This section tends to lag in time! Please see CV for current list.
Theses
HighlySelective 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. 1315).
[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. 710).
[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. 710).
[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 2627), pp. 251258.
[Acceptance rate oral presentation: 25/145=17%.]
Preprints
 A. K. Fletcher and S. Rangan,
Iterative Recontruction of Constrained RankOne Matrices in Noise, arXiv:1202.2759, Dec. 2012.
Submitted to "Information and Inference, a Journal of the IMA", Sep. 2015.
 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. Submitted to "IEEE Transactions on Information Theory", Aug. 2015.
 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, Oct. 2014.
 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,
OnOff Random Access Channels: A Compressed Sensing Framework,
arXiv:0903.1022, March 2009.
Journal Papers
 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):59195931, Nov. 2012.
 A. K. Fletcher and S. Rangan,
Orthogonal Matching Pursuit: A Brownian Motion Analysis,
IEEE Trans. Signal Processing, 60(3):10101021, 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):19021923, March 2012.
 A. K. Fletcher, S. Rangan, and V. K. Goyal,
Necessary and Sufficient Conditions on Sparsity Recovery,
IEEE Trans. Information Theory, 55(12):57585772, Dec. 2009.
 V. K. Goyal, A. K. Fletcher, and S. Rangan,
Compressive Sampling and Lossy Compression,
IEEE Signal Processing Magazine, 25(2):4856, 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):618632, Dec. 2007.
 A. K. Fletcher, S. Rangan, V. K. Goyal, and K. Ramchandran,
Denoising by Sparse Approximation: Error Bounds Based on RateDistortion Theory,
EURASIP J. Applied Signal Processing,
Special Issue on Frames and Overcomplete Representations,
vol. 2006, March 2006.
Other PeerReviewed Conferences, Symposia, and Workshops
 A. K. Fletcher,. S. Rangan and J. Viventi,
Neural Mass SpatioTemporal Modeling from HighDensity
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 MessagePassing 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 29July 4).
 A. K. Fletcher,
Bayesian Inference of Neural Connectivity,
Proc. Comput. Syst. Neurosci. (Cosyne) 2014 (Snowbird, UT, Feb. 27March 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 RankOne 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 1218),
pp. 15431547.
 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 OnOff Random Access Channels,
Proc. IEEE Int. Symp. Information Theory 2009
(Seoul, South Korea, June 28July 3), pp. 169173.
 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 611),
pp. 19821986.
 A. K. Fletcher, S. Rangan, and V. K. Goyal,
RateDistortion Bounds
for Sparse Approximation,
Proc. IEEE Workshop on Statistical Signal Processing 2007
(Madison, WI, Aug. 2629), pp. 254258.
 A. K. Fletcher, S. Rangan, and V. K. Goyal,
On the RateDistortion
Performance of Compressed Sensing,
Proc. IEEE Int. Conf. Acoustics, Speech, & Signal Processing 2007
(Honolulu, HI, April 1520), vol. III, pp. 885888.
 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 2224), pp. 13021307.
 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. 49), pp. 17061710.
 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 31Aug. 4), vol. 5914, pp. 172181.
 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. 2427), vol. 5, pp. 32453248.
 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 27July 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. 1417), vol. 1, pp. 6164.
 A. K. Fletcher and K. Ramchandran,
Estimation Error Bounds
for Denoising by Sparse Approximation,
Proc. IEEE Int. Conf. Image Processing 2003
(Barcelona, Spain, Sept. 1417), vol. 1, pp. 113116.
 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. 38), vol. 5207, pp. 915.
 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. 38), vol. 5207, pp. 4046.
 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. 2225), vol. 2, pp. 873876.
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.

Coorganizer, International Workshop on HighDimensional Statistical Inference in the Brain,
Neural Information Process. Symp. (NIPS), 2013 (Lake Tahoe, NV, Dec 510).

“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 HighDimensional Inference: Analysis and Applications,” University of California,
Davis Department of Electrical and Computer Engineering, May, 2011.

“Algorithms for HighDimensional Inference: Analysis and Applications,” University of Massachusetts
Department of Electrical and Computer Engineering, April 20, 2011.

“Algorithms for HighDimensional 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 HighDimensional 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.

“RateDistortion Performance of SparseSignal 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.
