Quantum information, optics & learning theory.

I'm drawn to problems that sit at the boundaries between fields. Much of my work focuses on quantum photonic computing — designing robust quantum gates in integrated circuits, building spatial mode transformers, and developing quantum key distribution protocols. I'm particularly interested in how ideas from robust control can make photonic quantum devices practical and scalable.

In parallel, I study quantum many-body physics — complexity diagnostics, quantum scrambling, localization transitions, and symmetry enhancement measurements in integrable models. I also explore foundational questions in machine learning theory (why gradient-based learning works, high-dimensional classification). I find that moving between these areas often leads to unexpected connections — methods from one field becoming tools in another.