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Assistant Professor Chou,Yuan-Tang

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Assistant Professo Yuan-Tang  Chou 
Yuan-Tang Chou       
TEL:31273(General Building III, R508 )
E-mail: yuan-tang.chou@cern.ch
Research Area: Experimental High Energy Physics。
Personal website: https://ytchoutw.github.io/

 

 


 


  Educatio
  1. Ph.D. in Physics, University of Massachusetts Amherst, Amherst, MA (2017/9–2023/8)
  2.  Bachelor of Science in Physics, National Tsing Hua University, Hsinchu, Taiwan (2011–2015)
  Current Position
Assistant Professor, Department of Physics, National Tsing Hua University (2026–present)
  Current Positione
Postdoctoral Scholar, Department of Physics, University of Washington, Seattle, WA (2023/9–2026/08)
  Awards and Honors
  1. Yunshan Young Fellow, Ministry of Education (MOE), Taiwan (2026) (2026)
  Research Areas
  1. Experimental particle physics
  2. AI for science and Machine learning
  3. High-performance computing
  Research Interests
Updated on July 1, 2026

Research Interests
My research combines experimental particle physics with modern artificial intelligence methods, primarily within the ATLAS experiment at CERN. This includes searches for new physics using machine learning, the development of deep-learning algorithms for particle identification and reconstruction, and building GPU AI inference infrastructure to support the High-Luminosity LHC (HL-LHC) upgrade. More recently, I have also worked on developing a foundation model for collider physics data, aiming to improve how we do physics data analysis in HEP .

 
  Selected Publications
  1. A. Saha, S. Govil, S. Miao, P. Li, Y.-T. Chou, A. Anand, S.-C. Hsu, J. Rodgers, M. Liu. Efficient Point Transformer for Charged Particle Track Reconstruction. NeurIPS 2025 · Machine Learning and the Physical Sciences Workshop (arXiv:2510.07594 [hep-ex]) 
  2. T.-H. Hsu, Y. Zhang, B.-H. Zhou, W.-P. Wang, Q. Liu, Y.-T. Chou, V. Mikuni, Y. Xu, S. Li, B. Nachman, and S.-C. Hsu. EveNet: Towards a Generalist Event Transformer for Unified Understanding and Generation of Collider Data (arXiv:2601.17126 [hep-ex]).
  3. E. Campolongo, Y.-T. Chou, E. Govorkova, W. Bhimji, W.-L. Chao, C. Harris, S.-C. Hsu et al. (2025). Elevating Citizen Science Opportunities to Advance Machine Learning: A Case Study in Anomaly Detection. (arXiv:2503.02112 [cs.LG])。
  4. H. Zhao, Y.-T. Chou, Y. Yao, X. Ju, Y. Feng et al. (2025). Track Reconstruction as a Service for Collider Physics. JINST, 20 (2025) 06, P06002(arXiv:2501.05520 [physics.ins-det])。
  5. ATLAS Collaboration (2025). Search for decays of the Higgs boson into scalar particles decaying into four or six b-quarks using pp collisions at √s = 13 TeV with the ATLAS detector. Phys. Rev. D 112 (2025) 072005(arXiv:2507.01165 [hep-ex])。
  6. ATLAS Collaboration (2025). Search for a new pseudoscalar decaying into a pair of bottom and antibottom quarks in top-associated production in √s = 13 TeV proton-proton collisions with the ATLAS detector. Eur. Phys. J. C, 85 (2025) 886(arXiv:2503.17254 [hep-ex])。
  7. ATLAS Collaboration (2025). Combination of searches for singly and doubly charged Higgs bosons produced via vector-boson fusion in proton–proton collisions at √s = 13 TeV with the ATLAS detector. Phys. Lett. B, 860 (2025) 139137(arXiv:2407.10798 [hep-ex])。
  8. ATLAS Collaboration (2024). Search for decays of the Higgs boson into a pair of pseudoscalar particles decaying into bb̄τ⁺τ⁻ using pp collisions at √s = 13 TeV with the ATLAS detector. Phys. Rev. D, 110 (2024) 5, 052013(arXiv:2407.01335 [hep-ex])。
 
 
 

 

 
 

 

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