Martin Raphan Postdoctoral Fellow Howard Hughes Medical Institute New York University Lab for Computational Vision
CV Resume (one page)	email:

Publications:

Least squares estimation without priors or supervision
by M Raphan and E P Simoncelli, Published in Neural Computation, vol. 23(2), pp. 374--420, Feb 2011.
Abstract | PDF

An Empirical Bayesian interpretation and generalization of NL-means
by M Raphan and E P Simoncelli, Computer Science Technical Report Technical Report TR2010-934, October 2010.
Abstract | PDF

Learning least squares estimators without assumed priors or supervision
by M Raphan and E P Simoncelli, Computer Science Technical Report Technical Report TR2009-923, August 2009.
Abstract | PDF

Optimal denoising in redundant representations
by M Raphan and E P Simoncelli, Published in IEEE Trans Image Processing, vol. 17(8), pp. 1342--1352, Aug 2008.
Abstract | PDF

Empirical Bayes least squares estimation without an explicit prior
by M Raphan and E P Simoncelli, Computer Science Technical Report Technical Report TR2007-900, May 2007.
Abstract | PDF

Learning to be Bayesian without supervision
by M Raphan and E P Simoncelli, Published in Adv. Neural Information Processing Systems 19, vol. 19 pp. 1145--1152, May 2007.
Abstract | PDF

Optimal estimation: Prior free methods and physiological application
by Martin Raphan, PhD thesis, Courant Institute of Mathematical Sciences, New York University,
New York, NY, May 2007.
Recipient of the 2008 K. O. Friedrichs prize for an outstanding dissertation in mathematics.
Abstract | PDF

Optimal denoising in redundant bases
by M Raphan and E P Simoncelli, Published in Proc 14th IEEE Int'l Conf on Image Proc, vol.III pp. 113--116, Sep 2007.
Winner, IBM student paper award.
Abstract | PDF

Empirical Bayes Least Squares Estimation without an Explicit Prior
by M Raphan and E P Simoncelli, Published in SIAM Conf. on Imaging Science, May 2006.
Abstract

Presentations:

Prior-Free Methods for Optimal Estimation, Columbia Department of Statistics. (March 2009)

Unsupervised Regression for Image Denoising, Yale Appied Mathematics Seminar. (March 2008)

Unsupervised Regression for Image Processing, MIT Computer Vision Colloquium. (February 2008)

Unsupervised Regression for Image Processing, Harvard School of Engineering and Applied Sciences. (February 2008)

Unsupervised Regression for Image Processing, CUNY Graduate Center Computer Science Colloquium (November 2007)

Unsupervised Regression for Image Processing, Harmonic Analysis and Signal Processing Seminar, Courant Institute. (October 2007)

Prior Free Methods for Image Denoising, Polytechnic University. (October 2007)

Unsupervised Regression with Application to Image Denoising, Google Labs, New York. (September 2007)

Modeling Contrast-Dependent Spatial Summation Area in Visual Cortex, Mount Sinai School of Medicine. (July 2007)

Contrast-Dependent Spatial Summation Area in Visual Cortex: Optimal Filtering and Modeling of Neural Mechanisms, Mostly Biomathematics Lunchtime Seminar, Courant Institute of Mathematical Sciences, New York University. (April 2007)

Empirical Bayes Least Squares Estimation Without an Explicit Prior, SIAM Conference on Imaging Science. (May 2006)