In Da Nang, Vietnam, Sep 2025
Embedded Files
Welcome
Welcome
Hi! This is Daiying Yin (尹代英) from Nanyang Technological University (NTU), Singapore.
I am currently a final-year Ph.D. student in Applied Mathematics, working with Prof. Juan-Pablo Ortega.
My research interest lies in structure-preserving machine learning for physical dynamical systems, with tools from kernel methods, (port-)Hamiltonian system theory, etc. The goal is to utilize domain knowledge from physics to provide qualitatively and quantitatively better machine learning algorithms, aiming at uncovering the unknown system.
Prior to PhD, I received my bachelor's degree in Mathematics from NTU in 2021, with a minor in Finance, and also a minor in Computing and Data Analysis. My undergraduate thesis was with Asst. Prof. Julian Sester and Asso. Prof. Ariel Neufeld.
Besides academic research, I am broadly interested in quantitative research in finance, 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
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 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
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
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
Performance Optimization for Federated Person Re-Identification via Benchmark Analysis (ACM-Multimedia, Oral, 2020) with Weiming Zhuang, etc.
Performance Optimization for Federated Person Re-Identification via Benchmark Analysis (ACM-Multimedia, Oral, 2020) with Weiming Zhuang, etc.
Preprint
Preprint
A kernel-based global method for the learning of elastic potentials on Lie groups with Domenico Campolo, Jianyu Hu and Juan-Pablo Ortega
A kernel-based global method for the learning of elastic potentials on Lie groups with Domenico Campolo, Jianyu Hu and Juan-Pablo Ortega
Page updated
Google Sites
Report abuse