Exploring topological phases, unconventional superconductivity, and quantum magnetism — and the peer-reviewed work that has come out of it.
I study topological phases in both electronic and magnetic systems. This includes Chern insulators, symmetry-protected topological phases, and topological magnons. A key focus is on Dirac nodal line (DNL) magnonic phases in layered honeycomb collinear antiferromagnets. I investigate how interlayer spin canting interactions break PT symmetry, induce topological phase transitions, and lead to observable thermal Hall responses.
More broadly I am interested in how crystal symmetries and magnetic order combine to produce non-trivial band topology in bosonic and fermionic systems alike.
I am fascinated by unconventional superconducting phases where topology protects gapless boundary modes. This includes Majorana zero modes (MZMs) in one-dimensional p-wave superconductors and their realization in proximity-coupled systems. I am interested in the robustness of Majorana modes to disorder, their non-Abelian braiding statistics, and their potential for topological quantum computation.
I also study topological phase transitions driven by the interplay of spin-orbit coupling, magnetism, and superconducting pairing in two-dimensional systems.
Proximity effects at interfaces between superconductors and other quantum materials are a rich playground for emergent physics. I study how superconducting correlations are inherited at SC–semiconductor and SC–magnet interfaces, how Andreev reflection and Crossed Andreev reflection operate in these systems, and what transport signatures can identify topologically non-trivial phases.
I am particularly interested in flat-band systems, altermagnets, and van der Waals heterostructures where topology and correlations intersect in unusual ways.
I am interested in engineered quantum materials where topology and correlations intersect, including flat-band systems, altermagnets, and van der Waals heterostructures. My goal is to connect theoretical models with experimentally realizable systems, making predictions that can guide materials synthesis and spectroscopic measurements.
I use numerical techniques including exact diagonalization, MPS/DMRG, and Monte Carlo methods to study quantum systems. I am also interested in machine learning approaches — particularly for identifying quantum phases of matter and for analyzing experimental data from scattering and spectroscopy experiments.
At TCS Research I applied deep learning to acoustic sensing in industrial pipelines, resulting in a published IEEE paper.
Peer-reviewed journal articles, conference proceedings, and preprints. Authored and co-authored work in condensed matter physics and quantum sensing.