👨💻 Cyber Capabilities Developer | 🎓 MSCS in Computer Science | 🔍 Reverse Engineering & Vulnerability Research Enthusiast | 🧠 ML for Cybersecurity
I am a U.S. Army National Guard Soldier and graduate student. I am currently on military orders supporting the 91st Cyber Brigade as a Cyber Capabilities Developer (CCD) and also taking the final course of my Masters of Science in Computer Science (MSCS) with a specialization in cyber operations. I have a deep interest in low-level security topics such as reverse engineering, vulnerability research, and threat hunting. Understanding manual workflows is essential, but I believe that automating or integrating them into Machine Learning and Large Language Models (ML / LLM) can set me apart from my peers.
As I near my return to the civilian workforce and completion of my degree, I’m actively seeking roles. I would love to find roles where I can focus on reverse engineering and vulnerability research, or bring a unique skillset to your red team or threat hunters.
I bring a unique mix of:
- Deep experience in low-level C programming, architecture, and binary analysis
- A strong foundation in cybersecurity across both offensive and defensive domains
- Practical and academic exposure to machine learning applied to security-related problems
| Skill | Associated Project/Experience |
|---|---|
| C/Assembly, Memory Safety | ARCYBER Development, Data Structures, Multithreading |
| Malware Analysis, Reverse Engineering | Ghidra, IDA Pro, gdb, Static + Dynamic Analysis |
| Binary Exploitation | Python, pwntools, fuzzing, Ropper |
| Threat Hunting & Emulation | Log analysis, intrusion detection, Cyber Threat Emulation |
| Machine Learning & NLP for Security | ML-based malware detection and LLM-based vulnerability detection |
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🧱 Generic Server-Client Architecture & Data Structure Libraries A modular C-based networking framework with reusable client-server code and custom implementations of data structures (BST, linked hash tables, queues).
Purpose: Demonstrates strong C programming and low-level architectural design. Serves as a foundation for future tooling. -
🔍 Machine Learning for Malware Detection A supervised classifier trained on Windows PE import features to distinguish malicious from benign executables.
Focus: Feature engineering, dimensionality reduction, and model comparison (KNN, MLR). -
🔧 FSM-Based Vulnerability Detection in Binary Executables A finite state machine-based static analyzer written in C for detecting uninitialized memory usage in compiled Juliet Test Suite binaries.
Focus: Manual static analysis, compiler-aware design, vulnerability pattern matching. -
🔬 LLM-Based Vulnerability Detection in Tokenized Assembly A transformer-based model trained to detect the use of uninitialized variables from disassembled binaries.
Focus: Integrates NLP-style tokenization, FSM-derived supervision, and disassembly features.
M.S. Computer Science – Dakota State University (Aug 2025)
Specialization: Cyber Operations
Coursework: Software Exploitation, Machine Learning, Cryptography
Special projects: FSM-Based static analyzer, LLM-Based vulnerability detection
B.S. Cyber Operations – Dakota State University (May 2023)
Coursework: Reverse Engineering, C Programming, Assembly Language, Network Security, Threat Hunting
🛡️ DoD 8570 IAT Level II Compliant
🔐 TS/SCI Security Clearance – Active