Ph.D. Graduate & Systems Engineer

I am a computer scientist specializing in Graph Neural Networks (GNNs), Large Language Models (LLMs), and High-Performance Computing.

I hold a Ph.D. in Computing (December 2025) and a B.S. in Computer Science from Boise State University. My work bridges the gap between theoretical machine learning and practical, scalable deployment—transitioning novel algorithms from whiteboards to production-grade infrastructure.

Research & Publications

My research focuses on structural node representation and zero-shot learning on graphs. I have authored multiple papers on detecting structural patterns and temporal dynamics:

• LLM-GMP (2025): Large Language Model-Based Message Passing for Zero-Shot Learning on Graphs (Accepted at IEEE Big Data 2025).
• 2FWL-SIRGN (2024): A Scalable Structural 2-dimensional Folklore Weisfeiler-Lehman Graph Representation Learning Approach (Accepted at IEEE Big Data 2024).
• Temporal SIR-GN (2023): Efficient and Effective Structural Representation Learning for Temporal Graphs (Accepted at VLDB 2023).
• Botnet Detection (2021): Detecting Botnet Nodes via Structural Node Representation Learning (Accepted at IEEE Big Data 2021).

Teaching & Leadership

Beyond research, I have served as a Graduate Teaching Assistant for upper-division and graduate-level courses, mentoring students in complex algorithmic concepts:

• CS 534: Machine Learning 
• CS 535: Large Scale Data Analysis 
• CS 321: Data Structures 

Technical Infrastructure

I maintain a robust local infrastructure to support my work and hobbies.

• Orchestration: Docker, KVM, Nginx Proxy Manager, Cloudflare Tunnels.
• Hardware: Multi-GPU compute nodes for local LLM inference.
• Languages: Python, C++, C, Java, SQL, LaTeX.
• Frameworks: PyTorch, TensorFlow, NetworkX, DGL.