Vidal Lab

Talks

Conference Talks / Selected Conference Presentations

  • Invited Talk, The 2nd Workshop on Foundation Models in the Wild (ICLR Workshop), Apr 26, 2025 — Invited Talk. [FM-Wild workshop program]
  • Workshop on Video Large Language Models — CVPR 2025 Workshops — June 2025
  • UPenn Optimization Seminar — Jan 25, 2024. René Vidal, UPenn ESE, “Learning Dynamics of Overparametrized Networks.”

Distinguished Lectures and Keynote Speeches

  • Scalable Sparse Subspace Clustering. Plenary Lecture, International MATHEON Conference on “Compressed Sensing and its Applications,” Berlin, Germany, December 2017
  • Global Optimality in Matrix and Tensor Factorization, Deep Learning, and Beyond. Plenary Lecture, Sampling Theory and Applications, Tallinn, Estonia, July 2017
  • Global Optimality in Matrix and Tensor Factorization, Deep Learning, and Beyond. Distinguished Lecture, Data Science Institute, Boston University, April 2017
  • Automatic Methods for the Interpretation of Biomedical Data. Keynote Speech, Industrial Week, Montevideo, Uruguay, November 2016
  • Automatic Methods for the Interpretation of Biomedical Data. Plenary Lecture, Symposium on Computational Methods in Biology and Biomedicine, Santiago, Chile, September 2016
  • Automatic Methods for the Interpretation of Visual Data, Distinguished Lecture, Data Science Institute, Boston University, May 2016
  • Global Optimality in Matrix and Tensor Factorization, Deep Learning, and Beyond. Plenary Lecture, Iberoamerican Congress on Pattern Recognition, Montevideo, Uruguay, November 2015
  • Algebraic, Sparse and Low Rank Subspace Clustering. Plenary Lecture, Symposium on Signal and Image Processing and Computer Vision, Bogotá, Colombia, September 2015
  • Algebraic, Sparse and Low Rank Subspace Clustering. Plenary Lecture, 1st Annual Workshop on Data Sciences, Tennessee State University, April 2015
  • Algebraic, Sparse and Low Rank Subspace Clustering. International Conference on Intelligence Science and Big Data Engineering, Beijing, China, July 2013
  • Subspace Clustering, J.K. Aggarwal Prize Plenary Lecture, International Conference on Pattern Recognition, Tsukuba, Japan, 2012
  • Global Bag of Latent Features Models for Semantic Segmentation, Keynote Speech, ECCV Workshop on Higher-Order Models and Global Constraints in Computer Vision, Florence, Italy, 2012
  • Distributed Algorithms for Camera Sensor Networks, Keynote Speech, CVPR Workshop on Camera Networks and Wide Area Scene Analysis, Colorado Springs, USA, 2011
  • Keynote Speaker at the Symposium for Underrepresented Undergraduates, Johns Hopkins University, 2010
  • Multi-Manifold Learning. AAAI Fall Symposium on Manifold Learning and its Applications, Arlington, VA, November 2010
  • Binet-Cauchy Kernels for the Recognition of Visual Dynamical Processes. Plenary Lecture, Benelux meeting in Systems and Control, Spa, Belgium, March 2009
  • Generalized Principal Component Analysis (GPCA). Keynote Speech, Workshop on Image Processing, Guanajuato, Mexico, August 2007
  • Segmentation of Dynamic Scenes and Textures. Keynote Speech, Workshop on Computational Vision, Robotics, Neurocontrol and Medical Image Processing, Guadalajara, Mexico, June 2006
  • Segmentation of Dynamic Scenes and Textures. Keynote Speech, Workshop on Statistical Methods in Multi-Image and Video Processing (SMVP), May 2006
  • Toward Dynamic GPCA: Hybrid System Identification for the Analysis of Dynamic Scenes. Sundaram Seshu Scholar Lecture, University of Illinois at Urbana Champaign, November 2005

Invited Talks at Workshops, Tutorials and Summer Schools

  • Global Optimality in Matrix and Tensor Factorization, Deep Learning, and Beyond, CoSIP Intense Course on Deep Learning, Berlin, November 2017
  • Dual principal component pursuit. Workshop on Frame Theory and Sparse Representation for Complex Data, Singapore, June 2017
  • Global Optimality in Matrix and Tensor Factorization, Deep Learning, and Beyond, North-American School of Information Theory, Duke University, June 2016
  • Scalable Subspace Clustering, IMA Workshop on Transdisciplinary Foundations of Data Science, September 2016
  • Global Optimality in Matrix and Tensor Factorization, Deep Learning, and Beyond, IMA Workshop on Optimization and Parsimonious Modeling, January 2016
  • Globally Optimal Structured Low-Rank Matrix and Tensor Factorization, ICCV Workshop on Robust Subspace Learning and Computer Vision, December 2015
  • Object Detection, Pose Estimation and Semantic Segmentation Using 3D Wireframe Models, ICCV Workshop on 3D Scene Understanding, December 2015
  • Subspace Arrangements in Vision and Learning, Meeting on Algebraic Vision, October 2015
  • Algebraic, Sparse and Low Rank Subspace Clustering, International Computer Vision Summer School, July 2015
  • Globally Optimal Factorizations and Deep Learning, Symposium on Data Science, ShanghaiTech, June 2015
  • Discovering the Language of Surgery, CVPR Workshop on Medical Computer Vision, June 2015
  • Globally Optimal Factorizations and Deep Learning, Conference on Computational Imaging and Vision, KAUST, March 2015
  • Semantic (less) Motion and Video Segmentation, First International Workshop on Video Segmentation, ECCV, September 2014
  • Algebraic, Sparse and Low Rank Subspace Clustering. Mathematical Image Analysis, Paris, January 2014
  • Computer Vision Methods in Surgery and Neuroimaging. 3rd Annual Hopkins Imaging Conference, Johns Hopkins University, November 2013
  • See All by Looking at A Few: Sparse Modeling for Finding Data Exemplars. Computer Vision Workshop, Oxford University, August 2013
  • See All by Looking at A Few: Sparse Modeling for Finding Data Exemplars. Duke Workshop on Sensing and Analysis of High-Dimensional Data, July 2013
  • Discovering the Language of Surgery. MICCAI Workshop, Tokyo University, May 2013
  • Discovering the Language of Surgery. Computer Vision Workshop, University of Southern California, February 2013
  • Subspace Sparsity for Classification and Clustering of High-Dimensional Data. International Workshop on Computer Vision, Siracusa, Italy, May 2012
  • Sparsity and Rank Minimization in Unions of Subspaces. Workshop on Sensing and Analysis of High-Dimensional Data (SAHD), Duke University, July 2011
  • Structured Sparsity for Subspace Classification and Clustering. Workshop on the Geometry of High Dimensional Data, Department of Mathematics, Vanderbilt University, May 2011
  • Segmentation and Categorization of Dynamic Scenes. Cotesys Spring Workshop, Technical University of Munich, April 2011
  • Processing High Angular Resolution Diffusion Images of the Brain. Workshop on What Can Computer Vision Do for Neuroscience and Vice Versa? Janelia Farm Campus, Howard Hughes Medical Institute
  • An Algebraic Geometric Approach to Hybrid System Identification, Workshop on Identification of Hybrid Systems, IEEE CDC, Seville, Spain, December 2005
  • Generalized Principal Component Analysis (GPCA), Machine Learning Summer School, Canberra, Australia, January 2005
  • Generalized Principal Component Analysis (GPCA). Catholic University of Chile, December 2004
  • Segmentation of Dynamic Scenes via Generalized Principal Component Analysis, Workshop on Mathematics and Image Analysis, Paris, France, September 2004
  • Reconstruction of Dynamic Scenes, Workshop on Imaging Beyond the Pinhole Camera, Dagstuhl, Germany, June 2004
  • Tutorial on Breakthroughs in 3D Reconstruction and Motion Analysis, IEEE ICRA, New Orleans, May 2003
  • — Invited Talks at Departmental Seminars (selected entries from CV) —
  • (Note: the CV lists many departmental seminars; below are representative entries — full departmental list is in the cited CV)
  • Global Optimality in Matrix and Tensor Factorization, Deep Learning, and Beyond, Seminar, Université de La Rochelle, December 2017
  • An Analysis of Dropout for Matrix Factorization, Seminar, Centre for Mathematics and its Applications, Université Paris-Saclay, December 2017
  • Applications of Structured Low-Rank Matrix Factorization to Image Processing, Seminar, Centre for Mathematics and its Applications, Université Paris-Saclay, December 2017
  • Global Optimality in Matrix and Tensor Factorization, Deep Learning, and Beyond, Seminar, IMT Lille-Douai, November 2017
  • Global Optimality in Matrix and Tensor Factorization, Deep Learning, and Beyond, Seminar, Dept. of Electrical Engineering and Computer Science, University of Michigan, November 2017
  • Global Optimality in Matrix and Tensor Factorization, Deep Learning, and Beyond, Seminar, (multiple departmental seminars through 2017 listed in CV)
  • Generalized Principal Component Analysis (GPCA). University of Pennsylvania, March 2004
  • Generalized Principal Component Analysis (GPCA). Carnegie Mellon University, February 2004
  • Generalized Principal Component Analysis (GPCA). UC San Diego, April 2003
  • Generalized Principal Component Analysis (GPCA): Caltech, Nov 2002
  • Segmentation of Dynamic Scenes. University of Illinois Urbana-Champaign, Oct 2002
  • The Multiple View Matrix, University of Pennsylvania, August 2001