GradNova

Home
Find Faculty
Universities
Research
Home
/
Research
/

Sparse and Compressive Sensing Techniques

Sparse and Compressive Sensing Techniques

180 faculty research Sparse and Compressive Sensing Techniques on GradNova.

W
Compressed Sensing
In-Memory Computing
Neural Engineering
Ultra-Low Power VLSI
+5
X
Blind Equalization
Cognitive Radio Networks
Signal Processing in Communications and Wireless Networks
Wireless Information Assurance
+5
Y
<p>statistical Machine Learning
Optimization for Machine Learning and Deep Learning</p>
Sparse and Compressive Sensing Techniques
Viral Infections and Immunology Research
+3
A
Advanced Bandit Algorithms Research
Markov Chains and Monte Carlo Methods
Sparse and Compressive Sensing Techniques
Statistical Methods and Inference
+1
R
Advanced Chemical Sensor Technologies
Anomaly Detection Techniques and Applications
Neural Networks and Applications
Sparse and Compressive Sensing Techniques
+1
P
Prashant Khanduri

Assistant Professor, Computer Science

Computer Science

Wayne State University

Distributed Sensor Networks and Detection Algorithms
Privacy-Preserving Technologies in Data
Sparse and Compressive Sensing Techniques
Stochastic Gradient Optimization Techniques
+1
H
Hanbaek Lyu

Assistant Professor, Department of Mathematics

Computer Science

University of Wisconsin–Madison

Cellular Automata and Applications
Nonlinear Dynamics and Pattern Formation
Sparse and Compressive Sensing Techniques
Stochastic Processes and Statistical Mechanics
+1
S
Complexity and Algorithms in Graphs
Cryptography and Data Security
Machine Learning and Algorithms
Sparse and Compressive Sensing Techniques
+1
J
Advanced Optimization Algorithms Research
Machine Learning and Algorithms
Optimization and Variational Analysis
Sparse and Compressive Sensing Techniques
+1
Y
Yudong Chen

Sheldon B. and Marianne S. Lubar Professor

Computer Science

University of Wisconsin–Madison

Complex Network Analysis Techniques
Machine Learning and Algorithms
Sparse and Compressive Sensing Techniques
Statistical Methods and Inference
+1
Y
Machine Learning, Including Generative Modelling, Representation Learning, Robustness, and Kernel Methods, as Well as Application to Computer Vision and Natural Language Processing
Optimization Algorithms, Including Convex and Non Convex Optimization, Distributed and Federated Learning, Iteration Complexity Bounds, Robust and Stochastic Optimization
Sparse and Compressive Sensing Techniques
Stochastic Gradient Optimization Techniques
+3
S
Adversarial Robustness in Machine Learning
Domain Adaptation and Few-Shot Learning
Human Pose and Action Recognition
Multimodal Machine Learning Applications
+1
S
Advanced Bandit Algorithms Research
Advanced Optimization Algorithms Research
Risk and Portfolio Optimization
Sparse and Compressive Sensing Techniques
+1
K
Computational Learning for Reasoning
Machine Learning on Graphs
Sparse and Compressive Sensing Techniques
Complex Network Analysis Techniques
+3
V
Advanced Graph Theory Research
Advanced Optimization Algorithms Research
Complexity and Algorithms in Graphs
Computational Geometry and Mesh Generation
+1
S
Machine Learning
Security & Privacy
Privacy-Preserving Technologies in Data
Sparse and Compressive Sensing Techniques
+3
S
Data Mining
Generative AI
Machine Learning
Theoretical Computer Science
+5
A
Data Management Systems
Knowledge Graphs
Large-Scale Graph Algorithms
LLM Applications
+7
N
Nishant Mehta

Associate professor; data science advisor

Computer Science

University of Victoria

Machine Learning Theory
Advanced Bandit Algorithms Research
Machine Learning and Algorithms
Statistical Methods and Inference
+2
A
Algorithms (approximation / Online Algorithms, Graph Algorithms)
Artificial Intelligence (ml: Theory and Modeling)
Complexity and Algorithms in Graphs
Machine Learning and Algorithms
+3
M
Mahdi Soltanolkotabi

Andrew and Erna Viterbi Early Career Chair and Professor of Electrical and Computer Engineering, Computer Science, and Industrial and Systems Engineering

Ming Hsieh Department of Electrical and Computer Engineering

University of Southern California

Domain Adaptation and Few-Shot Learning
Machine Learning and Algorithms
Privacy-Preserving Technologies in Data
Sparse and Compressive Sensing Techniques
+1
V
Adversarial Robustness in Machine Learning
Domain Adaptation and Few-Shot Learning
Machine Learning and Algorithms
Machine Learning and Data Classification
+1
G
Gonzalo Mateos

Professor of Electrical and Computer Engineering; Professor of Computer Science; Asaro Biggar Family Fellow in Data Science; Associate Director, Goergen Institute for Data Science and Artificial Intelligence

Department of Electrical and Computer Engineering

University of Rochester

Computer Networks
Decentralized Optimization
Graph Signal Processing
Machine Learning
+6
M
Mujdat Cetin

Professor of Electrical and Computer Engineering; Professor of Computer Science; Director, New York State Center of Excellence in Data Science; Associate Vice President and Vice Provost of Artificial Intelligence

Department of Electrical and Computer Engineering

University of Rochester

Computational Sensing and Imaging as Applied to Radar and Biomedical Imaging
Probabilistic Methods for Image and Video Analysis as Applied to Biomedical Image Analysis, Microscopic Neuroimaging, and Computer Vision
Signal Processing and Machine Learning for Brain-Computer/machine Interfaces, With Applications for Alternative Communication and Rehabilitation for Patients and Monitoring of Cognitive States
Advanced SAR Imaging Techniques
+4
Previous
1

...

46

...

8
Next