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Sparse and Compressive Sensing Techniques

Sparse and Compressive Sensing Techniques

180 faculty research Sparse and Compressive Sensing Techniques on GradNova.

A
Alejandro Ribeiro

Professor Solomon and Sylvia G. Charp Professor of Electrical and Systems Engineering Electrical and Systems Engineering Primary Computer and Information Science

Department of Electrical and Systems Engineering

University of Pennsylvania

Advanced Graph Neural Networks
Complex Network Analysis Techniques
Distributed Control Multi-Agent Systems
Sparse and Compressive Sensing Techniques
+1
Q
Advanced Neural Network Applications
Domain Adaptation and Few-Shot Learning
Microbial Community Ecology and Physiology
Sparse and Compressive Sensing Techniques
+1
A
AI in Cancer Detection
Generative Adversarial Networks and Image Synthesis
Solar and Space Plasma Dynamics
Sparse and Compressive Sensing Techniques
+1
X
Advanced Neural Network Applications
Face and Expression Recognition
Medical Image Segmentation Techniques
Sparse and Compressive Sensing Techniques
+1
J
Advanced Image Processing Techniques
Advanced Optimization Algorithms Research
Matrix Theory and Algorithms
Sparse and Compressive Sensing Techniques
+1
D
Daniel Boley

Professor, Distinguished University Teaching Professor

Computer Science

University of Minnesota

Face and Expression Recognition
Matrix Theory and Algorithms
Numerical Methods for Differential Equations
Sparse and Compressive Sensing Techniques
+1
A
Ambuj Tewari

Professor, Statistics; Professor (courtesy), EECS – Computer Science and Engineering

Computer Science and Engineering

University of Michigan

AI for Chemistry
AI for Psychiatry
Machine Learning
Advanced Bandit Algorithms Research
+4
A
Alfred O. Hero

John H. Holland Distinguished University Professor of EECS; R. Jamison and Betty Williams Professor of Engineering; Professor, EECS – Electrical and Computer Engineering; Professor (courtesy), Biomedical Engineering; EECS – Computer Science and Engineering; Statistics

Department of Electrical and Computer Engineering

University of Michigan

Theory and Algorithms Underlying Data Science and Machine Learning. Theory Includes Applied Probability, Statistical Modeling, and Optimization. Applications Include Complex Networks, Spatio-Temporal Processes, Computational Biology, Personalized Health, and Security.
Sparse and Compressive Sensing Techniques
Distributed Sensor Networks and Detection Algorithms
Statistical Methods and Inference
+2
M
Michał Dereziński

Assistant Professor, EECS – Computer Science and Engineering

Computer Science and Engineering

University of Michigan

Dimensionality Reduction
Machine Learning and Statistical Learning Theory
Randomized Numerical Linear Algebra
Scalable Stochastic and Distributed Optimization
+5
Y
Advanced Image and Video Retrieval Techniques
Advanced MRI Techniques and Applications
Image Retrieval and Classification Techniques
Medical Imaging Techniques and Applications
+1
Q
Qiang (Shawn) Cheng

Associate Professor, Division of Biomedical Informatics, Department of Internal Medicine (courtesy joint appointment)

Computer Science

University of Kentucky

Blind Source Separation Techniques
Face and Expression Recognition
Remote-Sensing Image Classification
Sparse and Compressive Sensing Techniques
+1
I
Computational Techniques for Learning From Data That Are Highly Scalable With the Availability of the Compute
Working on Optimizers and Model Architecture Scaling Schemes That Would Allow Predictable and Efficient Training of Transformer Models Containing Hundreds of Billions of Parameters
Sparse and Compressive Sensing Techniques
Topic Modeling
+3
K
Blind Source Separation Techniques
Face and Expression Recognition
Matrix Theory and Algorithms
Sparse and Compressive Sensing Techniques
+1
R
Cloud Computing and Resource Management
Quantum Computing Algorithms and Architecture
Quantum Information and Cryptography
Quantum-Dot Cellular Automata
+1
A
Advanced Bandit Algorithms Research
Distributed Control Multi-Agent Systems
Reinforcement Learning in Robotics
Sparse and Compressive Sensing Techniques
+1
T
Blind Source Separation Techniques
Image and Signal Denoising Methods
Mathematical Analysis and Transform Methods
Microwave Imaging and Scattering Analysis
+1
L
Blind Source Separation Techniques
Complexity and Algorithms in Graphs
Computational Geometry and Mesh Generation
Point Processes and Geometric Inequalities
+1
L
Chaos-Based Image/signal Encryption
Cooperative Communication and Network Coding
Distributed Sensor Networks and Detection Algorithms
Sparse and Compressive Sensing Techniques
+1
B
Artificial Intelligence (ai)
Control, Intelligent Systems, and Robotics (cir)
Signal Processing (sp)
Sparse and Compressive Sensing Techniques
+4
M
Advanced MRI Techniques and Applications
Advanced Nmr Techniques and Applications
Atomic and Subatomic Physics Research
Medical Imaging Techniques and Applications
+1
A
Mathematical Programming
Theoretical Computer Science
Complexity and Algorithms in Graphs
Sparse and Compressive Sensing Techniques
+3
M
Artificial Intelligence & Machine Learning
Mathematical Programming
Scientific Computing
Theoretical Computer Science
+5
A
Applied Optimization: Distance Geometry, Large-Scale Kernel Methods, and Algorithm Unrolling for Signal Processing
High-Dimensional Data Analysis: Matrix Completion, Compressive Sensing, and Structured Sparse Recovery
Mathematical Foundations of Ai: Interpretable Machine Learning, Dictionary Learning, and Geometry-Aware Representations
Sparse and Compressive Sensing Techniques
+4
M
Misha Kilmer

William Walker Professor of Mathematics

Computer Science

Tufts University

Image Reconstruction and Restoration
Numerical Linear and Multilinear Algebra
Scientific Computing
Sparse and Compressive Sensing Techniques
+4
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