Dr. Jin Song is a promising mathematician specializing in applied mathematics with a strong interdisciplinary orientation that bridges nonlinear science and artificial intelligence. His research centers on the analysis, simulation, and prediction of complex nonlinear systems, with particular emphasis on nonlinear wave equations and partial differential equations that describe solitons, vortices, rogue waves, and other coherent structures. Dr. Song has developed advanced mathematical and computational frameworks to study the dynamical behavior, stability, and evolution of such systems, combining rigorous mathematical theory with high-level numerical methods. A distinctive aspect of his work is the integration of modern machine learning techniques into mathematical modeling, especially through physics-informed machine learning and neural operator architectures that embed physical constraints directly into data-driven models. This approach enables more accurate, stable, and interpretable solutions to high-dimensional and nonlinear problems. His research also extends to generative modeling for partial differential equations, where he explores how physical structure and integrability can be incorporated as inductive biases to ensure physically consistent solution manifolds. Dr. Song’s work demonstrates both theoretical depth and methodological innovation, contributing to advances in nonlinear dynamics, computational mathematics, and scientific machine learning. In addition to his research contributions, he is actively engaged in academic collaboration, peer review, and knowledge dissemination through seminars and conferences. He possesses strong technical expertise in mathematical analysis, scientific computing, and programming, allowing him to tackle complex interdisciplinary challenges. Dr. Song’s research philosophy emphasizes the unification of mathematics, physics, and artificial intelligence to address fundamental and applied problems in modern science. Through his innovative research direction, interdisciplinary impact, and commitment to mathematical excellence, Dr. Jin Song stands out as a deserving recipient of the Research Excellence Award.

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