Research

My current research focus is on developing quantum-inspired classical machine learning architectures in order to solve computational problems of practical interest. More specifically, I am looking at the potential applications of neural network quantum states, which uses classical neural networks as trial wavefunctions for identifying the ground state energy of a given quantum Hamiltonian using variational Monte Carlo, alongside how these applications can be used to solve computational problems of practical interest: combinatorial optimization, high-dimensional linear algebra, physical particle simulation, and fundamental quantum chemistry.

I also have a broader interest in other areas of machine learning, including meta-learning, applications of deep learning toward disease modeling and prediction, computer vision, reinforcement learning, and privacy-preserving machine learning.

Peer-Reviewed Publications:

• Toward Neural Network Simulation of Variational Quantum Algorithms
  Oliver Knitter, James Stokes, Shravan Veerapaneni
  Neural Information Processing Systems (NeurIPS). Workshop on AI for Science: Progress and Promises. 2022. Available here on the ArXiV.

• Meta Variational Monte Carlo
  Tianchen Zhao, James Stokes, Oliver Knitter, Brian Chen, Shravan Veerapaneni
  Neural Information Processing Systems (NeurIPS). Workshop on Machine Learning and the Physical Sciences. 2020. Available here on the ArXiV.

My master's thesis was in the area of Diophantine approximation, more specifically in looking at regions of real n-space that are, "best," approximated by a given tuple of rational numbers. I expanded on previous work describing the geometric structure and complexity of these regions. This work was motivated primarily by the Littlewood Conjecture and may be seen either here or under, "Miscellaneous Writings," above.