🎓 I am a PhD student at Nanyang Technological University (NTU), Singapore, in the College of Computing and Data Science (CCDS), supervised by Prof. Yingzhen Li. I began my PhD in August 2026, supported by the NTU Research Scholarship (RSS). I graduated with Distinction from the MSc in ACSE (Applied Computational Science and Engineering) at Imperial College London. Before this, I earned a BSc in Applied Mathematics from the TYUT (Taiyuan University of Technology), graduating with a GPA of 4.5/5.0 (ranked 2/40). During my BSc and MSc, I focused primarily on physics-inspired and scientific machine learning, with particular interests in operator learning, Koopman theory, and representation learning for dynamical systems.
🔍 Currently, I have been increasingly interested in how latent dynamics, causal inference, reinforcement learning, and world models can come together to enable machines to predict, reason about, and act in complex environments.
Recent Updates
Aug 2026: I began my PhD at Nanyang Technological University, Singapore, supported by the NTU Research Scholarship (RSS).
2026: How Does the Lagrangian Guide Safe Reinforcement Learning through Diffusion Models? was accepted as a poster at the International Conference on Machine Learning (ICML) 2026.
We stabilize diffusion policies for safe reinforcement learning through augmented Lagrangian guidance.Jan 2026: Information Shapes Koopman Representation was accepted for an oral presentation at the International Conference on Learning Representations (ICLR) 2026.
We balance simplicity and expressiveness to learn stable, interpretable Koopman representations.