My research advances 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 advanced computational 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 solution methods. A central focus is the development of advanced quasi-static and generalized factorization methods, as well as accelerated solution strategies that accurately treat complex, heterogeneous reactor geometries and strongly transient scenarios.

• Reactor core physics and fuel management optimization

My research includes the development of physics-based optimization frameworks for in-core fuel management and reactor core design. Using adjoint-based sensitivity analysis and evolutionary and hybrid optimization algorithms, I address loading pattern optimization, burnup shaping, and fuel utilization strategies while enforcing operational constraints and robust safety margins. These approaches combine physical insight with modern numerical optimization to support next-generation core design methodologies.

• Reactivity control and inter-drum neutronic interactions in microreactors

A dedicated focus of my research is the reactivity control of microreactors employing rotating control drums. I investigate the strong spatial and spectral coupling between control drums and the reactor core, with particular emphasis on inter-drum neutronic interactions and their impact on reactivity control. This work addresses the breakdown of classical superposition assumptions in tightly coupled, compact cores, where control element interactions can be highly nonlinear.

• Advanced reactor concepts and multiphysics analysis

I conduct neutronic and safety analyses of advanced reactor systems, with emphasis on small modular reactors, microreactors, and advanced thermal-spectrum concepts. My group develops high-fidelity multiphysics models that couple neutron transport, thermal hydraulics, and control and feedback mechanisms. These tools enable transient analysis, feedback assessment, and design-space exploration of novel reactor architectures.

• Experimental reactor physics

My research also includes experimental reactor physics activities. I contribute to the design, execution, and interpretation of zero-power experiments, neutron noise measurements, and the development of specialized detector systems. Through collaborations with international partners, this work supports benchmarking, model validation, and the systematic integration of experimental data into computational and theoretical frameworks.

• Machine learning and AI for reactor physics

I explore integrating machine learning into reactor analysis using physics-informed neural networks, hybrid CNN-PINN architectures, and data-driven surrogate models. These methods aim to accelerate transport calculations, reconstruct neutron flux and power distributions, improve parameter estimation, and enable real-time or reduced-order modeling of complex reactor systems.