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Trevor Reed is a postdoctoral research associate at the University of Virginia School of Data Science. His background is in experimental nuclear physics, working on interdisciplinary projects at the intersection of nuclear physics and data science. Reed has collaborated with researchers at the Thomas Jefferson National Accelerator Facility to apply machine learning models, such as autoencoders and variational autoencoders, to reanalyze complex nuclear/particle reaction datasets and uncover hidden physical patterns.
Prior to joining UVA in 2025, Reed was a research assistant and postdoctoral associate at Florida International University. His research included serving as a collaborator with the Jefferson Lab’s CEBAF Large Acceptance Spectrometer (CLAS), focusing on improving the efficiency and accuracy of simulation studies using deep neural networks. He contributed to multiple projects within the CLAS Collaboration, including developing algorithms to correct detector alignment, kinematic fitting, and building software frameworks.
Reed holds a Ph.D. and a B.S. in physics from Florida International University.
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