### Highly-Selective Conferences

- A. K. Fletcher and S. Rangan, Scalable Bayesian Inference of Neuronal Connectivity*, Proc. 28th Ann. Conf. Neural Information Processing Systems 2014. [Acceptance rate: 25%, oral spotlight: top 4.9%]
- U. S. Kamilov, S. Rangan, A. K. Fletcher and M. Unser, Approximate Message Passing with Consistent Parameter Estimation and Applications to Sparse Learning, Proc. 26th Ann. Conf. Neural Information Processing Systems 2012. [Acceptance rate: 25%.]
- A. K. Fletcher, S. Rangan, L. Varshney, A. Bhargava, Neural Reconstruction with Approximate Message Passing (NeuRAMP), Proc. 25th Ann. Conf. Neural Information Processing Systems 2011 (Granada, Spain, Dec. 13-15). [Acceptance rate: 305/1400 = 22%.]
- A. K. Fletcher and S. Rangan, Orthogonal Matching Pursuit from Noisy Random Measurements: A New Analysis, Proc. 23rd Ann. Conf. Neural Information Processing Systems 2009 (Vancouver, Canada, Dec. 7-10). [Acceptance rate: 24%. Spotlight paper, top 8%.]
- S. Rangan, A. K. Fletcher, and V. K. Goyal, Asymptotic Analysis of MAP Estimation via the Replica Method and Compressed Sensing, Proc. 23rd Ann. Conf. Neural Information Processing Systems 2009 (Vancouver, Canada, Dec. 7-10). [Acceptance rate: 24%. Spotlight paper, top 8%.]
- A. K. Fletcher, S. Rangan, and V. K. Goyal, Resolution Limits of Sparse Coding in High Dimensions, Proc. 22nd Ann. Conf. Neural Information Processing Systems 2008. [Acceptance rate: 250/1022 = 24%.]
- A. K. Fletcher, S. Rangan, and V. K. Goyal, Estimation from Lossy Sensor Data: Jump Linear Modeling and LMI Analysis, Proc. ACM/IEEE Int. Conf. Information Processing in Sensor Networks 2004 (Berkeley, CA, April 26-27), pp. 251-258. [Acceptance rate oral presentation: 25/145=17%.]

### Preprints

- S. Rangan, P. Schniter, E. Riegler, A. K. Fletcher, V. Cevher, Fixed Points of Generalized Approximate Message Passing with Arbitrary Matrices, arXiv:1301.6295, Jan. 2013.
- S. Rangan and A. K. Fletcher, Iterative Estimation of Constrained Rank-One Matrices in Noise, arXiv:1202.2759, Dec. 2012.
- S. Rangan, A. K. Fletcher, V. K.Goyal and P. Schniter, Hybrid Approximate Message Passing with Applications to Structured Sparsity, arXiv:1111.2581, Nov. 2011.
- A. K. Fletcher, S. Rangan, and V. K. Goyal, On-Off Random Access Channels: A Compressed Sensing Framework, arXiv:0903.1022, March 2009.

### Journal Papers

- S. Rangan, A. K. Fletcher, P. Schniter, and U. Kamilov, Inference for Generalized Linear Models via Alternating Directions and Bethe Free Energy Minimization, submitted for publication, IEEE Trans. Information Theory, 2015.
- U. S. Kamilov, S. Rangan, A. K. Fletcher and M. Unser, Approximate Message Passing with Consistent Parameter Estimation and Applications to Sparse Learning", IEEE Trans. Information Theory, 2014.
- A. K. Fletcher, V. K. Goyal and S. Rangan, Ranked Sparse Signal Support Detection, IEEE Trans. Signal Processing, 60(11):5919-5931, Nov. 2012.
- A. K. Fletcher and S. Rangan, Orthogonal Matching Pursuit: A Brownian Motion Analysis, IEEE Trans. Signal Processing, 60(3):1010-1021, March 2012.
- S. Rangan, A. K. Fletcher, and V. K. Goyal, Asymptotic Analysis of MAP Estimation via the Replica Method and Applications to Compressed Sensing, IEEE Trans. Information Theory, 58(3):1902-1923, March 2012.
- A. K. Fletcher, S. Rangan, and V. K. Goyal, Necessary and Sufficient Conditions on Sparsity Recovery, IEEE Trans. Information Theory, 55(12):5758-5772, Dec. 2009.
- V. K. Goyal, A. K. Fletcher, and S. Rangan, Compressive Sampling and Lossy Compression, IEEE Signal Processing Magazine, 25(2):48-56, March 2008.
- V. K. Goyal, A. K. Fletcher, and S. Rangan, Distributed Coding of Sparse Signals, chapter in Distributed Source Coding: Theory, Algorithms, and Applications, P. L. Dragotti and M. Gastpar eds., Academic Press, 2009.
- A. K. Fletcher, S. Rangan, V. K. Goyal, and K. Ramchandran, Robust Predictive Quantization: Analysis and Design via Convex Optimization, IEEE J. Selected Topics in Signal Processing, 1(4):618-632, Dec. 2007.
- A. K. Fletcher, S. Rangan, V. K. Goyal, and K. Ramchandran, Denoising by Sparse Approximation: Error Bounds Based on Rate-Distortion Theory, EURASIP J. Applied Signal Processing, Special Issue on Frames and Overcomplete Representations, vol. 2006, March 2006.

### Other Peer-Reviewed Conferences, Symposia, and Workshops

- A. K. Fletcher,. S. Rangan and J. Viventi, Neural Mass Spatio-Temporal Modeling from High-Density Electrode Array Recordings, Proc. IEEE Information Theory and Applications, San Diego, CA, Feb. 2015
- P. Schniter, S. Rangan, and A. K. Fletcher, "Statistical Image Recovery: A Message-Passing Perspective,” Int. Biomedical & Astronomical Signal Process. Frontiers Workshop (Villars sur- Ollon, Switzerland, January 25–30, 2015.
- S. Rangan, A. K. Fletcher, P. Schniter, On the Convergence of Approximate Message Passing for Arbitrary Matrices, Proc. IEEE Int. Symp. Information Theory 2014 (Honolulu, HI, June 29-July 4).
- A. K. Fletcher, Bayesian Inference of Neural Connectivity, Proc. Comput. Syst. Neurosci. (Cosyne) 2014 (Snowbird, UT, Feb. 27-March 2).
- A. K. Fletcher, S. Rangan, Hybrid Approximate Message Passing for Structured Group Sparsity, Proc. Wavelets and Sparsity SPIE workshop, San Diego, Aug. 2013.
- S. Rangan, P. Schniter, E. Riegler, A. K. Fletcher, V. Cevher, Fixed Points of Generalized Approximate Message Passing with Arbitrary Matrices, Proc. IEEE Int. Symp. Information Theory (ISIT) (Istanbul, Turkey), July 2013.
- A. K. Fletcher and S. Rangan, Iterative Estimation of Constrained Rank-One Matrices in Noise, Proc. IEEE Int. Symp. Information Theory (Cambridge, MA), July 2012.
- S. Rangan, A. K. Fletcher, V. K. Goyal, and P. Schniter, Hybrid Generalized Approximate Message Passing with Applications to Structured Sparsity, Proc. IEEE Int. Symp. Information Theory (Cambridge, MA), July 2012.
- S. Rangan, A. K. Fletcher, and V. K. Goyal, Extensions of Replica Analysis to MAP Estimation with Applications to Compressed Sensing, Proc. IEEE Int. Symp. Information Theory 2010 (Austin, TX, June 12-18), pp. 1543-1547.
- A. K. Fletcher, S. Rangan, and V. K. Goyal,
__Random Access Channels: A Compressed Sensing Framework__Proc. Wavelets XIII, SPIE Optics & Photonics 2009. (invited) - A. K. Fletcher, S. Rangan and V. K. Goyal, A Sparsity Detection Framework for On-Off Random Access Channels, Proc. IEEE Int. Symp. Information Theory 2009 (Seoul, South Korea, June 28-July 3), pp. 169-173.
- A. K. Fletcher, S. Rangan, and V. K. Goyal, On Subspace Structure in Source and Channel Coding, Proc. IEEE Int. Symp. Information Theory 2008 (Toronto, Canada, July 6-11), pp. 1982-1986.
- A. K. Fletcher, S. Rangan, and V. K. Goyal, Rate-Distortion Bounds for Sparse Approximation, Proc. IEEE Workshop on Statistical Signal Processing 2007 (Madison, WI, Aug. 26-29), pp. 254-258.
- A. K. Fletcher, S. Rangan, and V. K. Goyal, On the Rate-Distortion Performance of Compressed Sensing, Proc. IEEE Int. Conf. Acoustics, Speech, & Signal Processing 2007 (Honolulu, HI, April 15-20), vol. III, pp. 885-888.
- A. K. Fletcher, S. Rangan, V. K. Goyal, and K. Ramchandran, Causal and Strictly Causal Estimation for Jump Linear Systems: An LMI Analysis, Proc. Conf. Information Sciences & Systems 2006 (Princeton, NJ, March 22-24), pp. 1302-1307.
- A. K. Fletcher, S. Rangan, V. K. Goyal, and K. Ramchandran, Analysis of Denoising by Sparse Approximation with Random Frame Asymptotics, Proc. IEEE Int. Symp. on Information Theory 2005 (Adelaide, Sept. 4-9), pp. 1706-1710.
- A. K. Fletcher, S. Rangan, and V. K. Goyal, Sparse Approximation, Denoising, and Large Random Frames, Proc. Wavelets XI, part of SPIE Optics & Photonics 2005 (San Diego, CA, July 31-Aug. 4), vol. 5914, pp. 172-181.
- A. K. Fletcher, S. Rangan, V. K. Goyal, and K. Ramchandran Optimized Filtering and Reconstruction in Predictive Quantization with Losses, Proc. IEEE Int. Conf. Image Processing 2004 (Singapore, Oct. 24-27), vol. 5, pp. 3245-3248.
- A. K. Fletcher, S. Rangan, V. K. Goyal, and K. Ramchandran, Robust Predictive Quantization: A New Design and Optimization Methodology, Proc. IEEE Int. Symp. Information Theory 2004 (Chicago, IL, June 27-July 2), p. 427.
- A. K. Fletcher, V. K. Goyal, and K. Ramchandran, On Multivariate Estimation by Thresholding, Proc. IEEE Int. Conf. Image Processing 2003 (Barcelona, Spain, Sept. 14-17), vol. 1, pp. 61-64.
- A. K. Fletcher and K. Ramchandran, Estimation Error Bounds for Denoising by Sparse Approximation, Proc. IEEE Int. Conf. Image Processing 2003 (Barcelona, Spain, Sept. 14-17), vol. 1, pp. 113-116.
- A. K. Fletcher, V. K. Goyal, and K. Ramchandran, Iterative Projective Wavelet Methods for Denoising, Proc. Wavelets X: Applications in Signal & Image Processing, part of SPIE Int. Symp. on Optical Science & Technology 2003 (San Diego, CA, Aug. 3-8), vol. 5207, pp. 9-15.
- A. K. Fletcher and K. Ramchandran, Estimation Error Bounds for Frame Denoising, Proc. Wavelets X: Applications in Signal & Image Processing, part of SPIE Int. Symp. on Optical Science & Technology 2003 (San Diego, CA, Aug. 3-8), vol. 5207, pp. 40-46.
- A. K. Fletcher, K. Ramchandran, and V. K. Goyal, Wavelet Denoising by Recursive Cycle Spinning, Proc. IEEE Int. Conf. Image Processing 2002 (Rochester, NY, Sept. 22-25), vol. 2, pp. 873-876.

### Selected Invited Talks and Workshops

- “Scalable Identification for Structured Nonlinear Neural Systems,” Redwood Center for Theoretical Neuroscience Seminar, University of California, Berkeley, May 7, 2014.
- “Scalable Identification for Structured Nonlinear Neural Systems,” ISL Big Data Seminar Series, Stanford University, April 10, 2014.
- “Bayesian Inference of Sparse Neural Dynamical Systems,” Information Theory and Applications Workshop, University of California, San Diego, February 13, 2014.
- “Scalable Identification for Structured Nonlinear Neural Systems,” University of California, Berkeley, Control Theory Seminar, February 3, 2014.
- “Scalable Identification for Structured Complex Nonlinear Systems,” University of California, San Diego, ECE Seminar, January 30, 2014.
- Co-organizer, International Workshop on High-Dimensional Statistical Inference in the Brain, Neural Information Process. Symp. (NIPS), 2013 (Lake Tahoe, NV, Dec 5-10).
- “Learning Sparse Priors in Approximate Message Passing,” Information Theory and Applications Workshop, University of California, San Diego, February 13, 2013.
- “Neural Connectivity and Receptive Field Estimation via Hybrid Message Passing,” Information Theory and Applications Workshop, University of California, San Diego, February 6, 2012.
- “Neural Connectivity and Receptive Field Estimation via Hybrid Message Passing,” Mathematical EE Seminar, University of California, Santa Cruz. January 27, 2012.
- ”Sparsity: Algorithms and Applications in Neuroscience,” Applied Mathematics and Mathematical Biology Seminar, Claremont Graduate University, January 25, 2012.
- “Exploiting Sparsity: Algorithms and Applications,” Electrical and Computer Engineering Seminar, University of California, Davis, November 14, 2011.
- “Generalized Approximate Message Passing and Applications in Neural Receptive Field Estimation and Connectomics,” Redwood Center for Theoretical Neuroscience, University of California, Berkleley, June 8, 2011.
- “Algorithms for High-Dimensional Inference: Analysis and Applications,” University of California, Davis Department of Electrical and Computer Engineering, May, 2011.
- “Algorithms for High-Dimensional Inference: Analysis and Applications,” University of Massachusetts Department of Electrical and Computer Engineering, April 20, 2011.
- “Algorithms for High-Dimensional Inference: Analysis and Applications,” University of Florida Department of Electrical and Computer Engineering, April 20, 2011.
- “Compressed Sensing to the Limits: Bounds, Algorithms, andWireless Applications,” University of Michigan Electrical Engineering and Computer Science Seminar, March 31, 2009.
- “Sparsity Recovery: Limits, Algorithms and Wireless Applications,” DIMACS/DyDAn Working Group on Streaming, Coding, and Compressive Sensing: Unifying Theory and Common Applications to Sparse Signal/Data Analysis and Processing, New Brunswick, NJ, March 25– 26, 2009. (By invitation only workshop speaker and participant.)
- “Sparsity Pattern Recovery: Precisely Contrasting Thesholding, Lasso, and Maximum Likelihood,” University of California at San Diego Information Theory and Applications Workshop, February 8–13, 2009.
- “Random Access Channels and Sparsity Detection,” University of California at San Diego Information Theory and Applications Workshop, February 8–13, 2009.
- American Institute of MathematicsWorkshop on Frames for the FiniteWorld: Sampling, Coding, and Quantization, August 18–22, 2008 (invited participant).
- Banff International Research Station Workshop on Mentoring for Engineering Academia II, July 22–27, 2007 (invited participant), Banff, Alberta, Canada.
- “Compressed Sensing as a Source Coding Technique,” 2007 von Neumann Symposium on Sparse Representation and High-Dimensional Geometry, July 8–12, 2007, Snowbird, UT.
- “On Encoding with a Codebook of Subspaces,” University of California at San Diego Information Theory and Applications Workshop, January 29, 2007.
- “Rate-Distortion Performance of Sparse-Signal Coding with Random Measurements,” SIAM Conference on Imaging Science, May 15, 2006, Minneapolis, MN.
- University of California at San Diego Workshop on Information Theory and Its Applications, February 6–10, 2006 (invited participant).
- “Estimation and Robust Communication of Signals with Markovian Losses,” ´ Ecole Polytechnique F´ed´erale de Lausanne, Computer and Communication Sciences Department, July 14, 2005, Lausanne, Switzerland.
- “Estimation with Markovian Dynamics and Sparseness,” University of California, Berkeley, Networking/Communication/DSP Seminar, April 20, 2005, Berkeley, CA.
- UCLA Institute for Pure and Applied Mathematics (IPAM) Program on Multiscale Geometry and Analysis in High Dimensions, Fall 2004.
- PAESMEM/Stanford School of EngineeringWorkshop on Mentoring in Engineering, June 21– 22, 2004.
- “Sparseness from Redundancy: Denoising Methods and Bounds,” University of Cambridge, Department of Engineering, Signal Processing Seminar, October 2, 2003, Cambridge, UK.
- “Wavelet Denoising by Recursive Cycle Spinning,” DIMACSWorkshop on Source Coding and Harmonic Analysis, May 9, 2003, New Brunswick, NJ.