14 citations · 19 across the 9 of their papers we have counts for
7 papers · 1 filter
Deep Learning for Astrophysics: An Open Textbook from the NASA Cosmic Origins AI/ML Science and Technology Interest Group
Yuan-Sen Ting, Digvijay Wadekar, Phill Cargile +21
Recent community assessments identify education as a principal barrier to adopting modern machine learning in astronomy. We present Deep Learning for Astrophysics, a freely availab…
Your Outie Is a Wonderful Astronomer: Macrodata Refinement of the Astro-ph ArXiv Feed at Phermon Industries
Yuan-Sen Ting
We present the Severed Floor, a framework for Macrodata Refinement of the daily astro-ph arXiv feed, deployed at Phermon Industries (formerly McPherson Laboratory, The Ohio State U…
What Understanding Means in AI-Laden Astronomy
Yuan-Sen Ting, André Curtis-Trudel, Siyu Yao
Artificial intelligence is rapidly transforming astronomical research, yet the scientific community has largely treated this transformation as an engineering challenge rather than…
Why Machine Learning Models Systematically Underestimate Extreme Values II: How to Fix It with LatentNN
Yuan-Sen Ting
Attenuation bias -- the systematic underestimation of regression coefficients due to measurement errors in input variables -- affects astronomical data-driven models. For linear re…
Deep Learning in Astrophysics
Yuan-Sen Ting
Deep learning has generated diverse perspectives in astronomy, with ongoing discussions between proponents and skeptics motivating this review. We examine how neural networks compl…
Statistical Machine Learning for Astronomy -- A Textbook
Yuan-Sen Ting
This textbook provides a systematic treatment of statistical machine learning for astronomical research through the lens of Bayesian inference, developing a unified framework that…
Why Machine Learning Models Systematically Underestimate Extreme Values
Yuan-Sen Ting
A persistent challenge in astronomical machine learning is a systematic bias where predictions compress the dynamic range of true values-high values are consistently predicted too…