Advanced Reactor Design and Analysis esearch activities in this area encompass the design, modeling, and analysis of advanced reactor cores using state-of-the-art computational tools. Our research focuses on developing and applying multiphysics and multiscale modeling and simulation (M&S) approaches to investigate reactor physics, thermal-hydraulics, fuel performance, and safety characteristics, supporting the development and assessment of innovative nuclear reactor concepts. Research projects we have worked in the past years include: An accelerator driven liquid lead cooled thorium fueled fast reactor design and analysis using MCNPX/SCALE tools Core design studies for a BWR-type small modular reactor with long-life core using CASMO-3/PARCS/RELAP-5 LWR analysis code suites Reactor design and analysis of stationary liquid fuel fast reactor (SLFFR) using ANL MC2-3/DIF3D/REBUS reactor physics code system Hypothetical accident analysis on SLFFR with self-developed single channel T/H transient code Reactor safety analysis on fuel conversion for the NIST plate-type research reactor (NBSR) Feasibility studies on research reactor replacement at NIST using MCNP-6 and PARET code system Integrated 1-D system level thermal stratification model development for sodium fast reactor (SFR) Computational Methods for Nuclear Applications The linear Boltzmann transport equation is the fundamental mathematical model governing particle transport phenomena in a wide range of nuclear applications, including reactor analysis, radiation shielding, and medical imaging. With continued advances in high-performance computing and numerical methods, there is an increasing demand for computational techniques that provide greater accuracy and computational efficiency in solving complex transport problems. Research topics that we have dedicated in this subject include: Applying the lattice Boltzmann method (LBM) to solve the neutron diffusion and transport equations A modified form of the SAAF transport equation with fully void-compatible feature Hybrid Monte Carlo-deterministic methods for reactor analysis Advances in inverse transport problems and applications to neutron tomography A new 1-D Sn analytic solution for heterogeneous problems with no iteration on interfacial fluxes A modified step characteristic method for Sn transport solution that possesses 2nd order accuracy and diffusion limit Sensitivity Analysis and Uncertainty Quantification The value of a physical quantity predicted by a computational model is limited without a clear understanding of its associated uncertainties. This is particularly important in nuclear applications, where nuclear data inherently contain statistical uncertainties that propagate through computational models and affect predicted quantities of interest. Consequently, sensitivity analysis (SA) and uncertainty quantification (UQ) have become essential components of modern predictive modeling and simulation. Our research efforts in this area include: GPT-free Sensitivity Analysis for Deterministic and Monte Carlo Models More accurate k-eigenvalue sensitivity estimation using the complex-step derivative method Enhanced 1-D SFR thermal stratification model via advanced inverse uncertainty quantification methods Data Analytics and Machine Learning Data analytics and machine learning (ML) offer powerful approaches for extracting information from complex nuclear systems, accelerating physics simulations, and developing predictive models that complement traditional physics-based methods. Our research emphasizes the integration of data-driven learning with nuclear engineering physics, particularly through physics-informed neural networks (PINNs), deep learning, regression, and inverse methods. Applications span reactor physics, transient analysis, nuclear data modeling, molten-salt systems, and uncertainty quantification. Previous research work we have done in this area include: Physics-informed neural networks (PINNs) for neutron diffusion and k-eigenvalue problems Deep learning methods for multidimensional reactor physics calculations Advanced sampling techniques for accelerating PINN-based simulations Data identification and regeneration for MSRE transient prediction Machine learning and regression for nuclear data and cross-section modeling Data-driven inverse modeling and uncertainty quantification for nuclear systems