Building intelligent systems at the intersection of AI and embedded computing.
I'm a computer engineer specializing in machine learning, embedded systems, and on-device AI. Currently pursuing my Master's at FIU while researching parameter-efficient deep learning for resource-constrained environments.
Selected Work
LLM Fine-Tuning for Anomaly Detection
Parameter-efficient anomaly detection pipeline using LoRA fine-tuning on LLaMA 3.2-3B, achieving 99.20% F1-score on HDFSv1 system logs. Published in EAI ICDF2C 2025.
CNN Autoencoder for Embedded Malware Detection
Novel pipeline creating space-filling curve image representations of microcontroller program traces via QEMU emulation, achieving 98.99% malware detection accuracy.
TasteBud - Smart Grocery & Recipe Assistant
End-to-end IoT smart kitchen system with embedded hardware, barcode scanning, full-stack web app, and AI-powered recipe generation addressing food waste.
About
I'm a Computer Engineering graduate student at Florida International University specializing in AI, machine learning, and embedded systems. As a Graduate Research Assistant at FIU's Department of Electrical & Computer Engineering, I focus on optimizing neural networks for resource-constrained embedded systems.
My research spans parameter-efficient deep learning, on-device AI deployment, and novel approaches to anomaly detection. I've published work on fine-tuning LLMs for distributed system logs and contributed to FIU earning NSA CAE-R and CAE-CD designations.
When I'm not coding or researching, I'm interested in robotics, AI advancements, and exploring the future of edge computing. Bilingual in English and Spanish.
Contact
Let's work together. Get in touch for research collaborations, opportunities, or just to connect.