Computational Physicist · Deep Learning · Quantum Computing
I'm a computational physicist turned deep learning practitioner. I hold a Ph.D. in Chemical Physics from U.C. Davis, where I was part of the Atomic, Molecular, and Optical Physics theory group at Lawrence Berkeley National Lab. My research there focused on ab initio non-relativistic scattering code development using Exterior Complex Scaling with a Finite Element Method / Discrete Variable Representation grid.
These days I build and ship deep learning systems, with a soft spot for the places where physics and ML meet. I'm always tinkering — whether it's pruning neural nets, hacking on Neovim configs, or reading papers on quantum algorithms. I've also recently discovered declaritive programming, specifically Answer Set Programming (ASP) and it's applications to the A.I. field.
Languages
ML / AI
Tools
| Project | Description |
|---|---|
| RTMPose-NCNN | Real-time pose estimation in C++ using the NCNN inference framework |
| fastfeedforward | Log-time feedforward networks — a novel efficient architecture |
| Prune-And-Train | Neural network pruning research for efficient model training |
| nvim | My personal Neovim configuration in Lua |
- Deep learning for scientific computing and physics simulations
- Quantum computing applications to quantum chemistry in the NISQ era
- Efficient neural network architectures and model compression
- HPC and high-performance numerical methods
- Email: zacharylouis42@gmail.com
- LinkedIn: Zachary Streeter