Research interests

My research focuses on advancing the scientific foundations and computational capabilities of modern nuclear reactor analysis. I work at the intersection of neutron transport theory, reactor physics, multiphysics modeling, optimization, and scientific machine learning. My group develops both fundamental theory and practical tools aimed at enabling safer, more efficient, and more innovative nuclear energy systems.

Neutron transport theory and computational methods

I investigate the mathematical and physical foundations of neutron transport and develop state-of-the-art deterministic and stochastic methods for solving the transport equation. This includes discrete ordinates solvers, multigroup diffusion approximations, and high-efficiency Monte Carlo techniques. A central focus of my work is the development of advanced quasi-static formulations and accelerated solution strategies capable of handling complex, heterogeneous reactor geometries and transient scenarios with high fidelity.

Reactor core physics and fuel management optimization

My research includes the development of optimization frameworks for in-core fuel management and reactor core design. Using adjoint-based sensitivity analysis and evolutionary algorithms, I focus on loading pattern optimization, burnup shaping, and strategies that enhance fuel utilization while respecting operational limits and ensuring robust safety margins. These methods combine physical insight with modern numerical optimization to support next-generation core design methodologies.

Advanced reactor concepts and multiphysics analysis

I work on neutronics and safety analysis of Generation IV reactor concepts, with particular emphasis on Small Modular Reactors (SMRs), microreactors, and advanced thermal-spectrum systems. My group develops high-fidelity multiphysics models that couple neutron transport, thermal hydraulics, and control logic. These tools support transient analysis, feedback evaluation, and design exploration of novel reactor architectures.

Experimental reactor physics

My research also engages with experimental programs in reactor physics. I contribute to the design and interpretation of zero-power experiments, neutron noise studies, and the development of specialized detectors. Collaborative work with partners abroad supports benchmarking, model validation, and the integration of experimental data into computational workflows.

Machine learning and AI for reactor physics

I explore the integration of machine learning into reactor analysis through physics-informed neural networks (PINNs), hybrid CNN-PINN architectures, and data-driven surrogate models. These tools aim to accelerate transport calculations, reconstruct neutron flux distributions, enhance parameter estimation, and support real-time or reduced-order modeling of complex reactor systems.