chinmay hegde


  1. Approximation-Tolerant Model-Based Compressive Sensing
    EIS Seminar, Carnegie Mellon University, November 2013.

  2. Approximation-Tolerant Model-Based Compressive Sensing
    CSIP Seminar, Georgia Institute of Technology, October 2013.

  3. A Convex Approach for Designing “Good” Linear Embeddings
    Workshop on Sparse Fourier Transform etc., MIT, February 2013.

  4. Signal Recovery on Incoherent Manifolds
    International Symposium on Information Theory (ISIT), Cambridge MA, July 2012.

  5. Geometric Models for Signal Acquisition and Processing
    University of Wisconsin-Madison, May 2012.

  6. Near-Isometric Linear Embeddings of Manifolds
    KECoM Workshop, The Ohio State University, May 2012.

  7. A Geometric Approach for Compressive Sensing
    Shell Technology Center, Houston TX, April 2012.

  8. Go with the Flow
    Allerton Conference, Monticello IL, September 2011.

  9. Geometric Signal Models for Compressive Sensing
    Mitsubishi Electric Research Labs, Cambridge MA, June 2011.

  10. High-Dimensional Data Fusion via Joint Manifold Learning
    AAAI Fall Symposium on Manifold Learning, Arlington VA, November 2010.

  11. Compressive Sensing of Streams of Pulses
    Allerton Conference, Monticello IL, September 2009.

  12. Structured Sparsity Models for Compressive Sensing
    Signal Processing with Adaptive Sparse Structured Representations (SPARS), Saint Malo, France April 2009.


  1. Near-Isometric Linear Embeddings of Manifolds
    Statistical Signal Processing Workshop (SSP), Ann Arbor MI, August 2012.

  2. Compressive Sensing of a Superposition of Pulses
    International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Dallas TX, March 2010.

  3. Sparse Signal Recovery Using Markov Random Fields
    Neural Information Processing Systems (NIPS), Vancouver BC, December 2008.

  4. Random Projections for Manifold Learning
    IMA Workshop on Multi-Manifold Data Modeling, Minneapolis MN, October 2008.