chinmay hegde

talks

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.

posters

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.