2026 Richard G. Baraniuk: Electronics and Electrical Engineering Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	120	144	139	84	81	741	76110
Electronics and Electrical Engineering	113	87	86	42	41	548	67991

Research.com Recognitions

2026 - Research.com Computer Science in United States Leader Award
2026 - Research.com Electronics and Electrical Engineering in United States Leader Award
2025 - Research.com Computer Science in United States Leader Award
2025 - Research.com Electronics and Electrical Engineering in United States Leader Award
2023 - Research.com Computer Science in United States Leader Award
2022 - Research.com Computer Science in United States Leader Award
2017 - Fellow of the American Academy of Arts and Sciences
2016 - Fellow, National Academy of Inventors
2009 - Fellow of the American Association for the Advancement of Science (AAAS)

Overview

Richard G. Baraniuk is affiliated with Rice University in the United States. Their research predominantly spans the field of Computer Science, with a strong focus on Artificial Intelligence, Computer Vision and Pattern Recognition, Computational Mechanics, Biomedical Engineering, and Statistics and Probability.

The scientist's work addresses a variety of topics within these areas, including:

Topic Modeling
Generative Adversarial Networks and Image Synthesis
Domain Adaptation and Few-Shot Learning
Natural Language Processing Techniques
Anomaly Detection Techniques and Applications
Intelligent Tutoring Systems and Adaptive Learning
Advanced Neural Network Applications

Baraniuk's publication record includes recent papers such as:

"Deep Learning Techniques for Inverse Problems in Imaging" (2020, IEEE Journal on Selected Areas in Information Theory)
"Current progress and open challenges for applying deep learning across the biosciences" (2022, Nature Communications)
"Clustering earthquake signals and background noises in continuous seismic data with unsupervised deep learning" (2020, Nature Communications)
"Dual Dynamic Inference: Enabling More Efficient, Adaptive, and Controllable Deep Inference" (2020, IEEE Journal of Selected Topics in Signal Processing)
"The science of deep learning" (2020, Proceedings of the National Academy of Sciences)

Their frequent co-authors include:

Randall Balestriero
Shashank Sonkar
Naiming Liu
Ahmed Imtiaz Humayun
Zichao Wang

The venues where Baraniuk has published regularly include:

arXiv (Cornell University)
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
SIAM Journal on Mathematics of Data Science
IEEE Transactions on Signal Processing
IEEE Journal on Selected Areas in Information Theory

Richard G. Baraniuk has received recognition from several organizations, including:

Fellow of the American Academy of Arts and Sciences (2017)
Fellow, National Academy of Inventors (2016)
Fellow of the American Association for the Advancement of Science (AAAS) (2009)

Best Publications

Compressive Sensing [Lecture Notes]

R.G. Baraniuk

6327
Citations
Single-Pixel Imaging via Compressive Sampling

M.F. Duarte;M.A. Davenport;D. Takhar;J.N. Laska

4489
Citations
The dual-tree complex wavelet transform

I.W. Selesnick;R.G. Baraniuk;N.C. Kingsbury

3213
Citations
A Simple Proof of the Restricted Isometry Property for Random Matrices

Richard G. Baraniuk;Mark A. Davenport;Ronald A. DeVore;Michael B. Wakin

3148
Citations
Wavelet-based statistical signal processing using hidden Markov models

M.S. Crouse;R.D. Nowak;R.G. Baraniuk

2510
Citations
Model-Based Compressive Sensing

R.G. Baraniuk;V. Cevher;M.F. Duarte;C. Hegde

2098
Citations
Beyond Nyquist: Efficient Sampling of Sparse Bandlimited Signals

J.A. Tropp;J.N. Laska;M.F. Duarte;J.K. Romberg

1397
Citations
Compressive Radar Imaging

R. Baraniuk;P. Steeghs

1309
Citations
pathChirp: Efficient available bandwidth estimation for network paths

Vinay J. Ribeiro;Jiri Navratil;Rudolf H. Riedi;Richard G. Baraniuk

1173
Citations
Material parameter estimation with terahertz time-domain spectroscopy

Timothy D. Dorney;Richard G. Baraniuk;Daniel M. Mittleman

1094
Citations
Recent advances in terahertz imaging

D.M. Mittleman;M. Gupta;R. Neelamani;R.G. Baraniuk

1037
Citations
A single-pixel terahertz imaging system based on compressed sensing

Wai Lam Chan;Kriti Charan;Dharmpal Takhar;Kevin F. Kelly

961
Citations
A multifractal wavelet model with application to network traffic

R.H. Riedi;M.S. Crouse;V.J. Ribeiro;R.G. Baraniuk

941
Citations
Fast Alternating Direction Optimization Methods

Tom Goldstein;Brendan O'Donoghue;Simon Setzer;Richard G. Baraniuk

917
Citations
1-Bit compressive sensing

P.T. Boufounos;R.G. Baraniuk

851
Citations
A new compressive imaging camera architecture using optical-domain compression

Dharmpal Takhar;Jason N. Laska;Michael B. Wakin;Marco F. Duarte

816
Citations
Bayesian tree-structured image modeling using wavelet-domain hidden Markov models

J.K. Romberg;Hyeokho Choi;R.G. Baraniuk

807
Citations
Signal Processing With Compressive Measurements

M.A. Davenport;P.T. Boufounos;M.B. Wakin;R.G. Baraniuk

791
Citations
Robust 1-Bit Compressive Sensing via Binary Stable Embeddings of Sparse Vectors

L. Jacques;J. N. Laska;P. T. Boufounos;R. G. Baraniuk

773
Citations
From Denoising to Compressed Sensing

Christopher A. Metzler;Arian Maleki;Richard G. Baraniuk

727
Citations
Compressive sensing

R. Baraniuk

652
Citations

Frequent Co-Authors

Michael B. Wakin Colorado School of Mines

Marco F. Duarte University of Massachusetts Amherst

Christoph Studer ETH Zurich

Mark A. Davenport Georgia Institute of Technology

Aswin C. Sankaranarayanan Carnegie Mellon University

Rudolf H. Riedi Rice University

Robert Nowak University of Wisconsin–Madison

Douglas L. Jones University of Illinois at Urbana-Champaign

Ashok Veeraraghavan Rice University

Petros T. Boufounos Mitsubishi Electric (United States)

External Links

Wikipedia of Richard G. Baraniuk Personal website of Richard G. Baraniuk Google Scholar page

If you think any of the details on this page are incorrect, let us know.

Related Online Degrees & Career Pathways

For those interested in advancing their education in Electronics and Electrical Engineering, exploring flexible learning options is essential. A competency based masters degree offers a personalized approach where students progress by demonstrating skills rather than spending fixed time in class. This format is especially beneficial for working professionals aiming to upskill efficiently.

Military spouses and dependents often face unique challenges when pursuing higher education. Fortunately, there are many online colleges for military spouses that provide flexible schedules and supportive resources, allowing them to continue their studies seamlessly despite frequent relocations.

Starting a program quickly can be a priority for many students. Numerous online colleges with frequent start dates allow learners to enroll throughout the year, minimizing wait times and offering more control over their educational timeline.

For those seeking to enter the workforce rapidly or enhance their credentials, short certificate programs that pay well provide targeted training with impressive ROI. These certificates can open doors to specialized roles in engineering and technology, complementing traditional degrees.