Presentations

October 25 2004 : The Input function for PET

Presentation on ASU CBS PSM Program at Aspen Rocky Mountain Bioinformatics Conference, December 2004

Presentation at Aspen Rocky Mountain Bioinformatics Conference, December 2004 The input Function of PET.

The ASU CBS program : Southwest Meeting Jan 2005

Abstract for the MAA meeting, April 1, 2005, University of Texas El Paso:  Presentation

Presentation at ASU Computer Science Department , April 15, 2005

Interface 2005 :Robust Clustering of PET data (Prasanna K Velamuru)

Poster Presented at the SIAM Conference on Mathematical and Computational Issues in the Geosciences 2005.

Automated Characterization of cellular migration phenomena : 2005 IEEE Computer Society Bioinformatics Conference (CSB2005).

Modifying a Linear Support Vector Machine for Microarray Data Classification, Householder Linear Algebra Meeting, May 2005

Challenges in Quantification of PET images : registration and restoration techniques  IMA workshop: Frontiers in Imaging, Nov 2005

ASU CBS program: A presentation at the USWCL AZ Dec 2005

The Computational Biosciences Program: February 24, 2006, Georgia State University  A discussion of the Professional Masters program and developments for the doctoral degree.

Iteratively Regularized Gauss-Newton with Variable Weighting
Copper Mountain Conference on Iterative Methods, 2006

Information Extraction from PET Images
SIAM Imaging Conference, May 2006, Minisymposium Presentation.

Signal restoration through deconvolution applied to deep mantle seismic probes
Colloquium ASU Sensip Series, March 2006

Ill-posed Inverse Problems: Algorithms and Applications
San Diego State University, Colloquium

Challenges for PET image Processing
Georgia State University, Atlanta, Colloquium, March 30, 2007.

Regularization Parameter Estimation for Least Squares
June 2007. Inverse Problems : Vancouver

Challenges in Improved Sensitivity of Quantification of PET Data for Alzheimer’s Disease Studies
July 2007 MTBI Presentation ASU

Towards Improved Sensitivity in Feature Extraction from Signals: one and two dimensional
Fourth International Conference of Applied Mathematics and Computing, August 2007 Bulgaria,1 hour presentation.

Regularization Parameter Estimation for Least Squares: Using the chi^2-curve
August 2007 Computational Methods with Applications, International Meeting, Harrachov, Czech Republic.

Regularization Parameter Estimation for Least Squares: Using the chi^2-curve
September 2007 Czech-France-Germany Optimization Meeting Heidelberg, Germany

Regularization Parameter Estimation for Least Squares: Using the chi^2-curve
Sep 2007 Numerical Linear Algebra and Optimization, IMA Meeting, Birmingham, England.

Determining the Regularization Parameters for the Solution of Ill-posed Least Squares: A Newton method using the $\chi^2$-distribution
October 2007 Computational and Applied mathematics Seminar, ASU/Temple

Postprocessing of nonuniform MRI Data
October 2007 Minisymposium organizer and presenter, SIAM Conference on Industry.

Determining the Regularization Parameters for the Solution of Ill-posed Least Squares: A Newton method using the $\chi^2$-distribution
December 2007 Colloquim, Boise State University

Newton’s Method for Estimating the Regularization Parameter for Least Squares: Using the Chi-curve
April 2008 Copper Mountain Conference on Iterative Methods.

Newton’s method for obtaining the Regularization Parameter for Regularized Least Squares
June 2008 International Linear Algebra Conference, Cancun, Mexico.

Improving the Efficiency of the Chi-squared Method for Regularization Parameter Estimation
July 2008 SIAM Annual Meeting

The Chi-squared Distribution of the Regularized Least Squares Functional for Regularization Parameter Estimation
September 2008 Colloquium, Dept. of Informatics and Mathematical Modelling, Technical Univ. of Denmark.

The Chi-squared Distribution of the Regularized Least Squares Functional for Regularization Parameter Estimation
September 2008 Colloquium, Academy of Sciences of the Czech Republic Institute of Computer Science, Prague.

The Chi-squared Distribution of the Regularized Least Squares Functional for Regularization Parameter Estimation
September 2008 Gamm Workshop on Applied and Numerical Linear Algebra, Hamburg

Statistical Properties of the Regularized Least Squares Functional and a hybrid LSQR Newton method for Finding the Regularization Parameter: Application in Image Deblurring and Signal Restoration
October 2008 Colloquium, Boise State.

Statistical Properties of the Regularized Least Squares Functional and a hybrid LSQR Newton method for Finding the Regularization Parameter: Application in Image Deblurring and Signal Restoration
April 2009, Michigan State University, Midwest Conference on Mathematical Methods for Images and Surfaces. Invited Presentation.

Statistically-Based hybrid LSQR Newton method for Finding the Regularization Parameter: Application in Image Deblurring and Signal Restoration
April 2009, Univerity of Maryland College Park, Colloquium.

A Hybrid LSQR Regularization Parameter Estimation Algorithm for Large Scale Problems
July 2009, SIAM Annual Meeting Minisymposium Presentation

Statistical Regularization Approaches for Linear/Nonlinear Inverse Problems: Hybrids
July 2009, Workshop Inverse Problems Fort Collins Presentation

Statistically-Based Regularization Parameter Estimation for Large Scale Problems
October 2009, University of Maryland Baltimore County. Colloquium

Towards Solution of Large Scale Image Restoration and Reconstruction Problems
November 2009, FDA. Colloquium

Towards Solution of Large Scale Signal Restoration Problems with Multi-Parameter Estimates
December 2009, Rensellaer Polytechnic Institute. Colloquium

Statistically-Based Regularization Parameter Estimation for Large Scale Problems
March 2010, U Texas Austin Colloquium

Regularized Least Squares by Multisplitting and Multiple Right Hand Sides
April 2010, SIAM Imaging Meeting, Minisymposium Presentation

Regularized Least Squares by Multisplitting and Multiple Right Hand Sides: Additive Schwarz for Least Squares with Updated Right Hand Side Approaches
IMA Numerical Linear Algebra and Optimization meeting, Birmingham, Sep 2010. Presentation. Also presented at the ILAS meeting in Pisa Italy, July 2010 and the BIT 50th Trends in Numerical Methods Meeting, Lund, June 2010.

Sparsity Enforcing Edge Detection Method for Blurred and Noisy Fourier Data
February Fourier Talks, 2011, University of Maryland, College Park. February 2011

Sparsity Enforcing Edge Detection Method for Blurred and Noisy Fourier Data
Minisymposium Presentation, SIAM Computational Science and Engineering Meeting, March 2011.

Sparsity Enforcing Edge Detection Method for Blurred and Noisy Fourier Data
Colloquium, Department of Mathematics, Temple University, April 2011.

SPARSITY ENFORCING EDGE DETECTION METHOD FOR BLURRED AND NOISY FOURIER DATA: IS THE REGULARIZATION PARAMETER IMPORTANT FOR IMAGE SEGMENTATION?
Applied Inverse Problems Conference, Texas A&M, May 2011

An approach for robust segmentation of images from arbitrary Fourier data using l1 minimization techniques
Large-scale Inverse Problems and Quantification of Uncertainty, Institute of Mathematics and Its Applications, Invited Workshop Presentation, June 2011.

Recycling Krylov Subspaces for Efficient Schwarz Algorithms with Extensions to Solve Regularized Least Squares Problems
Householder Symposium XVIII, Lake Tahoe, June 2011.

An approach for robust segmentation of images from arbitrary blurred and noisy Fourier data
Colloquium, Charles University, Prague, Czech Republic, November 2011.

Algorithms Towards Improving Data Uncertainty in Image Analysis Problems: Examples from Positron Emission Tomography Images
Distinguished Lecture Series, College of Arts and Sciences, Georgia State University, December 2011

An approach for feature extraction for images and signals which are defined from arbitrary blurred and noisy Fourier data
Institute of Information Theory and Automation, Academy of Sciences, Czech Republic, Prague, January 2012.

Solution of Ill-Posed Inverse Problems Pertaining to Signal Restoration for Feature Extraction
School of Mathematical Sciences, Leeds University, March 5, 2012.

Solution of ill-posed inverse problems pertaining to signal restoration : Total Variation restoration
Computational Mathematics Seminar, Oxford University, March 8, 2012.

Solution of ill-posed inverse problems pertaining to signal restoration : Total Variation restoration
Scientific Computing Seminar, University of Manchester, March 23, 2012.

Solution of ill-posed inverse problems pertaining to signal restoration : Understanding Noise in the Basis
Numerical Analysis Seminar, University of Edinburgh, April 12, 2012.

Generalized Picard Condition Analysis for Estimating Parameters in the Split Bregman Algorithm for Noisy Data
SIAM Imaging Sciences Presentation, May 2012.