Research
My research program is organized around several interconnected themes, each addressing fundamental challenges at the intersection of computational intelligence, optimization, and scientific AI.
Learning-driven Optimization
Enhancing evolutionary algorithms with machine learning to achieve faster convergence, better generalization, and adaptive behavior in complex optimization scenarios.
Evolutionary Computation for Dynamic Complex Systems
Developing evolutionary algorithms that can track moving optima and adapt to changing environments in real-time decision-making scenarios.
Scientific AI and Intelligent Simulation
Applying AI to scientific discovery and simulation, with emphasis on differentiable simulation, physics-informed learning, and AI for science.
Computational Intelligence: From Results to Insight
Advancing the field from performance-oriented benchmarking toward deeper scientific understanding of why and how computational intelligence methods work.
AI for Engineering and Real-world Decision Making
Translating computational intelligence research into practical solutions for engineering optimization, intelligent control, and real-world decision-making.