Welcome

Prior to Ph.D., I received my bachelor's degree in Mathematics from NTU, with a minor in Finance, and a minor in Computing and Data Analysis. My undergraduate thesis was with Asst. Prof. Julian Sester and Asso. Prof. Ariel Neufeld.

I am broadly interested in quantitative research, as well as theoretical and applied deep learning, including recent advances in diffusion models and LLMs.

Just drop me an email if you are interested to connect! YIND0004@e.ntu.edu.sg

Publication

A global structure-preserving kernel method for the learning of Poisson systems (Journal of Nonlinear Science, 2025) with Jianyu Hu and Juan-Pablo Ortega

A structure-preserving kernel method for learning Hamiltonian systems (Mathematics of Computation, 2025) with Jianyu Hu and Juan-Pablo Ortega

Learnability of linear port-Hamiltonian systems (Journal of Machine Learning Research, 2024) with Juan-Pablo Ortega

Detecting data-driven robust statistical arbitrage strategies with deep neural networks (SIAM Journal on Financial Mathematics, 2024) with Julian Sester and Ariel Neufeld

Expressiveness and Structure Preservation in Learning Port-Hamiltonian Systems (International Conference on Geometric Science of Information, 2023) with Juan-Pablo Ortega

A kernel-based global method for the learning of elastic potentials on Lie groups  (International Conference on Geometric Science of Information, 2025) with Domenico Campolo, Jianyu Hu and Juan-Pablo Ortega

Performance Optimization for Federated Person Re-Identification via Benchmark Analysis (ACM-Multimedia, Oral, 2020) with Weiming Zhuang, etc.

Preprint

A kernel method for the learning of Wasserstein geometric flows with Jianyu Hu and Juan-Pablo Ortega

Ph.D. Thesis

Structure-preserving machine learning paradigm for hamiltonian systems