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Abigail Flower

Position: Lecturer
Office: ols118c
Phone: 434 243 5331
Personal Homepage

Ph.D., Biophysics, University of Virginia, 2008 M.A., Physics, SUNY Stony Brook, 2001 B.S., Physics, The College of William and Mary, 1999


Abigail Acton Flower, Ph.D. received her B.S. in Physics in 1999 at the College of William and Mary, also receiving the Don E. Harrison Award for Excellence in Physics for the Class of 1999. From 1999 to 2001, she was an NSF Optics Fellow in the physics graduate program at State University of New York at Stony Brook, where she graduated with a Masters degree in physics. After this, Abigail was a researcher at the Kennedy Krieger Institute of Johns Hopkins Hospital, using functional MRI to study the neural function of autistic children when performing tasks involving the use of inhibition. In 2003, Abigail began her doctoral work at the University of Virginia as a student of J. Randall Moorman, M.D., studying heart rate variability in neonates and the relationship between episodes of transient decelerations of heart rate and the onset of sepsis. She received her doctorate in Biophysics in 2008. In 2009, Abigail was awarded the Andy Ford Award for Innovation in the Field of Pediatric Cardiovascular Disease from the University of Virginia School of Medicine. Before joining us, Abigail was a senior scientist and project manager at Philips Research North America, at the Briarcliff Manor, NY campus, where her area of focus was clinical predictive modeling for particular disease states and clinical decision support in the critical care environment. Abigail was at Philips for four years, during which time she and a small team of colleagues placed third in the international Computers in Cardiology Physionet Challenge of 2012, predicting mortality in the Intensive Care Unit. During her tenure at Philips, Abigail served as a member at large of the American Physical Society New York Section and founded, and participated in, several educational outreach programs.

Research Interests:

  • Predictive clinical modeling, physiological modeling, signal processing

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