phil schniter - research

publications by date

publications by topic

google scholar

selected overview talks:

• Recent Advances in Approximate Message Passing, Summer 2019.
• Regularization by Denoising: Clarifications and New Interpretations, Fall 2018.
• A Primer on Compressive Sensing, Summer 2016.
• Statistical Image Recovery: A Message-Passing Perspective, Spring 2015.
• Applications of GAMP to Communications Receivers, Spring 2014.
• Bilinear Generalized AMP, Fall 2013.
• AMP Tools for Large-Scale Inference, Fall 2013.

dissertations / theses supervised:

• Edward Reehorst (PhD 12/2022): ''Machine Learning for Image Inverse Problems and Novelty Detection''
• Michael Wharton (MS 5/2020): ''Deep Learning For RADAR Signal Processing''
• Subrata Sarkar (PhD 8/2020): ''Solving Linear and Bilinear Inverse Problems using Approximate Message Passing Methods''
• Evan Byrne (PhD 5/2019): ''Inference in Generalized Linear Models with Applications''
• Evan Byrne (MS 8/2015): ''Sparse Multinomial Logistic Regression via Approximate Message Passing''
• Jeremy Vila (PhD 5/2015): ''Empirical-Bayes Approaches to Recovery of Structured Sparse Signals via Approximate Message Passing''
• Justin Ziniel (PhD 8/2014): ''Message Passing Approaches to Compressive Inference Under Structured Signal Priors''
• Jason Parker (PhD 7/2014): ''Approximate Message Passing Algorithms for Generalized Bilinear Inference''
• Jordan Hawkins (BS 3/2013): ''Automating Music Production with Music Information Retrieval''
• Dong Meng (MS 6/2012): ''Approximate Message Passing for Multi-Carrier Transmission over Doubly Selective Channels''
• Rohit Aggarwal (PhD 5/2012): ''Resource Allocation and Design Issues in Wireless Systems''
• Sugumar Murugesan (PhD 7/2010): ''Opportunistic Scheduling Using Channel Memory in Markov-Modeled Wireless Networks''
• Sung Jun Hwang (PhD 12/2009): ''Noncoherent Communication over Time and Frequency Selective Channels''
• Brian Carroll (MS 6/2009): ''Analysis of Sparse Channel Estimation''
• Sibasish Das (PhD 1/2008): ''Analysis and Design of Pilot-Aided Multicarrier Systems over Doubly Selective Channels with a Local Subcarrier Processing Constraint''
• Hong "Iris" Liu (PhD 7/2007): ''Frequency-Domain Equalization of Single-Carrier Communications over Doubly Selective Channels''
• Arun Pachai Kannu (PhD 3/2007): ''Communications Over Noncoherent Doubly Selective Channels''
• Sugumar Murugesan (MS 7/2006), ''Training and Scheduling in the Non-Coherent MIMO Uplink''
• Adam R. Margetts (PhD 8/2005), ''Joint Scale-Lag Diversity in Mobile Wideband Communications''
• Sibasish Das (MS 11/2004), ''Turbo-Equalization of Pulse-Shaped Multi-carrier Modulation in Doubly Selective Channels''
• Arun Pachai Kannu (MS 5/2004), ''Tracking of Doubly-Dispersive Channels''
• Siddharth D'Silva (MS 10/2002), ''On OFDM in Doubly-Dispersive Channels ''
• Adam R. Margetts (MS 10/2002), ''Adaptive Chip-Rate Equalization of Downlink Multirate Wideband CDMA ''
• Wei Hu (MS 8/2002), ''Blind Equalization and Identification for Differential Space-Time Modulated Communication Systems''
• Bijoy Bhukania (MS 8/2002), ''Detection of Differential Unitary Space-Time Modulation in Fast Rayleigh-Fading Channels''

current supervisees:

• Saurav Shastri (PhD student): AMP algorithms, plug-and-play algorithms, MRI
• Srijith Nair (PhD student): Inverse problems, deep regularization
• Chris Ebersole (PhD student): Novelty detection, contrastive learning, radar
• Matt Bendel (PhD student): MRI, conditional GANs
• Jeffrey Wen (PhD student): MRI, conditional normalizing flows
• Xuan Lei (PhD student): MRI, registration

public software packages:

• MRI-CNF: A Conditional Normalizing Flow for Accelerated Multi-Coil Magnetic Resonance Imaging
  
• RC-GAN: A Regularized Conditional GAN for Posterior Sampling in Inverse Problems
  
• D-GEC: Denoising generalized expectation consistent approximation for inverse problems with Fourier-structured operators and signals
  
• corr+corr: A deep-net denoiser for correlated noise
  
• RED: Regularization by denoising, score-matching by denoising, RED-PG algorithms
  
• LAMP: Onsager-corrected deep neural networks (Learned AMP and learned VAMP)
  
• D-VAMP: Denoising-based Vector AMP.
  
• Co-L1: Iteratively Reweighted L1 Approaches to Sparse Composite Regularization.
  
• SMLR-AMP: Sparse Multinomial Logistic Regression via Simplified Hybrid GAMP.
  
• HUT-AMP: Hyperspectral Unmixing via Turbo GAMP.
  
• GrAMPA: GAMP for Analysis Compressive Sensing.
  
• BiG-AMP: Bilinear GAMP: a solver for matrix completion, robust PCA, dictionary learning, and related problems.
  
• EM-NN-GAMP: Expectation-Maximization Non-Negative GAMP.
  
• EM-GM-GAMP: Expectation-Maximization Gaussian-Mixture GAMP.
  
• EM-BG-GAMP: Expectation-Maximization Bernoulli-Gaussian GAMP.
  
• EMturboGAMP: A general framework for turbo-GAMP that use Expectation Maximization to automatically learn the priors and likelihood.
  
• DCS-AMP: AMP for the Dynamic Compressed Sensing problem.
  
• MMV-AMP: AMP for the Multiple Measurement Vector problem.
  
• Turbo-GAMP Channel-Estimation/Equalization/Decoding: Turbo GAMP applied to joint estimation/equalization/decoding of OFDM signals transmitted over channels with clustered-sparse impulse responses.
  
• Turbo-AMP Imaging: Turbo AMP applied to Compressive Imaging that exploits tree-sparse structure.
  
• FBMP: Fast Bayesian Matching Pursuit (FBMP): A greedy approach to Bayesian sparse reconstruction.
  
• The BERGulator: A Matlab-5 simulation environment for blind fractionally-spaced equalization via CMA.
  
• Direct Adaptive Linear Equalizer (DALE) Laboratory: A Matlab-5 simulation environment for trained fractionally-spaced equalization via LMS.
  
• Adaptive Linear Identifier (ALI) Laboratory: A Matlab-5 simulation environment for trained fractionally-space channel identification via LMS.
  

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