Major Participant And Contributor at University of Utah
Salt Lake City, Utah, United States
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Summary
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Senior
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Top School
Xiang Huang is a research-driven communications engineer with 9 years of experience applying machine learning to beamforming, channel estimation, equalization, and coding across terrestrial and challenging underwater environments. Based in Salt Lake City and pursuing a PhD at the University of Utah, he has led novel neural-network architectures—such as unrolled BF-PS networks, LSTM-based two-time-scale designs, and the Extrinsic Neural Network Equalizer—that bridge theory and implementable systems. His work includes practical implementations (MATLAB/CUDA) and contributions to IEEE conferences and journals, demonstrating both algorithmic innovation and systems-level optimization. Xiang often targets domain-specific constraints—e.g., vehicle acceleration patterns for underwater dictionary design or limited-sample channel extrapolation—yielding measurable gains in reliability and spectral efficiency. He combines solid coding and parallel-computing skills with a publication track record, positioning him to translate cutting-edge research into deployable communications solutions. An imaginative practitioner (GitHub bio: "Create my own world!") who thrives on building tailored ML solutions for complex wireless problems.
9 years of coding experience
The University of Utah
Master, Information and Communication Engineering, Master, Information and Communication Engineering at Chongqing University of Post and Telecommunications(CQUPT), China
Contributions:18 commits, 17 pushes, 1 branch in 2 days
pytorchfiltersobject-detectionyolov7yolov5
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Xiang Huang - Major Participant And Contributor at University of Utah