Background
Dr. René Vidal is a professor of biomedical engineering at the Whiting School of Engineering and the Johns Hopkins School of Medicine. His research focuses on biomedical imaging, computer vision and machine learning. Dr. Vidal serves as the director of the Vision Dynamics and Learning Lab in the Center for Imaging Science.
He is currently engaged in the development of mathematical methods for the interpretation of high-dimensional data, such as images, videos and biomedical data.
Dr. Vidal received his B.S. degree in electrical engineering (valedictorian) from the Pontificia Universidad Catolica de Chile in 1997. He earned his M.S. and Ph.D. degrees in electrical engineering and computer sciences from the University of California, Berkeley in 2000 and 2003, respectively. He was a research fellow at the National ICT Australia in 2003. He joined the Johns Hopkins faculty in 2004.
Dr. Vidal is the recipient of numerous awards for his work, including the 2012 J.K. Aggarwal Prize, the 2012 Best Paper Award in Medical Robotics and Computer Assisted Interventions (with Benjamin Bejar and Luca Zappella) and the 2011 Best Paper Award Finalist at the Conference on Decision and Control (with Roberto Tron and Bijan Afsari).
He is chair of the advisory board of the Computer Vision Foundation and associate editor of the journals Computer Vision and Image Understanding, Medical Image Analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence and SIAM Journal on Imaging Sciences.
Dr. Vidal is a fellow of the International Association of Pattern Recognition and the Institute of Electrical and Electronics Engineers.
Patient Ratings & Comments
The Patient Rating score is an average of all responses to physician related questions on the national CG-CAHPS Medical Practice patient experience survey through Press Ganey. Responses are measured on a scale of 1 to 5, with 5 being the best score. Comments are also gathered from our CG-CAHPS Medical Practice Survey through Press Ganey and displayed in their entirety. Patients are de-identified for confidentiality and patient privacy.