Research
My research program is organized around four themes: Learning-driven Evolutionary Optimization, Neuro-Symbolic Knowledge Discovery and Multi-Physical Simulation, AI for Science, and Other Topics.
Learning-driven Evolutionary Optimization
Integrating learning and transfer into evolutionary search to improve adaptation, convergence, and robustness in dynamic and high-dimensional optimization.
Neuro-Symbolic Knowledge Discovery and Multi-Physical Simulation
Using symbolic discovery, neural-symbolic methods, and physics-aware models to uncover governing structure and simulate complex coupled systems.
AI for Science
Applying AI to scientific discovery, scientific modeling, and complex simulation, with emphasis on differentiable modeling, physics-informed learning, and robust inference.
Other Topics
Applied AI, engineering decision support, and cross-cutting methodological work that sits outside the three core research directions.