Jorge Chang is a data scientist and PhD candidate in Cognitive Psychology who blends rigorous academic research with industry impact, currently applying machine learning at LexisNexis Risk Solutions. With 11 years of experience, he specializes in Bayesian optimization, neural networks, and Bayesian statistics to make experiments and material synthesis (e.g., single-wall carbon nanotubes, 3D printing) more efficient and ecologically valid. He programs primarily in Python with additional C++, MATLAB, and JavaScript experience, and has a strong track record in noisy-signal speaker recognition and applied ML research from roles at Ohio State and Oak Ridge National Laboratory. A multilingual professional born in Guatemala and based in Georgia, he is fluent in English, Spanish, and spoken Cantonese, which aids cross-cultural collaboration. His academic record includes an MS in Computer Science and a near-complete PhD with stellar GPAs, reflecting a rare combination of quantitative rigor and experimental design expertise. Notably, he translates academic ML methods into practical frameworks for industrial research problems, bridging theory and production.
11 years of coding experience
2 years of employment as a software developer
Bachelor of Science - BS, Mathematics and Computer Science, 3.94, Bachelor of Science - BS, Mathematics and Computer Science, 3.94 at Morehead State University
Master of Science - MS, Computer Science, 3.93, Master of Science - MS, Computer Science, 3.93 at The Ohio State University
Contributions:56 commits, 49 pushes in 4 years 10 months
ruby-on-railsadminrailsrails-engineruby
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