I work full time as a software engineer/data scientist primarily working on machine learning and data analytics software using tools like PyTorch, PySpark & Azure/AWS Databricks.
    Outside of my full time job I conduct research as an independent research scientist. I have previously collaborated with the Quantum Fields and Quantum Information group lead by Olivier Pfister at the University of Virginia where I worked on machine learning applications for photonic quantum computing. I have also collaborated with the Quantum Physical Learning Group, which is a collaboration lead by Jennifer M. Schwarz at Syracuse University and Olivier Pfister, that explores how to perform machine learning with physical (quantum) systems i.e. for the training and deployment of physical neural networks.
    My research is largely interdisciplinary, specifically at the intersection of quantum computing/information and AI/ML. I am also interested in the broader intersection of physics and computing within the field of neuromorphic computing.
    I also contribute to open source scientific software such as QuTiP as well as open source teaching resources for data science/ML such as the following on Bayesian statistics.

I hold a M.S. in computer science with a concentration in machine learning from the Georgia Institute of Technology.


I hold a B.S. in computer science and physics with a minor in mathematics from the University of Virginia. While there I completed my distinguished major thesis on quantum computing applied to machine learning problems with graphs which was supervised by Olivier Pfister.


• University of Virginia Echols Scholar
• University of Virginia Computer Science B.A. - Highest distinction
• University of Virginia Physics B.S. - Distinction

Research Areas & Interests:
Machine
Learning

Quantum
Computing

Neuromorphic
Computing

Scientific
Computing

Cheminformatics

Bioinformatics

Data
Science

Network
Science

Mathematical
Physics

High Performance
Computing

Mathematical
Optimization

Bayesian
Statistics