Agenda

A preliminary version of the technical program is now available.

Below are useful links regarding the technical program.

Monday, July 27

09:15 - 09:30 Welcome

09:30 - 11:30 Invited Talks I

  • Stan Osher - How sparsity and L1 optimization impacts "continuous" applied mathematics, physics and engineering
  • Phil Schniter - Iteratively reweighted L1 approaches to sparse composite regularization
  • Volkan Cevher - A universal primal-dual convex optimization framework
  • Yuxin Chen - Solving random quadratic systems of equations is nearly as easy as solving linear systems

11:30 - 12:30 Whiteboards I

12:30 - 13:30 Lunch

13:30 - 15:30 Invited Talks II

  • Jared Tanner - Parallel-L0, a fully parallel algorithm for combinatorial compressed sensing
  • Babak Hassibi - LASSO with nonlinear measurements
  • Joel Tropp - Applied random matrix theory
  • Yonina Eldar - Sub-Nyquist sampling without sparsity

15:30 - 17:00 Posters I


Tuesday, July 28

09:00 - 10:30 Invited Talks III

  • Cynthia Rudin - Sparse if-then rule models
  • David Wipf - Non-Convex, Bayesian-inspired algorithms for sparse and low-rank estimation
  • John Paisley - Scalable Bayesian nonparametric dictionary learning

10:30 - 11:00 Coffee Break

11:00 - 12:00 Invited Talks IV

  • Alex Bronstein - Graph matching: relax or not?
  • Ken Bollen - The Latent Variable - Autoregressive Latent Trajectory (LV-ALT) model: a general framework for longitudinal data analysis

12:00 - 13:00 Whiteboards II

12:30 - 13:30 Lunch

13:30 - 15:30 Invited Talks V

  • Patrick Wolfe - Network analysis and nonparametric statistics
  • Pradeep Ravikumar - Elementary estimators for “big-p” statistical models
  • Marc Suchard - High-dimensional biological sequences through simple models and posterior diagnostics
  • James Scott - False discovery rate smoothing

15:30 - 17:00 Posters II

19:00 - 22:00 Workshop banquet


Wednesday, July 29

09:00 - 10:30 Invited Talks VI

  • Andrea Montanari - Semidefinite programming relaxations for graph estimation
  • Alfred Hero - Correlation mining from massive data: high dimensional sampling regimes
  • Marina Meila - Modeling ordered data by counting inversions

10:30 - 11:00 Coffee Break

11:00 - 12:00 Invited Talks VII

  • Tony Jebara - Graphical modeling with the Bethe approximation
  • Surya Ganguli - A theory of neural dimensionality, dynamics, and measurement
progOutline

Whiteboard Session I (Monday, July 27 from 11:30 am - 12:30 pm)

  • Pallavi Basu - Model Selection in High-Dimensional Misspecified Models
  • Miles Lopes - Compressed Sensing without Sparsity Assumptions
  • Henry Pfister - Connections Between Coding and Compressed Sensing
  • Farhad Pourkamali-Anaraki - Efficient PCA for large high-dimensional datasets via Randomized Sketching
  • Galen Reeves - Scalable Approximations of Marginal Posteriors in Variable Selection
  • Rebecca Willett - Learning Single Index Models in High Dimensions

Whiteboard Session II (Tuesday, July 28 from 12:00 - 1:00 pm)

  • Ingrid Daubechies - ConceFT: Concentration in Frequency and Time
  • Kai Fan - Hierarchical Graph-Coupled HMMs for Heterogeneous Personalized Health Data
  • Raja Giryes - Theoretical Limits in Sparsity and Deep Learning
  • Sayan Mukherjee - Learning mixtures of subspaces
  • Manolis Tsakiris - Abstract Algebraic Subspace Clustering
  • Sergey Voronin - Randomized blocked algorithms for efficiently computing rank-revealing factorizations of matrices

Poster Session I (Monday, July 27 from 3:30 - 5:00 pm)

Last name M - Z

  • Yanting Ma - Universal Denoising in Approximate Message Passing
  • Sorin Mitran - Information geometry and model reduction
  • Ikenna Odinaka - Spectrally Grouped Edge-Preserving Reconstruction
  • Qiang Qiu - Random Forests Can Hash
  • Qing Qu - Complete Dictionary Learning Over the Sphere
  • Akshay Rangamani - Learning Program Attributes in Control Flow Graphs
  • Owen Rehrauer - Fluorescence Modeling for OB-CD Raman Spectroscopy
  • Abhra Sarkar - Bayesian Nonparametric Higher Order Markov Chains
  • Anand Sarwate - The performance of differentially private PCA
  • Shahin Sefati - Linear Systems with Sparse Inputs
  • Anish Simhal - Computational statistics for CLARITY volumes
  • Catherine Stamoulis - Signal processing approaches for genomic data
  • Charles Talbot - Reduced Stochastic Models of Permeable Medium Flow
  • Mariano Tepper - Compressed NMF is Fast and Accurate
  • Manolis Tsakiris - Abstract Algebraic Subspace Clustering
  • Kyle Ulrich - Gaussian Process Kernels for Cross-Spectrum Analysis
  • Sergey Voronin - An efficient algorithm for computing a CUR factorization
  • Tong Wang - Bayesian Or’s of And’s for Interpretable Classification
  • Yizhe Zhang - Spatial dependent deep factor model

Poster Session II (Tuesday, July 28 from 3:30 - 5:00 pm)

Last name A - L

  • Amit Ashok - Analysis & Simulation Framework: X-ray Threat Detection
  • Martin Azizyan - Extreme Compressive Sampling for Covariance Estim.
  • Dror Baron - Image Reconstruction in Radio Astronomy
  • Evan Byrne - Sparse Multinomial Logistic Regression via AMP
  • Dan Coroian - Learning a Personalized CDSS From EHR Data
  • Jyotishka Datta - Bayesian Cluster Detection for Rare Variants
  • Mauricio Delbracio - Burst Deblurring
  • Lee Dicker - Efficient variance estimation for high-dimensional linear models
  • Yan Feng - Model reduction of stochastic biomechanical system
  • Raja Giryes - Deep Neural Networks with Random Gaussian Weights: A Universal Classification Strategy?
  • Joel Greenberg - Coding and compression in snapshot XRD imaging
  • Shermin Hamzehei - Compressive Parameter Estimation via AMP
  • Jordan Hashemi - Pose-invariant cross-modality facial expression
  • Alfred Hero - On the sample complexity of correlation mining
  • Xin Jiang - Minimax Rates for Photon Limited Image Reconstruction
  • Mojtaba Kadkhodaie - Locating Rare and Weak Material Anomalies by Convex Demixing of Propagating Wavefield Data
  • Yan Kaganovsky - Variational Automatic Relevance Determination
  • Yuehaw Khoo - NMR structural calculation via semidefinite programming
  • Jinyoung Kim - Robust Prediction of DBS targeting structures
  • Santhosh Kumar - Reed-Muller Codes Achieve Capacity on erasure Channels
  • Yuanxin Li - Stable Super-Resolution of Mixture Models
  • Mengke Lian - Belief-Propagation Reconstruction for Compressed Sensing: Quantization vs. Gaussian Approximation
  • Luoluo Liu - Partial Face Recognition
  • Miles Lopes - Compressed Sensing without Sparsity Assumptions
  • John Lu - Optical imaging for forensics
  • Yue Lu - Randomized Kaczmarz Algorithm and its Cousins: Exact MSE Analysis and Asymptotically Sharp Bounds