I am a mathematical and computational scientist whose primary interests lie at the intersection of quantum many-body physics and machine learning (ML). The aims of my research are twofold: 1. leverage the huge success of ML in high-dimensional learning tasks to efficiently solve quantum many-body problems and 2. develop impactful scientific computing tools using quantum many-body technology as a resource.
Accomplishments and Awards
- 2023 Propelling Original Data Science (PODS) Grant Award: Neural Quantum States at Scale: Applications in Sciences and Engineering.