Recent B.S. Statistics graduate from the University of Central Florida with a heavy CS course load and a passion for cybersecurity. Building secure, data-driven solutions on Azure and AWS — accelerated by an AI-native development workflow.
I graduated from UCF in December 2025 with a B.S. in Statistics and a course load deep in computer science — programming in C, Java, Python, and R, statistical learning, big data analytics, and security in computing.
My focus is cybersecurity: detection engineering, threat intelligence, and the analytics that drive both. I'm building a portfolio of practical tools that solve real problems for security learners and practitioners — every project ships, gets used, and gets iterated on.
I'm AI-native. I run a daily workflow across GitHub Copilot, Claude Code, and OpenAI Codex — not as autocomplete, but as a multi-agent engineering team. It's how I ship faster than a typical new grad, with a higher standard of code review and security hygiene than I could maintain alone.
“ Statistics taught me to think in distributions. Security taught me to think in adversaries. AI taught me to think in systems. The job is to do all three. ”
I don't just use AI coding tools — I run a coordinated multi-agent workflow across four of the strongest agents and orchestration tools available, picking the right one for each phase of the build. This isn't a productivity gimmick; it's how I scale my impact as a one-person team.
main.A portfolio of working tools at the intersection of cybersecurity, data, and cloud. Every featured project is shipped to a live GitHub Pages URL, open-sourced on GitHub, and built with the AI workflow above.
Maps MITRE ATT&CK techniques to Sentinel-friendly detection ideas and starter KQL. Genuinely useful to working SOC teams — not just a study aid.
Interactive D3.js graph connecting Microsoft security, identity, and compliance concepts. Clickable nodes expand into definitions and links to official documentation.
Trading-card-style profiles of major threat actors — nation-state origin, target sectors, common TTPs, and linked MITRE techniques. The F1-card aesthetic translates perfectly here.
Searchable, filterable browser of MITRE ATT&CK techniques. Filter by tactic, platform (Windows · Linux · Cloud), and detection difficulty — the kind of utility blue teamers reach for daily.
Side-by-side reference for NIST CSF, ISO 27001, SOC 2, and CIS Controls. Filter by control category and see how the frameworks map to each other — a strong GRC signal.
Branching incident-response tabletop with consequence-driven paths and replayable scenarios. Pure JS state machine — no backend — teaching real IR sequencing and trade-offs.
Statistics major with a substantial computer-science course load — programming in C, Java, Python, and R, plus statistical learning, big data analytics, and security in computing.
Selected UCF coursework, framed around the applied problem each course let me dig into. Each is grounded in real assignments, projects, and self-directed extensions.
Studied attack surfaces, cryptography, authentication, and defense-in-depth — the foundation for my detection-engineering portfolio.
Hands-on with ML pipelines, evaluation, and reproducible analysis — the techniques I'm now applying to anomaly detection.
Regression, classification, regularization, and model selection — directly applicable to detection model tuning and false-positive reduction.
Large-scale data processing and analytical workflows — the toolkit for working with security telemetry at scale.
Earned an A applying hypothesis testing and modeling to real datasets — translates cleanly to security baseline-vs-anomaly analysis.
Probability, distributions, estimation, and inference — the mathematical backbone for anything from detection scoring to risk modeling.
Data structures, algorithms, and complexity analysis — the foundation for everything I build today.
Java OOP design — classes, inheritance, polymorphism, and the patterns that show up across modern enterprise security tooling.
Low-level systems thinking: how CPUs, memory, and instruction sets actually behave — useful context for reverse engineering and exploit basics.
Earned an A in the math behind cryptography, automata, and graph algorithms — the formal underpinning of computer security.
Numerical methods, error analysis, and computational techniques — the practical bridge between math theory and working code.
Linear systems, eigenvalues, and matrix decomposition — the math behind ML, dimensionality reduction, and modern detection algorithms.
Technical writing fundamentals — documenting decisions clearly, which is half the job in any security team.
Reproducible statistical computing — the discipline that translates directly to scripting security telemetry workflows.
Looking to bring statistical rigor, security thinking, and an AI-native build process to a team that ships. If that's you, here's how to reach me.