Richard G. Baraniuk

About

Richard G. Baraniuk is a professor whose research focuses on signal processing, machine learning, and compressive sensing. His work involves developing innovative methods for data acquisition, analysis, and reconstruction, with applications across various scientific and engineering domains. Throughout his career, he has contributed significantly to the advancement of compressive sensing techniques and their practical implementations. Professor Baraniuk's background includes extensive research in signal processing and related fields, and he is actively involved in leading research groups and collaborative projects. His contributions have been recognized through numerous awards and fellowships, and he has mentored many students and postdoctoral researchers who have gone on to prominent academic and industry positions. His work continues to influence the development of efficient algorithms and systems for processing large-scale and high-dimensional data.

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Computational biology
• Biology
• Data Mining
• Engineering
• Mathematics
• Statistics
• Information Retrieval

Selected publications

• Deep Learning Techniques for Inverse Problems in Imaging

  IEEE Journal on Selected Areas in Information Theory · 2020 · 553 citations

  Recent work in machine learning shows that deep neural networks can be used to solve a wide variety of inverse problems arising in computational imaging. We explore the central prevailing themes of this emerging area and present a taxonomy that can be used to categorize different problems and reconstruction methods. Our taxonomy is organized along two central axes: (1) whether or not a forward model is known and to what extent it is used in training and testing, and (2) whether or not the learni…

  DOI

• Current progress and open challenges for applying deep learning across the biosciences

  Nature Communications · 2022 · 371 citations

  Deep Learning (DL) has recently enabled unprecedented advances in one of the grand challenges in computational biology: the half-century-old problem of protein structure prediction. In this paper we discuss recent advances, limitations, and future perspectives of DL on five broad areas: protein structure prediction, protein function prediction, genome engineering, systems biology and data integration, and phylogenetic inference. We discuss each application area and cover the main bottlenecks of…

  PublisherOA PDFDOI

• Clustering earthquake signals and background noises in continuous seismic data with unsupervised deep learning

  Nature Communications · 2020 · 199 citations

  Senior authorCorresponding

  The continuously growing amount of seismic data collected worldwide is outpacing our abilities for analysis, since to date, such datasets have been analyzed in a human-expert-intensive, supervised fashion. Moreover, analyses that are conducted can be strongly biased by the standard models employed by seismologists. In response to both of these challenges, we develop a new unsupervised machine learning framework for detecting and clustering seismic signals in continuous seismic records. Our appro…

  DOI

• Dual Dynamic Inference: Enabling More Efficient, Adaptive, and Controllable Deep Inference

  IEEE Journal of Selected Topics in Signal Processing · 2020 · 87 citations

  State-of-the-art convolutional neural networks (CNNs) yield record-breaking predictive performance, yet at the cost of high-energy-consumption inference, that prohibits their widely deployments in resource-constrained Internet of Things (IoT) applications. We propose a dual dynamic inference (DDI) framework that highlights the following aspects: 1) we integrate both input-dependent and resource-dependent dynamic inference mechanisms under a unified framework in order to fit the varying IoT resou…

  DOI

• Drawing Early-Bird Tickets: Toward More Efficient Training of Deep Networks

  arXiv (Cornell University) · 2020 · 78 citations

  Senior authorCorresponding

  (Frankle & Carbin, 2019) shows that there exist winning tickets (small but critical subnetworks) for dense, randomly initialized networks, that can be trained alone to achieve comparable accuracies to the latter in a similar number of iterations. However, the identification of these winning tickets still requires the costly train-prune-retrain process, limiting their practical benefits. In this paper, we discover for the first time that the winning tickets can be identified at the very early tra…

Recent grants

• Collaborative Research: Design and Analysis of Compressed Sensing DNA Microarrays

  NSF · $316k · 2007–2012

• CIF: Small: Lens-Free Imaging: Can Signal Processing Replace Lenses?

  NSF · $548k · 2015–2018

• Accelerating STEM Learning Through Large-Scale Data Science

  NSF · $5.2M · 2019–2024

Frequent coauthors

• Randall Balestriero

  74 shared

• Andrew Lan

  60 shared

• Rudolf H. Riedi

  HES-SO University of Applied Sciences and Arts Western Switzerland

  52 shared

• Hyeokho Choi

  University of Illinois Urbana-Champaign

  51 shared

• Michael B. Wakin

  47 shared

• Marco F. Duarte

  University of Massachusetts Amherst

  44 shared

• Aswin C. Sankaranarayanan

  Carnegie Mellon University

  42 shared

• Arian Maleki

  42 shared

Labs

• Rice DSP GroupPI

Awards & honors

• Elected Member of the National Academy of Engineering (NAE)…
• Harold W.. McGraw, Jr. Prize in Education (2022)

Similar researchers at Rice University

• Moshe Y. Vardih-105
• Stephen Wongh-90
• Lydia E. Kavrakih-71