Current Research Interests

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Atomic Physics

• Atomic structure modeling: Computational methods for calculating the electronic structure and properties of atoms using quantum mechanical models.
• Electron-ion collisions: Theoretical and computational modeling of electron-impact processes, including excitation, ionization, and recombination.
• Configuration interaction: Non-perturbative electronic structure methods for accurately describing electron correlation in many-electron atoms.
• Dielectronic recombination: Resonant electron capture and radiative stabilization processes that play a central role in determining the ionization balance and spectra of astrophysical and laboratory plasmas.

Computational Methods

• Numerical algorithms: Methods for approximating solutions to mathematical relations to arbitrary precision.
• Scientific software: Codes for modeling physical phenomena and interpreting physical data.
• High-performance computing: Optimization, parallelization, and memory allocation and management for computationally intensive simulations or calculations.
• Machine learning methods: Data-driven approaches for accelerating scientific computation, selecting optimal models, and discovering efficient representations of complex physical systems.

Applications

• Plasma spectroscopy: Analysis of emitted radiation to diagnose the elemental abundances and temperature profile of a plasma.
• Astrophysical plasmas: Interpretation of spectra from stellar atmospheres, nebulae, planetary nebulae, and other astrophysical environments.
• Laboratory plasmas: Applications to fusion and laboratory plasma experiments, including tokamaks, stellarators, and storage-ring measurements.

Emerging Research Interests

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Quantum Information and Algorithms

• Quantum chemistry simulations: Exploring quantum algorithms for electronic structure calculations and many-body quantum systems.
• Generalizable quantum algorithms: Investigating mathematical frameworks that may extend the applicability of quantum algorithms across multiple classes of scientific computing problems.

Machine Learning for Physical Sciences

• Linearizing complex physical systems: Investigating operator-theoretic approaches for representing nonlinear dynamical systems through linear evolution in higher-dimensional spaces.
• Scientific model discovery: Applying machine learning techniques to identify reduced-order models, hidden structure, and emergent patterns in complex many-body systems.