René Vidal is the Herschel L. Seder Professor of biomedical engineering at the Whiting School of Engineering, director of the Vision Dynamics and Learning Lab, and a member of the Institute for Computational Medicine and the Center for Imaging Science.
His research focuses on biomedical imaging, computer vision, and machine learning. He is currently engaged in the development of mathematical methods for the interpretation of high-dimensional data, such as images, videos, and biomedical data.
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.
Vidal is a fellow of the International Association of Pattern Recognition and the Institute of Electrical and Electronics Engineers.
Vidal received his bachelor’s in electrical engineering from the Pontificia Universidad Catolica de Chile in 1997. He earned his master’s and doctorate 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.
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René Vidal receives IEEE Computer Society's 2021 Edward J. McCluskey Technical Achievement Award
CategoriesVidal was recognized "for pioneering contributions to subspace clustering and generalized principal component analysis with applications in computer vision and pattern recognition.”
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René Vidal and team receive MoDL award from the National Science Foundation-Simon’s Foundation
CategoriesResearchers will develop a mathematical, statistical and computational framework that will explain the success of current deep network architectures, understand their pitfalls, and guide the design of novel architectures with guaranteed, robustness, interpretability, optimality, and transferability.
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Vidal was recognized for outstanding contributions to medical image analysis and medical robotics.