I am a Ph.D. candidate in Applied Mathematics at the University of Arizona, advised by Dr. Michael Chertkov and Dr. Laurent Pagnier. My research lies at the intersection of mathematical modeling, probability, stochastic processes, dynamical systems, and scientific computing. I develop theoretical and computational methods for analyzing complex dynamical and networked systems under uncertainty, with a particular focus on modern energy infrastructures. My work includes probabilistic risk assessment, rare-event analysis, Monte Carlo and importance-sampling methods, surrogate and reduced-order modeling, stability analysis, and large-scale simulation of transmission-level power networks and coupled energy systems.
My research experience spans academic, national laboratory, and international energy-system environments, including collaborative work at Los Alamos National Laboratory. I develop Python- and Julia-based research software and scalable computational workflows to translate mathematical ideas into practical simulation, inference, and decision-support tools. More broadly, I am interested in the intersection of applied mathematics, machine learning, artificial intelligence, optimization, and complex systems. I am particularly motivated by problems where rigorous mathematical modeling and modern data-driven methods can be combined to better understand, predict, and manage large-scale engineered systems.