معرفی
Quanling Deng is a Lecturer in the School of Computing at the Australian National University (ANU), where he focuses on applied mathematics, computational methods, and machine learning. Previously, he held positions as a Van Vleck Visiting Assistant Professor at the University of Wisconsin-Madison (2020–2022) and a Research Associate at Curtin University (2016–2020). He earned his Ph.D. in Mathematics from the University of Wyoming in 2016 and has conducted visiting research at institutions including INRIA (Paris), AGH University (Krakow), and École des Ponts ParisTech.
Education:
- Ph.D. in Mathematics, University of Wyoming, 2016
- Moved to the USA in 2011 to pursue studies in mathematics
His research interests span Applied Mathematics (e.g., sea ice dynamics, ocean/atmosphere systems), Computational Mathematics (finite element methods, isogeometric analysis), and Machine Learning (feature interaction, deep neural networks). He also investigates Data Assimilation techniques, including stochastic models and Lagrangian-Eulerian frameworks.
Research Trends: His recent work emphasizes multiscale modeling (e.g., sea ice floes), eigenvalue problem solutions using advanced finite element techniques, and explainable machine learning. He explores applications of physics-informed neural networks and parallel computing for high-performance simulations.
Grants & Projects:
- Leading the project "Advancing Numerical Computation for Schrödinger Eigenvalue Problems" (2023)
Affiliations: Previously affiliated with Curtin Institute for Computation and Curtin TIGeR. Collaborates internationally on computational mathematics and climate modeling.
