Yuan-Sen Ting
Physics and Astronomy · The Ohio State University
Publications
369
Citations
7,295
Est. group size
—
Recurring co-author estimate
Active years
21
Publishing since 2006
Yuan-Sen Ting studies the structure, formation, and chemical composition of the Milky Way and nearby galaxies, using large surveys of stars (like Gaia, LAMOST, and APOGEE) combined with machine learning and statistical modeling techniques. A significant part of his recent work also focuses on developing and applying artificial intelligence tools, including large language models, to interpret astronomical data and explores broader questions about how AI is changing scientific practice in astronomy.
Publication output grew substantially from 2017 through 2024, then leveled off at a high rate (around 50 papers/year) in 2025, indicating a period of strong sustained growth followed by stabilization.
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- Most canopy disturbance in Sarawak is partial, widespread, and invisible to global forest-change products: evidence from a calibrated 10 m Sentinel-2 archive (2015--2024)
SSRN Electronic Journal · 2026
- Why Machine Learning Models Systematically Underestimate Extreme Values II: How to Fix It with LatentNN
The Open Journal of Astrophysics · 2026
- What Understanding Means in AI-Laden Astronomy
ArXiv.org · 2026
- What Understanding Means in AI-Laden Astronomy
arXiv (Cornell University) · 2026
- The future of artificial intelligence and the mathematical and physical sciences (AI+MPS) <sup>*</sup>
Machine Learning Science and Technology · 2026
- Teaching Astronomy with Large Language Models
The Open Journal of Astrophysics · 2026
- Mephisto: Self-improving Large Language Model–based Agents for Automated Interpretation of Multiband Galaxy Observations
The Astrophysical Journal Supplement Series · 2026
- The Milky Way–Large Magellanic Cloud interaction with simulation-based inference
Monthly Notices of the Royal Astronomical Society · 2026
- Towards Unveiling the Origins of the Milky Way Bulge through Multi-band-Messenger Sky Surveys
arXiv (Cornell University) · 2025
- Towards Understanding the Milky Way's Matter Field and Dynamical Accretion History based on AI-GS3 Hunter
arXiv (Cornell University) · 2025
- High-Precision Differential Radial Velocities of C3PO Wide Binaries: A Test of Modified Newtonian Dynamics (MOND)
arXiv (Cornell University) · 2025
- Towards Unveiling the Origins of the Milky Way Bulge through Multi-band-Messenger Sky Surveys
arXiv (Cornell University) · 2025
- High-Precision Differential Radial Velocities of C3PO Wide Binaries: A Test of Modified Newtonian Dynamics (MOND)
arXiv (Cornell University) · 2025
- Towards Understanding the Milky Way's Matter Field and Dynamical Accretion History based on AI-GS3 Hunter
arXiv (Cornell University) · 2025
- Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform
arXiv (Cornell University) · 2025
- arXiv (Cornell University)×131
- Monthly Notices of the Royal Astronomical Society×66
- The Astrophysical Journal×41
- The Astrophysical Journal Supplement Series×17
- VizieR Online Data Catalog×9
- Richard W. Pogge
Physics and Astronomy · The Ohio State University
- Liam O. Dubay
Physics and Astronomy · The Ohio State University
- Byeong-Gon Park
Physics and Astronomy · The Ohio State University
- Andrew Gould
Physics and Astronomy · The Ohio State University
- M. Scalco
Physics and Astronomy · Indiana University
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Jul 19, 2026.
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