Tag: Jerry Prince

REU Students Participate in the 2017 Homewood Research Symposium

August 7, 2017

The students in the 2017 Research Experience for Undergraduates Program in Computational Sensing and Medical Robotics (CSMR REU) participated in the Homewood Research Symposium and also presented their work in a final presentation event that was held on August 4, 2017. Eleven students worked for 10-weeks on cutting-edge research projects on the Homewood Campus of JHU. They were […]

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ECE Professor, Jerry Prince, Awarded 2017 Diversity Recognition Award

April 28, 2017

Professor Jerry L. Prince has been selected by the Johns Hopkins Diversity Leadership Council to receive a 2017 Diversity Recognition Award.  The Diversity Recognition Award is given to a student, staff, or faculty member who has demonstrated efforts that  advance and celebrate diversity and inclusion at Hopkins. Professor Prince will be presented with the award by […]

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Baltimore’s start-up community gives boost to Sonavex

November 1, 2016

ECE Prof. Jerry Prince and former engineering graduate student David Narrow’s Sonavex, a company that has created an ultrasound solution that detects post-operative blood clots, have benefited from Baltimore’s supportive start-up community. Read more on CNBC

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Jerry Prince speaking at lectern

Jerry Prince honored by UConn

July 7, 2015

Jerry Prince, WSE’s William B. Kouwenhoven Professor in the Department of Electrical and Computer Engineering, was named to the University of Connecticut Academy of Distinguished Engineers.

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Dr. Jerry Prince and Devin O’Brien-Coon develop EchoSure

June 24, 2015

EchoSure is a new imaging device that was developed by Jerry L. Prince and Devin O’Brien-Coon, a surgeon at Johns Hopkins Hospital, that holds the promise in easy and quick detection of blood clots in patients recovering from reconstructive, transplant, and vascular surgery.

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Jerry Prince invited to the University of Utah to present research on “Medical Image Synthesis Methods and Applications”

November 11, 2014

We have been exploring image synthesis methods to change this state of affairs. At least for the purpose of analyzing images using automatic algorithms, we are hopeful that through synthesis, we will be able to obtain more consistent results, and through this effort the results of automatic image analysis methods applied to MR images might become a more important part of clinical practice in the future.

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