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Bio
Dr. Baraniuk's research interests in signal processing and machine learning lie primarily in new theory and algorithms involving low-dimensional models. His research on theory of deep learning, compressive sensing, multiscale natural image modeling using wavelet-domain hidden Markov models, and time-frequency analysis has been funded by NSF, DARPA, ONR, AFOSR, AFRL, ARO, IARPA, DOE, NGA, EPA, NATO, the Texas Instruments Leadership University Program, and several companies. In particular, he has served as Project Director for the ARO MURI on "Opportunistic Sensing" from 2013-2018, the ONR MURI on "Foundations of Deep Learning" from 2020-2025, the DARPA/DOE "INCITE" project, and several DARPA projects, including "Analog to Information," "Analog to Information Receiver," and "Network Modeling and Simulation." He was a member of the DARPA Information Science and Technology (ISAT) Study Group from 2008-2011. More information on his signal processing and machine learning research.
Research Interests
Papers共 871 篇Author StatisticsCo-AuthorSimilar Experts
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FOURTEENTH INTERNATIONAL CONFERENCE ON LEARNING ANALYTICS & KNOWLEDGE, LAK 2024 (2024): 828-835
CoRR (2024)
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CoRR (2024): 163-176
CoRR (2024): 309-323
SIAM JOURNAL ON MATHEMATICS OF DATA SCIENCEno. 1 (2024): 199-225
arxiv(2024)
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arxiv(2024)
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