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About
Hi! I am a Ph.D. student advised by Prof. Shao-Kai Jian and Prof. Lev Kaplan at the Department of Physics and Engineering Physics, Tulane University, USA.
Previously, I studied as a master at the School of Physics, Zhejiang University and was fortunately advised by Prof. Lih-King Lim. During this time, my research centred on topological phases in nonperiodic order, revealing fractal surface states in quasicrystals.
Prior to that, I studied at the School of Energy and Power, Guangdong University of Technology, where my curiosity led me to transition from engineering to theoretical physics through research with Prof. Kaige Hu in condensed matter theory.
Research Interests
My long-term research goal is to understand when and why machine learning can faithfully capture complex dynamics—and how its predictions can be verified. I am particularly interested in three directions:
Scaling Laws and Learnability
How do prediction accuracy, data requirements, and computational cost scale with system size, time horizon, model capacity, and dynamical complexity?Neural Representations of Dynamics
What do neural networks learn about a dynamical system, and how are their internal representations connected to its underlying physical structure?Reliable Prediction and Verification
How can we predict dynamics beyond directly accessible numerical regimes while quantifying uncertainty and testing predictions against physical constraints?
Quantum many-body dynamics provides the primary physical setting for these questions. My current research focuses on operator growth, scrambling, and relaxation far from equilibrium, using tensor networks, exact diagonalization, quantum Monte Carlo, large-N methods.
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