Researcher, Systems Builder, and Future Compiler & Runtime Engineer
I am a Ph.D. researcher in Computer Science at the University of North Dakota, working at the intersection of trustworthy cybersecurity machine learning, reproducible research infrastructure, and systems programming. My long-term direction is to become a compiler and runtime engineer capable of building the low-level machinery that makes software faster, safer, and more reliable.
An Interdisciplinary Path into Computer Science
My academic path began outside traditional computer science. I previously studied Physics before moving deeply into computing, cybersecurity, machine learning, and systems engineering.
That background shaped how I approach technical problems. I tend to examine definitions, assumptions, evidence, incentives, failure modes, and the larger system in which a technology operates. Today, I apply that analytical mindset to both research and low-level software development.
Rather than treating systems programming and research as separate identities, I am intentionally connecting them: rigorous experimentation on one side, and increasingly deep implementation knowledge on the other.
What I Am Focused on Now
My current work combines dissertation research, systems foundations, technical teaching, and open-source project development.
GenCyberSynth Research
Reproducible synthetic malware-image generation, utility, auditing, and trustworthy evaluation.
C Systems Mastery
Building deep understanding of memory, pointers, interfaces, debugging, tests, and reusable C architecture.
Systems Projects
C CLI Lab, MetricForge, reusable tooling, build systems, tests, and performance-conscious project design.
Teaching & Mentorship
Supporting courses in systems programming, architecture, cybersecurity, networks, and software fundamentals.
How I Work
The principles that guide my research, engineering, and continued progression toward deeper systems expertise.
Understand Before Abstracting
I prefer to understand the underlying mechanism before depending on a framework or high-level abstraction.
Evidence Over Assumption
I use experiments, tests, diagnostics, measurements, and clear reasoning to validate technical claims.
Build for Reproducibility
Code, commands, configurations, results, and decisions should be preserved so work can be repeated and examined.
Progress Through Mastery
I am deliberately building strong foundations rather than rushing toward advanced titles without the required depth.
Building Reliable Systems from Research to Runtime
My goal is to combine research rigor, low-level engineering, performance awareness, and open-source contribution in a career centered on compilers, runtimes, and dependable systems infrastructure.