Summary
Ting Xiao is an assistant professor and data scientist with a Ph.D. in Experimental Particle Physics and nine years of experience turning noisy, large-scale measurements into statistically robust discoveries. She has led precision analyses that improved muon g-2 hadronic contribution uncertainty and contributed to first-of-their-kind timelike form factor and hyperon production measurements, translating those rigorous statistical and ML techniques into sensor analytics and data curation for interdisciplinary applications. At UNT she holds joint appointments bridging data science, computer science, and information science, teaching and developing graduate courses in AI, big data, and information retrieval. Comfortable moving between code, models, and domain experiments, she combines particle-physics–grade uncertainty quantification with practical machine/deep learning system building for real-world sensors. An undeclared but recurring strength is her ability to extract weak signals from overwhelming noise—a skill she now applies beyond physics to applied analytics and engineering problems.
9 years of coding experience
14 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Experimental Particle Physics, Doctor of Philosophy (Ph.D.), Experimental Particle Physics at Northwestern University
Bachelor of Science (BS), Physics, Bachelor of Science (BS), Physics at Zhejiang University