Miriam Ramírez Zárate

I’m a Software Engineering student at the Universidad Veracruzana, driven by a strong interest in Artificial Intelligence and software engineering research. I focus on making AI more trustworthy through explainable approaches, especially where real decisions depend on model reliability.

My research experience spans explainable AI (XAI) and software defect prediction, including an article accepted for publication and additional work currently in progress. I’ve evaluated multiple machine learning classifiers on software defect datasets and compared interpretability methods like LIME, SHAP, and BreakDown to address the “black box” challenge.

I also enjoy building solutions that connect research to impact. Together with my team, we won second place in the SPEI Banxico 2025 Hackathon, and I participated in the Cyberdemocracy Chihuahua 2026 Hackathon, contributing to technology designed for meaningful outcomes.

In particular, I contributed to the PREP Electoral System—an AI-powered real-time vote tallying platform—where I supported data capture using Azure Form Recognizer and implemented role-based workflows with strict access control and traceability. Alongside this, I’ve developed RESTful backends and full-featured applications, applying tools like Docker, NGINX, React, and SQL to deliver end-to-end functionality.