Stephanie Knowlton Recipient of 2016 National Science Foundation (NSF) Graduate Research Fellowship Program (GRFP) Fellowship

First year BME graduate student, Stephanie Knowlton, has been awarded a National Science Foundation (NSF) Graduate Research Fellowship Program (GRFP) Fellowship. The award amount is $138,000 ($34k/year as stipend, $12k/year to institution for tuition and fees), and the fellowship period is 5 years. The NSF Graduate Research Fellowship Program recognizes and supports outstanding graduate students in NSF-supported science, technology, engineering, and mathematics disciplines who are pursuing research-based Master’s and doctoral degrees at accredited United States institutions. 

For more on the NSF Graduate Research Fellowship Program see the website here

Stephanie Knowlton earned her BS from UConn in 2015 as and is now a graduate student in Biomedical Engineering. Her current research interests include using 3D printing and magnetic levitation to develop medical technologies which can ultimately be applied in clinical settings and enhance healthcare in developing countries. Stephanie is a member of Phi Sigma Rho, a sorority for women in engineering and the Biomedical Engineering Society.


Stephanie Knowlton and her advisor Dr. Savas Tasoglu


Stephanie’s research focuses on the development of a portable diagnostic platform. Currently, many medical diagnostic procedures require advanced, expensive testing equipment, a trained technician, and labor-intensive protocols. This results in delays to patient treatment up to several days, as samples must be sent to remote testing labs where specialized and costly equipment is available. To address this need in the medical field, we have designed a low-cost, automated disease diagnostic platform which will serve as an on-site alternative to sending samples to a remote lab for testing.The compact and portable diagnostic platform functions by levitating a patient’s cells (such as a drop of blood) in a magnetic field, imaging the cells, and performing an automatic analysis on the same device to count cells based on their densities. Based on this analysis, the user interface will display medical diagnostic information for the physician. The technology can be applied to a range of diseases, such as diagnosis of sickle cell anemia. In addition, the device is user-friendly and automated, making it useful for untrained users in clinical settings, in patients’ homes, and in low-resource communities. This technology will allow medical treatment to be administered in a timely manner and enable world-wide access to diagnostic procedures.