About Me

I am an Applied Research Scientist at Global Technology Applied Research at JPMorgan Chase, where I focus on quantum-inspired and randomized algorithms.

I earned a PhD in Theoretical Physics from the University of Massachusetts, Amherst (advised by Romain Vasseur). My research centered on applying tools from statistical physics and quantum information to problems in quantum many-body dynamics.

Currently, my work applies quantum-inspired techniques — particularly tensor networks — to practical problems in machine learning and optimization. Recent contributions include the development of constrained tensor network architectures for generative modeling and combinatorial optimization, as well as leveraging tensor networks to improve the efficiency of large language models.

Background

PhD in Physics, University of Massachusetts, Amherst.
Thesis: “Quantum Chaos, Integrability, and Hydrodynamics in Nonequilibrium Quantum Matter”
Supervisor: Prof. Romain Vasseur.
MS in Physics, École Normale Superiéure, Paris.
BS in Physics, Universidad Autónoma, Madrid.

Interests

Tensor Networks
Statistical Physics
Machine Learning
Quantum Networking