WHOAMI

Hello, my name is Zachary D. Rife — applied researcher, systems developer, and public servant dedicated to advancing high-performance computing solutions for artificial intelligence research.

I work at the intersection of systems development and AI, building custom HPC environments to support deep learning workflows, DNN training pipelines, and reproducible scientific computing. My work is grounded in C++, CUDA, Python, and SQL, with a focus on scalable architecture, mathematical modeling, and precision engineering.

Background

My introduction to computing came through Linux — a shift that led me toward systems-level thinking, performance optimization, and low-level development. Since then, I’ve built a workflow centered on open-source tools and custom-configured environments running Ubuntu, designed to support distributed AI workloads and high-throughput data processing.

I specialize in building reproducible, efficient computational systems for training deep neural networks, simulating AI architectures, and exploring novel methods for scientific inference and systems evaluation. My approach combines HPC infrastructure design with deep learning systems engineering — always with performance, clarity, and rigor in mind.

Research Philosophy

As an AI researcher and HPC developer, I aim to push the boundaries of what’s possible with custom systems and optimized code. I approach my work with three core principles:

• Reproducibility: Every experiment and model should be transparent and replicable. I document all research in LaTeX and use strict version control for configuration and code artifacts.
• Rigor: My systems are designed with mathematical discipline, diagnostic accuracy, and theoretical grounding.
• Performance: From low-level C++ routines to GPU-accelerated CUDA kernels, every component is built for speed, scale, and reliability.

Sample Code

Here’s a basic C++ template used in my diagnostic and runtime toolchain, demonstrating a CUDA kernel for performance benchmarking:

#include <iostream>
#include <cuda_runtime.h>

__global__ void vectorAdd(const float* A, const float* B, float* C, int N) {
    int i = blockIdx.x * blockDim.x + threadIdx.x;
    if (i < N) C[i] = A[i] + B[i];
}

int main() {
    // Device setup and allocation logic here
    std::cout << "Running CUDA vector addition benchmark..." << std::endl;
    // Launch kernel, measure performance, validate result
}

Most of my work is rooted in C++ systems development, with a focus on building runtime components, memory optimization tools, and AI infrastructure engineered for GPU-based parallelism and distributed computing. CUDA enables me to unlock hardware-level acceleration, while Python serves as a scripting layer for ML integration and orchestration. SQL rounds out my stack for backend data engineering and system telemetry.

All research and development is documented using LaTeX, ensuring high standards for reproducibility, clarity, and academic publication readiness.

Systems Focus

My primary area of interest is HPC-driven AI research — building and testing deep learning systems from the ground up. This includes:

• Designing custom HPC environments for AI training
• Implementing deep neural networks and evaluating their mathematical behavior
• Developing tools and utilities that bridge high-level analysis with low-level optimization
• Contributing to reproducible, scalable AI infrastructure

Closing Note

Outside of my technical work, I serve as a volunteer firefighter — a role that grounds my commitment to service, responsibility, and teamwork.

This site is a record of my work in AI systems research, C++/CUDA development, and high-performance computing. You’ll find projects, technical notes, and reflections aimed at advancing scientific insight and infrastructure for artificial intelligence.

Whether you’re working in systems, algorithms, or theoretical AI, I hope this portfolio offers something useful, efficient, and thoughtfully engineered.

Feel free to reach out for collaboration, feedback, or shared research interests.