Research

Topological phases of matter

Topology has become one of the cornerstones of modern condensed matter physics. Starting with the quantum Hall effect, where a perpendicular magnetic field induces a quantized response in a two-dimensional system, topological ideas have evolved into a major theme in the field. Topological insulators and topological superconductors exhibit remarkable properties that are present even in the simple free-electron picture, thus enriching band theory. I am interested in understanding the interplay between the topological properties of quantum systems, often described by the Berry curvature and quantum geometry, and factors like electron-electron interactions and geometric confinement. The richness of these systems enables studying entirely new phases of matter, where the electrons “fractionalize” and form new types of order.

Superconductivity in novel mesoscale systems

Superconductivity is one of the most striking quantum many-body phenomena, showcasing quantum coherence in a very tangible way: when cooled below a certain temperature, many materials exhibit zero electrical resistance and perfect expulsion of magnetic fields. Investigating mesoscopic superconductors offers valuable new insights into the nature of superconductivity. For example, Josephson junctions provide significant information both about the superconductors involved and the conducting region between them. The rapid experimental advancements in superconductivity motivate theorists to think about novel settings where new physics can be uncovered. In particular, I am interested in superconductors with a non-trivial topology, disorder, unconventional pairing symmetry, or finite Cooper-pair momentum. Systems of interest in this direction include (but are not limited to) superconductor-semiconductor heterostructures and two-dimensional layered materials.

Machine learning in condensed matter physics

Machine learning (ML), a transformative approach for computational problem solving, has achieved remarkable success across various domains. Applying ML to quantum many-body theory is an exciting new frontier. One significant application of ML is data-centric, where existing data (measured or simulated) is utilized to uncover new insights into the underlying physics. A different approach is harnessing the computational power of ML algorithms to tackle theoretical questions, in particular the quantum many-body problem. Both approaches are fascinating and hold the potential to unlock fresh avenues and solutions to both old and new challenges in the field.