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Yuan-Sen Ting is an Associate Professor at the Australian National University, specializing in machine learning and large astronomical surveys. His research encompasses various domains including stellar astrophysics, star formation, galactic evolution, black holes, reionization, and cosmology. A central component of his methodology involves advanced Bayesian statistics and contemporary deep learning techniques, essential for analyzing vast survey datasets. He utilizes multiple data sources such as spectroscopy (SDSS-V, DESI, 4MOST), astrometry (Gaia), photometry (Euclid, Roman, CSST), and time-series data (LSST, TESS, PLATO). His work aims to address fundamental questions in the field by synthesizing machine learning techniques with astronomical data analysis. Additionally, he leads the UniverseTBD initiative, which focuses on improving existing machine learning models for better scientific insights in astronomy. By probing the statistical behaviors of neural networks, he aims to uncover mathematical principles relevant to astronomical research.
Requirements are standardized across most Master of Science and Arts programs within the College of Science and College of Arts & Social Sciences.