About
I'm a Computer Science graduate with a concentration in Advanced Cybersecurity and a strong foundation in IT systems, networking, and database management. My current focus is on cybersecurity, with a particular interest in digital forensics and incident response.
Previously, I worked as a IT Technician at Firebaugh Las Deltas Unified School District where I was able to gain a solid foundation in IT support, network administration, and system maintenance. In addition to my IT background, I have worked on several technical projects that reflect my growing expertise in cybersecurity. This includes deploying cloud-based infrastructure and managing SQL databases for a web application, as well as developing machine learning models for phishing detection. Through these projects, I have gained experience in secure system design, along with exposure to various tools and technologies commonly used in the cybersecurity field.
Experience
Information Technology Technician (Volunteer)@Firebaugh Las Deltas Unified School District
June - September 2025
Returned as a volunteer to support ongoing IT operations ahead of the new school year. Assisted staff with hardware refreshes, device imaging, and software configurations across sites. Helped audit and document network assets and wrote scripts to automate Active Directory management.
Information Technology Technician@Firebaugh Las Deltas Unified School District
June - August, 2016 - 2019
Provided technical support and system maintenance for a school district environment, diagnosing and resolving hardware and software issues to ensure minimal downtime, and ensuring secure handling of data through regular backups and updates. Assisted in improving network infrastructure by configuring wireless access points and organizing network distribution systems.
Projects
Archivr - Media Recommendation
Archivr is a website that serves a one-stop hub for tracking, rating, and discussing your favorite movies and TV. It allows you to share your thoughts, join discussions, and discover new favorites based on personalized recommendations.
Phishing URL Detector
A phishing URL detector that uses machine learning to analyze URLs and identify potential phishing threats. The model is trained on a dataset of known phishing and legitimate URLs, utilizing features such as URL length, presence of special characters, and domain age to make predictions.
Want to see more projects?
View All ProjectsBlog & Writing
Flag In Flame — Log File Analysis and Encoded Image Recovery
↗A forensic investigation into a log file concealing a Base64-encoded JPEG image, followed by hexadecimal decoding to recover a hidden flag.
2025
Hidden In Plain Sight — Steganographic Analysis of a Concealed Payload
↗A multi-layered forensic analysis combining EXIF metadata inspection, nested Base64 decoding, and steghide extraction to recover a hidden flag from a JPEG image.
2025
Enhanced Facial Detection: Age and Mask Identification for Diverse Applications
↗A report on building a real-time facial analysis system using deep learning to estimate age and detect mask usage. Integrated trained CNN models into a live web app, achieving over 94% accuracy in mask detection.
2024
Detecting Phishing Website: A Deep Learning Approach with MLP
↗A deep learning–based approach to detecting phishing websites using a Multilayer Perceptron, achieving high accuracy through feature engineering and hyperparameter tuning. This report highlights the model’s effectiveness in identifying malicious URLs while minimizing false positives and negatives.
2024