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Draft:Yuan-Sen Ting

From Wikipedia, the free encyclopedia
  • Comment: Unlike the previous reviewer I am not convinced by the citations as too many of them are team survey efforts. None of the awards are senior, so my feeling is that this is WP:TOOSOON. An additional and major problem is that this is written like a resume/essay extolling his own achievements. Way too many citations to your own work, at most the 10 most significant. Remove all boasting phrases such as "the author of more than 240 papers" or "one of the first methods". Also remove everything that is routine for academics such as giving talks, getting funding etc. Ldm1954 (talk) 12:30, 14 July 2026 (UTC)
  • Comment: This is a bit too obviously AI assisted to be accepted in this form. Even for formatting, AI / LLM is banned. See WP:NEWLLM. That said, given the subject's h-index, it appears they have a good case under WP:NACADEMIC criteria 1. So kindly redo this article, manually editing, and perhaps add in one brief paragraph, paragraph 2, that summarises impact of their work. Even better if some other academic has written about the subject's work either positively or even in negative terms. Also the lead paragraph is Rule of Three, so again manually reword that as a human being please. ChrysGalley (talk) 07:28, 6 June 2026 (UTC)
Yuan-Sen Ting
丁源森
Born
Malaysia
CitizenshipMalaysia
Alma mater
Known for
  • The Payne (stellar-spectra fitting)
  • AstroMLab / AstroSage language models
Awards
Scientific career
Fields
Institutions
Charlie Conroy
Websitewww.ysting.space

Yuan-Sen Ting (Chinese: 丁源森) is a Malaysian astrophysicist and an associate professor of astronomy at The Ohio State University.[1][2] His research applies statistical inference and machine learning to problems in astrophysics, ranging from the engulfment of planets by stars and stellar spectroscopy to the evolution of the Milky Way and inference in cosmology.[1][3] He also works on large language models as autonomous agents for scientific discovery and on representation learning for astronomical foundation models.[3][2]

Ting was invited to write the review "Deep Learning in Astrophysics" for the Annual Review of Astronomy and Astrophysics, and he led the astronomy contribution to a National Science Foundation white paper on the future of artificial intelligence in the mathematical and physical sciences.[3][4] He is the author of more than 240 papers, and according to Google Scholar his work had been cited more than 12,000 times as of 2026.[5][6]

Early life and education

[edit]

Ting grew up in Malaysia.[2][5] He completed concurrent bachelor's and master's studies in physics, with a minor in mathematics, through a joint programme of the National University of Singapore and the École Polytechnique in France.[5][7] During this period he made research visits to the University of Oxford, working with Joseph Silk; to the Australian National University, with Kenneth Freeman; and to the Max Planck Institute for Astronomy, with Hans-Walter Rix.[7] He then moved to Harvard University, where he received a Master of Arts and, in 2017, a Ph.D. in astronomy and astrophysics under the supervision of Charlie Conroy; his doctoral work was supported by a NASA Earth and Space Science Fellowship.[5][7]

Career

[edit]

From 2017 to 2021, Ting held a Carnegie–Princeton Fellowship together with a NASA Hubble Fellowship, an appointment shared across the Institute for Advanced Study, Princeton University and the Carnegie Observatories.[7][8] He was named a NASA Hubble Fellow in 2018 for a project on chemically tagging the Milky Way.[9][10]

In 2021 he joined the Australian National University (ANU) with a joint appointment in astrophysics and computer science, and he was promoted to associate professor in 2022.[11][7] In the same period he received an Australian Research Council Discovery Early Career Researcher Award (DECRA).[12] Since 2024 he has been an associate professor in the Department of Astronomy at The Ohio State University, where he is a faculty associate of the Center for Cosmology and AstroParticle Physics.[1][13] In 2026 he founded CASPER (Computational and Agentic Scientific Practices, Epistemology, and Reasoning), an initiative on the epistemic implications of astronomical research and on agentic scientific practice.[1][5] He has held an appointment as an adjunct scientist at the Max Planck Institute for Astronomy since 2024, and was awarded an Alexander von Humboldt Fellowship.[5][7]

Ting is the inaugural chair of NASA's AI/ML Science and Technology Interest Group, which supports training in AI methods across the astronomical community, and previously served as the inaugural chair of NASA's Stars Science Interest Group.[14][5][15]

Research

[edit]

Ting's research focuses on questions in astronomy that can be advanced with statistical and machine-learning methods, and it spans stellar spectroscopy, galaxy evolution, exoplanet science and cosmology, as well as the use of artificial intelligence in the physical sciences.[1][3]

Stellar spectroscopy

[edit]

During his doctoral work, Ting and his collaborators developed "The Payne", one of the first methods to use neural networks to emulate the output of stellar radiative transfer models, making it possible to measure the physical parameters and chemical abundances of stars directly from their spectra.[16] He showed that, even at low spectral resolution, modelling the full spectrum rather than only isolated unblended features can recover detailed chemical abundances.[16] The approach has been used to derive stellar abundances from large spectroscopic surveys, including a catalogue of 16 elements for about six million stars from LAMOST.[17]

Exoplanets and planetary engulfment

[edit]

Ting is a senior author of a 2024 study, part of the C3PO (Complete Census of Co-moving Pairs of Stars) programme, that analysed stars born together as "co-natal" pairs.[18] By comparing the chemical compositions of the two stars in each pair, the study found that at least about one in twelve Sun-like stars shows chemical evidence of having engulfed a planet, indicating that planetary engulfment is relatively common.[18] The work was published as a cover article in Nature and was widely reported in the science and general press.[19][20][21][22][23]

Galaxy evolution

[edit]

Ting studies the formation and dynamical evolution of the Milky Way. He has worked on weak chemical tagging, which uses the chemical composition of groups of stars to probe the conditions of star formation in the early Galaxy, and on the vertical heating of the Galactic disk, using the age-dependent vertical motions of stars to trace how the disk has thickened over time.[24][1] With Neige Frankel, a student he co-supervised at the Max Planck Institute for Astronomy, he studied radial migration in the disk;[25] a 2020 paper from that work, on which he was a co-author, received the institute's Ernst Patzer Prize.[26][27]

With his collaborator Gregory Green, Ting developed "Deep Potential", a method that uses a deep-learning normalizing flow to recover the Galactic gravitational potential from the phase-space distribution of stars without assuming an analytic form.[28]

Interstellar medium

[edit]

Ting is interested in how turbulence in the interstellar medium is imprinted on the chemical composition of stars, and in how weak chemical tagging can be used to infer how elements are transported through that medium. With Mark Krumholz he developed an analytic model, based on stochastically forced diffusion, for the statistics of metallicity fluctuations in the interstellar medium and young stars.[29]

Cosmology

[edit]

A 2020 paper led by Ting's student Sihao Cheng, with Brice Ménard, Joan Bruna and Ting as co-authors, introduced the wavelet scattering transform as a summary statistic for observational cosmology, a way to capture non-Gaussian information beyond the power spectrum.[30] The method has since been taken up by several groups in cosmology, including for weak gravitational lensing analyses.[30][31]

Machine-learning methodology and language models

[edit]

Ting also studies machine learning as a subject within astronomy. He has worked on representation learning for stellar spectroscopy, showing that the neural scaling laws seen in Transformer-based large language models also carry over to Transformer-based emulation of stellar spectra.[32]

He leads work on domain-specialised large language models for astronomy. AstroLLaMA (2023), on which he was a senior author, was an early domain-adapted language model for the field, fine-tuned on astronomy abstracts.[33] The later open-source AstroMLab and AstroSage models are reported to reach the performance of contemporary general-purpose models on astronomy question-answering benchmarks.[34] His group also develops research agents and literature-based tools for astronomy, including a knowledge graph of astronomical concepts built with large language models, methods for predicting new concept–object associations by mining the research literature, and Mephisto, an agent for interpreting multi-band galaxy observations.[35][36][37]

In 2026 Ting was the sole author of an invited review, "Deep Learning in Astrophysics", in the Annual Review of Astronomy and Astrophysics.[3] He was the astronomy representative on a National Science Foundation white paper on the future of artificial intelligence in the mathematical and physical sciences.[4] He has also written on the epistemology of AI-driven science: a 2026 Nature Astronomy perspective, co-authored with two philosophers of science, examined what scientific "understanding" means when AI takes part in the inference process, a theme he pursues through CASPER.[38][1]

Books

[edit]
  • Statistical Machine Learning for Astronomy (2025), a graduate-level textbook released openly on arXiv.[39]
  • Deep Learning for Astrophysics (2026), a community textbook Ting curated from a lecture series run through NASA's AI/ML Science and Technology Interest Group.[40]

Public engagement

[edit]

Ting is active in science communication, particularly in Malaysia and Southeast Asia. He created two lessons for TED-Ed, on measuring distances in space and on the study of stars, which have together been viewed several million times,[41][42] gave a TEDx talk in Kuala Lumpur in 2023,[43] and wrote a recurring column on astronomy and artificial intelligence for Sin Chew Daily.[44] In 2023 he chaired International Astronomical Union Symposium 377, held in Kuala Lumpur, the first IAU symposium hosted in Southeast Asia since 1990.[45] In the same year he was a lead organiser of the Global Malaysian Astronomy Convention, the first convention to bring together the local and international Malaysian astronomy community.[46]

Awards and honours

[edit]

Selected publications

[edit]
  • Ting, Yuan-Sen; Conroy, Charlie; Rix, Hans-Walter; Cargile, Phillip (2019). "The Payne: Self-consistent ab initio fitting of stellar spectra". The Astrophysical Journal. 879 (2): 69. arXiv:1804.01530. Bibcode:2019ApJ...879...69T. doi:10.3847/1538-4357/ab2331.
  • Ting, Yuan-Sen; Rix, Hans-Walter (2019). "The vertical motion history of disk stars throughout the Galaxy". The Astrophysical Journal. 878 (1): 21. arXiv:1808.03278. Bibcode:2019ApJ...878...21T. doi:10.3847/1538-4357/ab1ea5.
  • Cheng, Sihao; Ting, Yuan-Sen; Ménard, Brice; Bruna, Joan (2020). "A new approach to observational cosmology using the scattering transform". Monthly Notices of the Royal Astronomical Society. 499 (4): 5902–5914. arXiv:2006.08561. Bibcode:2020MNRAS.499.5902C. doi:10.1093/mnras/staa3165.
  • Liu, Fan; Ting, Yuan-Sen; Yong, David; et al. (2024). "At least one in a dozen stars shows evidence of planetary ingestion". Nature. 627 (8004): 501–504. arXiv:2403.13209. Bibcode:2024Natur.627..501L. doi:10.1038/s41586-024-07091-y. PMID 38509276.
  • Ting, Yuan-Sen (2026). "Deep Learning in Astrophysics". Annual Review of Astronomy and Astrophysics. arXiv:2510.10713. doi:10.1146/annurev-astro-051024-021708. (in press)
[edit]

References

[edit]
  1. 1 2 3 4 5 6 7 8 "Yuan-Sen Ting". Department of Astronomy, The Ohio State University. Retrieved 2026-07-11.
  2. 1 2 3 "Malaysia's stars: Yuan-Sen Ting explores how AI is transforming science". Office of International Affairs, The Ohio State University. 2025-10-15. Retrieved 2026-07-11.
  3. 1 2 3 4 5 Ting, Yuan-Sen (2026). "Deep Learning in Astrophysics". Annual Review of Astronomy and Astrophysics. arXiv:2510.10713. doi:10.1146/annurev-astro-051024-021708. (in press)
  4. 1 2 Ferguson, Andrew; Ting, Yuan-Sen; et al. (2026). "The future of artificial intelligence and the mathematical and physical sciences (AI+MPS)". Machine Learning: Science and Technology. 7 (2): 023001. arXiv:2509.02661. Bibcode:2026MLS&T...7b3001F. doi:10.1088/2632-2153/ae3e4e.
  5. 1 2 3 4 5 6 7 "Yuan-Sen Ting — Astrophysicist & AI Data Scientist". Retrieved 2026-07-11.
  6. ↑ "Yuan-Sen Ting". Google Scholar. Retrieved 2026-07-11.
  7. 1 2 3 4 5 6 7 8 Ting, Yuan-Sen. "Curriculum Vitae" (PDF). Retrieved 2026-07-11.
  8. 1 2 "Member Yuan-Sen Ting named NASA Hubble Fellow". Institute for Advanced Study. 2018. Retrieved 2026-07-11.
  9. 1 2 "2018 NHFP Fellows". Space Telescope Science Institute. Retrieved 2026-07-11.
  10. ↑ "NASA announces class of 2018 Sagan, Hubble and Einstein postdoctoral fellows". IPAC, Caltech. 2018. Retrieved 2026-07-11.
  11. ↑ "Yuan-Sen Ting". ANU School of Computing. Retrieved 2026-07-11.
  12. 1 2 "ANU early-career researchers win ARC funding". Australian National University. 2020. Retrieved 2026-07-11.
  13. ↑ "Yuan-Sen Ting". Center for Cosmology and AstroParticle Physics, The Ohio State University. Retrieved 2026-07-11.
  14. ↑ "NASA AI/ML Science and Technology Interest Group". Retrieved 2026-07-11.
  15. ↑ "Stars Science Interest Group (Stars SIG) — Leadership Council". NASA Cosmic Origins Program. 25 August 2025. Retrieved 2026-07-11.
  16. 1 2 Ting, Yuan-Sen; Conroy, Charlie; Rix, Hans-Walter; Cargile, Phillip (2019). "The Payne: Self-consistent ab initio fitting of stellar spectra". The Astrophysical Journal. 879 (2): 69. arXiv:1804.01530. Bibcode:2019ApJ...879...69T. doi:10.3847/1538-4357/ab2331.
  17. ↑ Xiang, Maosheng; Ting, Yuan-Sen; et al. (2019). "Abundance estimates for 16 elements in 6 million stars from LAMOST DR5 low-resolution spectra". The Astrophysical Journal Supplement Series. 245 (2): 34. arXiv:1908.09727. Bibcode:2019ApJS..245...34X. doi:10.3847/1538-4365/ab5364.
  18. 1 2 Liu, Fan; Ting, Yuan-Sen; Yong, David; et al. (2024). "At least one in a dozen stars shows evidence of planetary ingestion". Nature. 627 (8004): 501–504. arXiv:2403.13209. Bibcode:2024Natur.627..501L. doi:10.1038/s41586-024-07091-y. PMID 38509276.
  19. ↑ "Astrocomputing detectives uncover planet-eating stars". Australian National University. 2024-03-21. Retrieved 2026-07-11.
  20. ↑ "Study of twin stars finds some of them are planet eaters". Reuters. 2024-03-20. Retrieved 2026-07-11.
  21. ↑ "Planet-eating stars more common than previously thought, astrophysicists find". The Guardian. 2024-03-23. Retrieved 2026-07-11.
  22. ↑ "Planet-eating stars are surprisingly common, new study suggests". Scientific American. 2024. Retrieved 2026-07-11.
  23. ↑ "Planet cannibalism is common, says cosmic twin study". The Conversation. 2024. Retrieved 2026-07-11.
  24. ↑ Ting, Yuan-Sen; Rix, Hans-Walter (2019). "The vertical motion history of disk stars throughout the Galaxy". The Astrophysical Journal. 878 (1): 21. arXiv:1808.03278. Bibcode:2019ApJ...878...21T. doi:10.3847/1538-4357/ab1ea5.
  25. ↑ Frankel, Neige; Rix, Hans-Walter; Ting, Yuan-Sen; Ness, Melissa; Hogg, David W. (2018). "Measuring radial orbit migration in the Galactic disk". The Astrophysical Journal. 865 (2): 96. arXiv:1805.09198. Bibcode:2018ApJ...865...96F. doi:10.3847/1538-4357/aadba5.
  26. ↑ Frankel, Neige; Ting, Yuan-Sen; et al. (2020). "Keeping it cool: much orbit migration, yet little heating, in the Galactic disk". The Astrophysical Journal. 896 (1): 15. arXiv:2002.04622. Bibcode:2020ApJ...896...15F. doi:10.3847/1538-4357/ab910c.
  27. ↑ "Patzer Prize Award 2020". Max Planck Institute for Astronomy. 2020. Retrieved 2026-07-11.
  28. ↑ Green, Gregory M.; Ting, Yuan-Sen (2020). "Deep Potential: Recovering the gravitational potential from a snapshot of phase space". arXiv:2011.04673 [astro-ph.GA].
  29. ↑ Krumholz, Mark R.; Ting, Yuan-Sen (2018). "Metallicity fluctuation statistics in the interstellar medium and young stars – I. Variance and correlation". Monthly Notices of the Royal Astronomical Society. 475 (2): 2236–2252. arXiv:1708.06853. Bibcode:2018MNRAS.475.2236K. doi:10.1093/mnras/stx3286.
  30. 1 2 Cheng, Sihao; Ting, Yuan-Sen; Ménard, Brice; Bruna, Joan (2020). "A new approach to observational cosmology using the scattering transform". Monthly Notices of the Royal Astronomical Society. 499 (4): 5902–5914. arXiv:2006.08561. Bibcode:2020MNRAS.499.5902C. doi:10.1093/mnras/staa3165.
  31. ↑ Cheng, Sihao; Ménard, Brice (2021). "Weak lensing scattering transform: dark energy and neutrino mass sensitivity". Monthly Notices of the Royal Astronomical Society. 507 (1): 1012–1024. arXiv:2103.09247. Bibcode:2021MNRAS.507.1012C. doi:10.1093/mnras/stab2102.
  32. ↑ Różański, Tomasz; Ting, Yuan-Sen (2025). "Scaling Laws for Emulation of Stellar Spectra". The Open Journal of Astrophysics. 8: 69. arXiv:2503.18617. Bibcode:2025OJAp....8E..69R. doi:10.33232/001c.140607.
  33. ↑ Nguyen, Tuan Dung; Ting, Yuan-Sen; et al. (2023). "AstroLLaMA: Towards specialized foundation models in astronomy". arXiv:2309.06126 [cs.CL].
  34. ↑ de Haan, Tijmen; Ting, Yuan-Sen; et al. (2025). "AstroMLab 3: Achieving GPT-4o level performance in astronomy with a specialized 8B-parameter large language model". Scientific Reports. 15 (1): 13751. arXiv:2411.09012. doi:10.1038/s41598-025-97131-y. PMC 12012197. PMID 40258872.
  35. ↑ Sun, Zechang; Ting, Yuan-Sen; et al. (2024). "Knowledge graph in astronomical research with large language models: Quantifying driving forces in interdisciplinary scientific discovery". arXiv:2406.01391 [astro-ph.IM].
  36. ↑ Li, Jinchu; Ting, Yuan-Sen; et al. (2026). "Predicting new concept–object associations in astronomy by mining the literature". arXiv:2602.14335 [astro-ph.IM].
  37. ↑ Sun, Zechang; Ting, Yuan-Sen; et al. (2026). "Mephisto: Self-improving large language model-based agents for automated interpretation of multi-band galaxy observations". The Astrophysical Journal Supplement Series. 285: 28. arXiv:2510.08354. doi:10.3847/1538-4365/ae5d3a. (in press)
  38. ↑ Ting, Yuan-Sen; Curtis-Trudel, André; Yao, Siyu (2026). "What understanding means in AI-laden astronomy". Nature Astronomy. 10 (4): 468. arXiv:2601.10038. Bibcode:2026NatAs..10..468T. doi:10.1038/s41550-026-02809-6.
  39. ↑ Ting, Yuan-Sen (2025). "Statistical Machine Learning for Astronomy". arXiv:2506.12230 [astro-ph.IM].
  40. ↑ Ting, Yuan-Sen. "Deep Learning for Astrophysics". NASA AI/ML STIG Textbook. Retrieved 2026-07-11.
  41. ↑ Ting, Yuan-Sen. "How do we measure distances in space?". TED-Ed. Retrieved 2026-07-11.
  42. ↑ Ting, Yuan-Sen. "How do we study the stars?". TED-Ed. Retrieved 2026-07-11.
  43. ↑ "Seeing Humanity through Dystopian AI". TEDx Petaling Street. 2023. Retrieved 2026-07-11.
  44. ↑ Ting, Yuan-Sen (2023-08-30). "【代码之外】丁源森／AI、ChatGPT与我妈的扫地机器人". Sin Chew Daily (in Chinese). Retrieved 2026-07-11.
  45. ↑ "Spotlight on Malaysian astronomy at IAUS 377 in Kuala Lumpur". Astrobites. 2023-02-20. Retrieved 2026-07-11.
  46. ↑ "Global Malaysian Astronomy Convention (GMAC 2023) — Organizers". Global Malaysian Astronomy Convention. Retrieved 2026-07-11.

Category:Living people Category:Year of birth missing (living people) Category:Malaysian astronomers Category:Malaysian people of Chinese descent Category:Harvard University alumni Category:National University of Singapore alumni Category:École Polytechnique alumni Category:Ohio State University faculty Category:Australian National University faculty Category:Machine learning researchers Category:21st-century astronomers

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