphological Predictors Of Vascular Graft Failure

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US Patent:
20200008765, Jan 9, 2020
Filed:
Jul 3, 2019
Appl. No.:
16/502264
Inventors:
- Stanford CA, US
- Oakland CA, US
Alison L. Marsden - Stanford CA, US
International Classification:
A61B 6/00
A61B 6/03
A61B 34/10
Abstract:
A non-invasive method to predict post-surgery vascular graft failure is provided. Computer Tomography Angiography (CTA) images are obtained of a patient post-surgery. A personalized three-dimensional computer model of the patient is derived from the obtained CTA images. The personalized three-dimensional computer model distinguishes a Computational Fluid Dynamics (CFD) model coupled with a closed-loop Lumped Parameter Network (LPN). Post-surgery vascular graft predictors are calculated from the personalized three-dimensional computer model indicative, i.e. predictors, of the post-surgery vascular graft failure or vascular stenosis.
Alison L Marsden from Stanford, CA, age ~48 Get Report