Aaron Loera
Computer Science Student | Deep Learning & AI Researcher
Passionate about advancing artificial intelligence through innovative research and developing cutting-edge solutions in deep learning and data science.
Passionate about advancing artificial intelligence through innovative research and developing cutting-edge solutions in deep learning and data science.
I'm a dedicated Computer Science student at the University of Texas at Tyler with a profound passion for artificial intelligence, deep learning, and data science. My research focuses on developing innovative AI solutions that can make a real-world impact, particularly in healthcare and assistive technologies.
Through my work as a Deep Learning Research Assistant and various projects, I've gained extensive experience in designing and implementing sophisticated neural networks, from CNNs and Transformers for medical signal processing to computer vision systems for assistive technologies.
University of Texas at Tyler
University of Texas at Tyler
Data Annotation Tech
Texas Roadhouse
This project fine-tunes a Vision Transformer to classify QR codes as legitimate or malicious, addressing the growing security risks posed by visually indistinguishable phishing codes. Beyond detection, the model's predictions are made interpretable through attention rollout, highlighting the global visual patterns the transformer relies on when identifying malicious intent. Overall, the work demonstrates the feasibility of image-only QR code security as a proactive defense against QR-based attacks.
This project focuses on detection of cardiovascular diseases from 12-lead ECG signals using a custom deep learning CNN tailored to capture cross-lead cardiac patterns. Beyond classification, the learned feature space is explored using a probing classifier to evaluate how well metadata (age, sex, etc.) is encoded in the model's representations. The work aims to enhance automated cardiovascular disease detection and support more reliable decision-making in healthcare settings.
This project leverages the power of Langchain, Ollama, and Chroma to create a document-based question answering system. It uses a vector database to store document chunks and retrieves relevant information based on user queries. The system is capable of ingesting CSV files, splitting them into manageable chunks, and embedding them into a vector space for efficient search and retrieval. The assistant, acting as a college advisor, answers questions by leveraging the stored data.
This project implements sidewalk lane detection using a fine-tuned YOLOv8 model to support safe navigation for visually impaired users in real-world environments. To prioritize user safety, detections are filtered with a high-confidence threshold, while additional classes such as bicycles and automobiles are incorporated to reflect realistic walking conditions. Overall, the system demonstrates how real-time, vision-based AI can improve pedestrian safety by providing reliable environmental awareness in everyday navigation.
Support for academic and applied AI research projects, including literature review, methodology design, and implementation of cutting-edge algorithms.
Rapid development of proof-of-concept machine learning solutions to validate ideas and demonstrate feasibility for various applications.
Custom deep learning models for real-world applications, from computer vision to natural language processing and signal analysis.