Biography
Shu Tian Eu obtained her PhD in Theoretical High Energy Physics from the University of Wisconsin–Madison in 2025. Her research lies at the intersection of particle physics and machine learning, with collaborations spanning institutions in the United States and Asia. She specializes in beyond-the-Standard-Model (BSM) physics, with a particular focus on leveraging reinforcement learning and related computational approaches to explore complex theory spaces.
Her recent work centers on the use of reinforcement learning for automated BSM model discovery, as well as inverse design problems in materials physics, including perovskites. She is currently an Assistant Professor at Xiamen University Malaysia.
Research Interest
Theoretical High Energy Physics: Exploring physics beyond the Standard Model through model building and phenomenology, with possible applications of various machine learning techniques.
AI-Driven Inverse Design in Materials Physics: Developing reinforcement learning frameworks for the inverse design of functional materials such as perovskite.
Physics in Data and Education
Educational Background
- PhD: Theoretical Particle Physics, University of Wisconsin-Madison, USA (2025)
- BSc: Physics, Nanyang Technological University, Singapore (2016)
Working Experience
- Xiamen University Malaysia (Assistant Professor, since 2026)
- University of Wisconsin-Madison (Graduate Teaching Assistant, 2020 – 2024)
- University of Wisconsin-Madison (Graduate Research Assistant, 2018 – 2025)
- Nanyang Technological University (Project Officer, 2016 – 2018)
Research Experience
- Particle Physics Model Building with Deep Reinforcement Learning
- Models Building of Lepton Flavor Portal Matter
- Flavored Gauge-Mediated Supersymmetry Breaking Models
Representative Publications
- Wojcik, G. N., Eu, S. T., & Everett, L. L.* (2025). Graph reinforcement learning for exploring model spaces beyond the Standard Model. Physical Review D, 111, 035007.
- Wojcik, G. N., Everett, L. L.*, Eu, S. T., & Ximenes, R. (2023). Lepton flavor portal matter. Physical Review D, 108, 055033.
- Wojcik, G. N., Everett, L. L.*, Eu, S. T., & Ximenes, R. (2023). Portal matter, kinetic mixing, and muon g-2. Physics Letters B, 841, 137931.
- Eu, S. T., Everett, L. L.*, Garon, T., & Leonard, N. (2022). Flavored gauge-mediated supersymmetry breaking models with discrete non-Abelian symmetries. Physical Review D, 105, 055019.
- Wojcik, G. N., Eu, S. T., & Everett, L. L.* (2024). Towards beyond-Standard-Model model building with reinforcement learning on graphs. arXiv:2407.07184 [hep-ph].
Honors / Awards
- Dillinger Award for Teaching Excellence 2023-24: Physics Department, University of Wisconsin-Madison.
- Elizabeth S. Hirschfelder Award 2021-22: Physics Department, University of Wisconsin-Madison.
- Teaching Award Fall 2022: Physics Department, University of Wisconsin-Madison.
- Jeff& Lily Chen Wisconsin Distinguished Graduate Fellowship 2018-20: University of Wisconsin-Madison.
SNAS Award 2016: Singapore National Academy of Science - ASEAN Scholarship, CNYang Scholars Programme