Yu-hsuan Teng is a postdoctoral researcher at the University of Maryland with nine years of experience applying Python and remote Linux workflows to astrophysical problems. They specialize in radiative transfer and Bayesian modeling to probe the interstellar medium and star formation in nearby galaxies, combining theoretical rigor with practical coding. Yu-hsuan earned a Ph.D. in Physics from UC San Diego after an MS in Physics and a BS in Electrical Engineering from National Taiwan University, blending hardware-minded engineering intuition with advanced astrophysics. Their work bridges data-intensive modeling and observational interpretation, often developing reproducible analysis pipelines for large datasets. Comfortable in collaborative academic settings, they have experience teaching and mentoring graduate students while contributing to research projects across institutions in Taiwan and the U.S. An understated strength is their fluency moving between low-level computational environments and high-level probabilistic inference, enabling both scalable analyses and careful physical interpretation.
9 years of coding experience
4 years of employment as a software developer
University of California, San Diego
Master's degree, Physics, Master's degree, Physics at National Taiwan University
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