Alfredo González-Espinoza is a Research Data Librarian at Carnegie Mellon University with 11 years of quantitative research experience and a PhD in Physics, blending chemistry, physics and computer science to solve interdisciplinary data problems. He advises researchers on data management, reproducible workflows, and Python/Julia-based analysis while building digital artifacts for knowledge discovery and semantic mapping. His prior postdoctoral work quantified innovation in music—results featured in Scientific American—and he has developed hybrid data-driven clustering methods for cancer genomics. Alfredo designs AI-assisted data pipelines and applies complex-systems methods to time series and music score characterization, bringing a rare combination of theoretical rigor and practical tooling to academic and institutional research services.
11 years of coding experience
Doctor of Philosophy (Ph.D.), Science (Physics), Doctor of Philosophy (Ph.D.), Science (Physics) at Facultad de Ciencias UAEM / Instituto de Ciencias Físicas UNAM
Master's degree, Science (Physics), Master's degree, Science (Physics) at Facultad de Ciencias UAEM
Contributions:93 commits, 87 pushes, 1 branch in 3 years 4 months
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Alfredo González-espinoza - Research Data Librarian