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My research uncovers how emergent order, topology, and correlations organize quantum materials, and translates that understanding into device-relevant platforms. I combine first-principles electronic-structure calculations, Green's-function and quantum-transport methods, and spectroscopy modeling spanning photoemission, RIXS, and Compton scattering. An emerging effort explores machine learning for quantum materials.
My research uncovers how emergent order, topology, and correlations organize quantum materials, and translates that understanding into device-relevant platforms. I combine first-principles electronic-structure calculations, Green's-function and quantum-transport methods, and spectroscopy modeling spanning photoemission, RIXS, and Compton scattering. An emerging effort explores machine learning for quantum materials.
Energy–momentum analogues of relativistic causality in interacting Weyl systems.
Anomalous Hall transport in magnetic Weyl systems and symmetry-controlled spin textures, including nonorthogonal spin-momentum locking.
Strain-induced topological states and interlayer-phase-driven structural chirality in layered charge-density-wave materials.
Coherent secondary and tertiary photoemission from quantum-oxide photocathodes, and modeling of resonant inelastic x-ray scattering in correlated materials.
Green’s-function and atomistic methods for supercurrent transport, decoherence, and quantum-material junctions.
Topological electronic phases as a route to sodium-ion anodes—predicted from first principles and probed by x-ray Compton scattering.
Machine Learning & Quantum Materials
Machine Learning & Quantum Materials
Machine-learning approaches for quantum materials, developed through a multi-institutional collaboration.
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