René Vidal, PhD

Professor of Biomedical Engineering,
Computer Science, Mechanical Engineering, and
Electrical and Computer Engineering

302B Clark Hall
3400 N Charles St.
Baltimore MD 21218, USA

Phone: 410-516-7306
Fax: 410-516-4557
E-mail: rvidal at jhu dot edu
About me
I am a professor in the Center for Imaging Science (CIS) and the Department of Biomedical Engineering at The Johns Hopkins University, with secondary appointments in Computer Science, Electrical and Computer Engineering, and Mechanical Engineering. I am also a faculty member in the Institute for Computational Medicine (ICM) and the Laboratory for Computational Sensing and Robotics (LCSR). My research areas are machine learning, computer vision, biomedical image analysis, dynamical systems, robotics and signal processing. I am particularly interested in the development of mathematical methods for the interpretation of high-dimensional data, such as images, videos, and biomedical data. In particular, I have developed methods from algebraic geometry, sparse and low-rank representation theory for clustering and classification of high-dimensional data, and methods from dynamical systems theory for modeling and comparison of time series data. Applications include motion segmentation, dynamic texture classification, object and activity recognition in images and videos, surgical skill and gesture recognition in kinematic and video, segmentation and registration of brain images, and classification of cardiac myocytes.
Research Interests
  • Machine learning: mathematics of deep learning, subspace clustering, sparse and low-rank representation, manifold learning and clustering, matrix factorization, time series classification, GPCA, kernel GPCA, dynamic GPCA
  • Computer vision: 3D scene analysis, activity recognition, semantic segmentation of images and videos, dynamic texture segmentation and recognition, 3D motion segmentation, camera sensor networks, non-rigid shape and motion analysis, structure from motion and multiple view geometry, omnidirectional vision
  • Biomedical image analysis: gesture and skill recognition in robotic surgery, analysis of high angular resolution diffusion images (HARDI), classification of stem cell derived cardiac myocites, interactive medical image segmentation segmentation and fiber tracking in cardiac MRI, interactive medical image segmentation, heart motion analysis
  • Dynamical systems: observability, identification, realization, metrics and topology for hybrid systems
  • Robotics: gesture and skill recognition in robotic surgery, 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.
  • Recent Talks
  • Global Optimality in Structured Matrix Factorization, Invited talk, ICCV Workshop on Robust Subspace Learning and Computer Vision, Santiago de Chile, 2015.
  • Algebraic, Sparse and Low Rank Subspace Clustering, Tutorial on Subspace Learning, CVPR June 2015
  • Globally Optimal Factorizations, Deep Learning and Beyond, KAUST March 2015, MACV April 2015, SSDS June 2015
  • Bio
    Professor Vidal received his B.S. degree in Electrical Engineering (valedictorian) 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 2003 and has been a faculty member in the Department of Biomedical Engineering and the Center for Imaging Science of The Johns Hopkins University since 2004. He has held several visiting faculty positions at Stanford, INRIA/ENS Paris, the Catholic University of Chile, Universite Henri Poincare, and the Australian National University. Dr. Vidal is co-author of the book ``Generalized Principal Component Analysis" (2016), co-editor of the book ``Dynamical Vision" and co-author of over 200 articles in machine learning, computer vision, biomedical image analysis, hybrid systems, robotics and signal processing. Dr. Vidal is or has been Associate Editor of Medical Image Analysis, the IEEE Transactions on Pattern Analysis and Machine Intelligence, the SIAM Journal on Imaging Sciences, Computer Vision and Image Understanding, and the Journal of Mathematical Imaging and Vision, and guest editor of the International Journal on Computer Vision and Signal Processing Magazine. He is or has been program chair for ICCV 2015, CVPR 2014, WMVC 2009 and PSIVT 2007. He was area chair for AAAI 2016, NIPS 2015, MICCAI 2013 and 2014, ICCV 2007, 2011, 2013 and 2017, and CVPR 2005, 2013 and 2017. Dr. Vidal is recipient of numerous awards for his work, including the 2012 J.K. Aggarwal Prize for ``outstanding contributions to generalized principal component analysis (GPCA) and subspace clustering in computer vision and pattern recognition", the 2012 Best Paper Award in Medical Robotics and Computer Assisted Interventions (with Benjamin Bejar and Luca Zappella), the 2011 Best Paper Award Finalist at the Conference on Decision and Control (with Roberto Tron and Bijan Afsari), the 2009 ONR Young Investigator Award, the 2009 Sloan Research Fellowship, the 2005 NFS CAREER Award and the 2004 Best Paper Award Honorable Mention (with Prof. Yi Ma) 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 is a fellow of the IEEE (2014), fellow of the IAPR (2016), and a member of the ACM and SIAM.

    Complete CV.

    Current Research Scientists and PostDocs
  • Benjamin Haeffele (Associate Research Scientist, 2015-present): cell reconstruction, detection, classification and tracking
  • Guilherme Franca (postdoc, 2016-present): optimization for machine learning
  • Haider Ali (Associate Research Scientist, 2017-present): 3D scene analysis and activity recognition
  • Benjamin Bejar (Associate Research Scientist, 2017-present): cell detection, classification
  • Current Graduate Students
  • Chong You (PhD ECE): sparse subspace clustering
  • Evan Schwab (PhD ECE): analysis of diffusion MRI data
  • Siddharth Mahendran (PhD ECE): 3D object modeling and semantic segmentation
  • Giann Gorospe (PhD BME): classification of cardiac myocites, computational anatomy
  • Efi Mavroudi (PhD BME): activity recognition
  • Florence Yellin (PhD ME): convolutional sparse coding and dictionary learning for cell classification
  • Alumni
  • Lingling Tao (PhD ECE, now Research Scientist at Oculus VR): activity segmentation and classification
  • Manolis Tsakiris (PhD ECE 2017, now Assistant Professor at ShanghaiTech): algebraic subspace clustering and sparse coding on the sphere
  • Colin Lea (PhD CS 2017, now Research Scientists at Oculus Research): fine-grained action recognition (coadvised with Greg Hager and Austin Reiter)
  • Bijan Afsari (postdoc 2010-2014, then Research Scientist 2014-2016): averaging on Riemannian manifolds, metrics on dynamical systems, activity recognition
  • Shahin Sefati (postdoc 2015, now Senior Researcher at Comcast): dynamic sparse coding and dictionary learning
  • Benjamin Haeffele (PhD BME 2015, then Associate Research Scientist at JHU): structured matrix factorization and globally optimal deep learning
  • Benjamin Bejar (MSc BME 2013, then postdoc at EPFL, then Associate Research Scientist at JHU): language of surgery
  • Erdem Yoruk (post-doc 2012-2013, now Chief Scientist at Vispera Information Technologies): modeling and inference for visual recognition
  • Luca Zappella (post-doc 2011-2013, then Senior Research Engineer at Metaio, now R&D Engineer at Apple): language or surgery, motion segmentation
  • Aastha Jain (post-doc 2012, now Senior Data Scientist at Linkedin): joint segmentation and categorization of objects in images and videos
  • Roberto Tron (PhD ECE 2012, then postdoc at Upenn, now Assistant Professor at Boston University): consensus on manifolds, localization of camera sensor networks, motion segmentation
  • Rizwan Chaudhry (PhD CS 2012, then Software Engineer at Microsoft and Nest-Google): kernels on dynamical systems and activity recognition
  • Ehsan Elhamifar (PhD ECE, 2012, then postdoc at UC Berkeley, now Assistant Professor at Northeastern University): sparse subspace clustering, block-sparse classification, manifold clustering, robust consensus, observability and identification of hybrid systems
  • Ertan Cetingul (PhD BME 2011, then Research Scientist at Siemens Corporate Research, now Research Program Manager at GE): fiber tracking, heart motion analysis, processing, segmentation and registration of diffusion weighted images
  • Diego Rother (post-doc 2009-2011, now Software Engineer at Google): object segmentation, reconstruction and recognition using 3D shape priors
  • Avinash Ravichandran (PhD ECE 2010, then postdoc at UCLA, now Research Scientist at Amazon): registration, segmentation and recognition of dynamic textures
  • Dheeraj Singaraju (PhD ECE 2010, then postdoc at UC Berkeley, now Software Engineer at Google): discrete optimization, object recognition and segmentation, image matting and segmentation, 2D motion segmentation
  • Alvina Goh (PhD BME 2010, then Lab Head DSO National Laboratories Singapore, now Lead Computational Scientist at GovTech Singapore): estimation and processing of diffusion weighted images, manifold clustering
  • Mihaly Petreczky: (post-doc 2007-2008, then Assiatant Professor at CWI Netherlands and Ecole des Mines de Douai, now Research Scientist at CNRS) realization theory for hybrid systems
  • Prospective Students
    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'.