I study nonlinear plasma dynamics and discharge phenomena using computational modeling and machine learning.

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My work combines reduced-order fluid and kinetic approaches, including Particle-in-Cell (PIC) and Monte Carlo simulations, with physics-informed and data-driven machine learning to develop predictive models of plasma and vacuum discharge systems.

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I am interested in building next-generation computational tools that integrate first-principles multiphysics modeling with scalable machine learning and AI-enabled approaches, with the goal of improving the reliability and design of plasma-based technologies in particle accelerators, high-power microwave systems, and fusion energy.