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Poster Session

Once again the Norbert Wiener Center will be hosting a poster session on the first day of the conference. The poster session will be held on Thursday, February 18, 2016. It will take place from 5:00pm-6:15pm in the rotunda of the Mathematics building.

We are pleased to feature the following research posters at the 2016 February Fourier Talks:

Armenak Petrosyan, Vanderbilt
Iterative Systems of Vectors

Anna Ma, Claremont
Convergence properties of the randomized extended Gauss-Seidel and Kaczmarz method

Tina Woolf, Claremont
Stochastic Greedy Methods with Sparse Constraints

Tingran Gao, Duke
Horizontal Laplacian and the Diffusion Geometry of Fibre Bundles

Joakim Anden, Princeton
Estimating noise power spectra in a low-rank noise model

Rujie Yin, Duke
Construction of Orthonormal Directional Wavelets based on quincunx dilation subsampling

Elham Sakhaee, University of Florida
Sparse Partial Derivatives and Reconstruction from Partial Fourier Data

Hassan Mohy-ud-Din, Yale
Quantitative myocardial perfusion PET parametric imaging at the voxel-level

Christopher Kennedy, UT Austin
Fast Cross-Polytope Hashing via Johnson Lindenstrauss Embeddings

Ayush Bhandari, MIT
A Swiss Army Knife for Sparse Sampling Theory

Josiah Park, USF
Generalied Phase Retrieval: Isometries in Vector Spaces

Rachel Yin, Duke
Construction of Orthonormal Directional Wavelets based on quincunx dilation subsampling

Sam Scholze, TAMU
Encoding Frame Protected Reconstruction from Frame Erasures

Soledad Villar, UT Austin
Fast certificates for clustering optimality using convex programming

Jen Dumiak, Leo Brody, and Brian Dizio, American
Topological Data Quality Assessment for Sonar Targets and Homological Features for Sonar Target Classification

Ankit Parekh, NYU
Enhanced Low-Rank Matrix Approximation

Haizhao Yang, Stanford and Yingzhou Li, Duke
Butterfly Factorization

Chang-Hsin Lee, Vanderbilt
General Error Bounds of Consistent Reconstruction and Rangan-Goyal Algorithm

Calvin Hotchkiss, Iowa State
A Fast Fourier Transform for Fractal Approximations

Kyle Michael Bowman, UNC, Wilmington
Numerical Methods and the Finite Balian-Low Conjecture

Bubacarr Bah, UT Austin
The sample complexity of weighted sparse approximation

Cihan Bilge Kayasandik, University of Houston
A multiscale geometric descriptor for the automated extraction of somas in fluorescent images of neurons

Joseph Woodworth, UCLA
Compressed Sensing Recovery via Nonconvex Shrinkage Penalties

Ke Yin, UCLA
Compressed Wannier modes for imperfect crystals

Sam Potter , UMD
A Periodic FMM-based Nonuniform FFT

Tara Shreve and David D'Auria American
Validation of Angle Estimation Filters for Ocean Wave Spectra

Farhad Pourkamali Anaraki, UC, Boulder
Preconditioned Data Sparsification for Big Data with Applications to PCA and K-means

Farhad Pourkamali Anaraki, UC, Boulder
Preconditioned Data Sparsification for Big Data with Applications to PCA and K-means

Wei-Hsuan, Michigan State
There are no 76 equiangular lines in R^19

wjliao, Duke
Adaptive geometric multi-resolution analysis for high-dimensional data

Olga Orlova, UNM
On approximation properties of generalized (Kantorovich-type) sampling operators.

Cheng Cheng, UCF
Spatially Distributed Sampling and Reconstruction

JeremyTrageser, GW
A nonlocal biharmonic operator and its connections to the classical operator

Michael Northington, Vanderbilt
Sharp Uncertainty Principles for Shift-Invariant Spaces

Marilyn Vazquez, GMU
Diffusion Maps for Image Segmentation

Li Chen, UDC
Mathematical problems in data science

Katherine Cordwell and Mark Magsino, University of Maryland
On Constant Amplitude Zero Autocorrelation Sequences of length p

Abbas Kazemipour, University of Maryland
Sampling Requirements of Stable Autoregressive Estimation

Sui Tang, Vanderbilt
The exact reconstruction of an evolving signal from the incomplete information of its current and future states

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Norbert Wiener Center
Department of Mathematics
University of Maryland
College Park, MD 20742
Phone: (301) 405-5158
The Norbert Wiener Center is part of the College of Computer, Mathematical, and Natural Sciences.