- Biomedical image analysis: Surgical gesture and skill recognition, analysis of high angular resolution diffusion imaging (HARDI), classification of stem cell derived cardiac myocites, interactive medical image segmentation
- Computer vision:
camera sensor networks,
dynamic texture segmentation and recognition, 3D motion segmentation,
non-rigid shape and motion analysis, structure from motion and multiple view geometry, omnidirectional vision
- Machine learning:
manifold clustering, kernels on dynamical systems, GPCA, kernel GPCA, dynamic GPCA
- Dynamical systems:
observability, identification, realization, metrics and topology for hybrid systems
formation control of teams of non-holonomic robots,
coordination and control of multiple autonomous vehicles for pursuit-evasion games,
multiple view motion estimation and control for landing an unmanned aerial vehicle
- Signal processing:
consensus on manifolds, distributed optimization, compressive sensing.
Keen focus on the development of computational methods includes: 1) inferring models from images (image/video segmentation, motion segmentation), static data (subspace clustering) or dynamic data (identification of hybrid systems); and 2) using these models to accomplish a complex task — such as tracking fibers in the brain, recognizing actions in videos, landing a helicopter on a moving platform, pursuing a team of evaders, or following a formation.
- Herschel L. Seder Professor, Biomedical Engineering
- Professor, Computer Science
- Professor, Electrical & Computer Engineering
- Professor, Mechanical Engineering
- Director, Mathematical Institute for Data Science
Affiliated Centers & Institutes
- PhD, Electrical Engineering and Computer Sciences, University of California at Berkeley, 2003
- MS, Electrical Engineering and Computer Sciences, University of California at Berkeley, 2000
- MS, Engineering, Automatic Control, Catholic University of Chile, 1997
- BS, Electrical Engineering, Catholic University of Chile, 1995
December 22, 2021News Brief: René Vidal receives IEEE Computer Society’s 2021 Edward J. McCluskey Technical Achievement AwardVidal was recognized "for pioneering contributions to subspace clustering and generalized principal component analysis with applications in computer vision and pattern recognition.”
June 28, 2021Using a $7.5 million, five-year grant from the U.S. Department of Defense, a multi-university team that includes Johns Hopkins engineers is tackling one of today's most complex and important technological challenges: How to ensure the safety of autonomous systems, from self-driving cars and aerial delivery drones to robotic surgical assistants.
August 25, 2020René Vidal, the Herschel L. Seder Professor in Johns Hopkins Department of Biomedical Engineering, is leading a team of engineers, mathematicians, and theoretical computer scientists from multiple institutions who seek to revolutionize our understanding of the mathematical and scientiﬁc foundations of deep learning.