I am an assistant professor in the Department of Biomedical Engineering at The Johns Hopkins University. I direct the Vision Dynamics and Learning Lab, which is part of the Center for Imaging Science (CIS) and the Institute for Computational Medicine (ICM). We are also afiliated with the Laboratory for Computational Sensing and Robotics (LCSR). My research spans a wide range of areas in biomedical imaging, computer vision, machine learning, dynamical systems theory and robotics. Most of our work deals with the development of computational methods for (1) inferring models from images (image/video segmentation, motion segmentation), static data (subspace clustering) or dynamic data (identification of hybrid systems), and (2) using such models to accomplish a complex task (recognize actions in videos, land a helicopter on a moving platform, pursue a team of evaders, follow a formation, etc.)

Biomedical imaging: fiber tracing in the Purkinje network, segmentation and registration of diffusion weighted images, heart motion analysis

Computer vision: camera sensor networks, activity recognition, dynamic scene analysis, non-rigid 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: consensus on manifolds, observability, identification, and realization of hybrid systems

Robotics: vision-based control of unmanned ground and aerial vehicles

Professor Vidal received his B.S. degree in Electrical Engineering (highest honors) from the Pontificia Universidad Catolica de Chile in 1997 and his M.S. and Ph.D. degrees in Electrical Engineering and Computer Sciences from the University of California at Berkeley in 2000 and 2003, respectively. He was a research fellow at the National ICT Australia in the Fall of 2003 and joined The Johns Hopkins University in January 2004 as an Assistant Professor in the Department of Biomedical Engineering and the Center for Imaging Science. His areas of research are biomedical imaging, computer vision, dynamical systems, machine learning, and robotics. Dr. Vidal is recipient of the 2005 NFS CAREER Award and the 2004 Best Paper Award Honorable Mention (with Prof. Yi Ma) for his work on "A Unified Algebraic Approach to 2-D and 3-D Motion Segmentation" presented at the European Conference on Computer Vision. He also received the 2004 Sakrison Memorial Prize for "completing an exceptionally documented piece of research", the 2003 Eli Jury award for "outstanding achievement in the area of Systems, Communications, Control, or Signal Processing", the 2002 Student Continuation Award from NASA Ames, the 1998 Marcos Orrego Puelma Award from the Institute of Engineers of Chile, and the 1997 Award of the School of Engineering of the Pontificia Universidad Catolica de Chile to the best graduating student of the school. He was a program chair for PSIVT 2007 and area chair for CVPR 2005 and ICCV 2007. He is a member of the IEEE.

Ertan Cetingul (PhD BME): fiber tracking, heart motion analysis, diffusion tensor imaging

Rizwan Chaudhry (PhD CS): kernels on dynamical systems and activity recognition

Ehsan Elhamifar (PhD ECE): robust consensus, observability and identification of hybrid systems

Alvina Goh (PhD BME): manifold clustering, registration and segmentation of diffusion weighted images

Avinash Ravichandran (PhD ECE): modeling, estimation and classification of dynamic textures

Dheeraj Singaraju (PhD ECE): image matting and segmentation via discrete optimization, motion segmentation

Roberto Tron (PhD ECE): consensus on manifolds, camera sensor networks, motion segmentartion

If you are interested in joining my lab, please apply directly to the department your are most interested in: Applied Mathematics and Statistics, Biomedical Engineering, Computer Science, Electrical and Computer Engineering, or Mechanical Engineering. Please make sure to mention my name in your statement of purpose. Once you have applied, please send me an e-mail with a subject such as 'PhD Application to BME 2009'.