My main area of research is at the intersection of quantum computing and learning theory. My works centers around developing quantum ensembling algorithms under various learning models and proving theoretical bounds for convergence, generalization bounds, and speedups. For more details, click here.
I am also interested in a broad number of other theoretical computer science topics. I have worked in quantum algorithms for solving linear systems of equations, variational optimization algorithms for NISQ era hardware, shallow circuit complexity, and reducing divergence for distribution mixtures with respect to neural networks.