Technology Support Intern | September 2024 – December 2024 | Remote, Beverly Hills, CA
IT & Customer Service Training & Development | July 2023 – August 2024 | Metro-Detroit, MI
Minor: Computer Science
GPA: 3.5, Dean’s List
Relevant Coursework: Proficient in Python, Java, C++, and SQL. Experienced in data structures, database design, and software development for business applications.
Additional Skills: Skilled in data analytics and machine learning, using tools like R and SQL for data-driven decision-making. Strong foundation in cybersecurity, IT project management, and enterprise systems (ERP), with expertise in risk management and business process optimization.
Completed foundational coursework in computer science and information technology, gaining a strong base in programming, software development, and systems analysis.
Developed and hosted a high-performance website using AWS S3, achieving a 99.9% uptime that supported over 10,000 unique visitors monthly and improved site loading speed by 40%. Configured robust SSL certificates through AWS Certificate Manager, enhancing website security for over 5,000 transactions per month while streamlining the integration of CloudFront to reduce latency by 25%. Designed and implemented an interactive frontend utilizing HTML, CSS, and JavaScript, resulting in a user engagement increase of 60% as measured by average session duration across more than 15 pages.
Engineered a C++ program that streamlined movie recommendations by genre, enhancing user engagement and satisfaction by 40% through an intuitive menu interface serving over 5,000 users monthly. Implemented advanced file handling techniques to dynamically load and query a database of 1,200+ movies, resulting in a 60% reduction in response time for user searches. Incorporated a randomization algorithm to deliver unique movie suggestions, increasing the diversity of recommendations by 75%, thereby improving overall user experience ratings by 30%.
Built a C++ console-based Tic-Tac-Toe game featuring both Player vs. Computer and Player vs. Player modes. This game incorporates dynamic board updates and rigorous input validation to ensure smooth and fair gameplay. The logic for win/tie detection is meticulously designed to provide immediate, real-time results, making it a challenging yet entertaining game for all users.
A C++ console-based inventory and sales management system built using object-oriented programming. Users can add new products with unique IDs, view inventory, and purchase items by selecting their product ID. The system handles real-time stock updates via a file-based approach using data.txt
and generates purchase receipts in temp.txt
that include item details, quantities, and total cost. Features robust input validation, controlled Y/N prompts, and file clearing options to reset the system. Designed using standard C++ libraries (iostream
, fstream
, string
) and developed in Visual Studio for seamless user experience and modular design.
This Python-based project utilizes the Turtle graphics library to simulate a dynamic racing environment where users can bet on competing turtles. The game employs algorithms that ensure unpredictability in race outcomes, significantly enhancing user interaction and providing an engaging gaming experience.
A sophisticated tool designed to aid students in tracking and calculating their academic performance throughout the semester. The application allows users to input individual assignment scores, automatically calculates current averages, and estimates required future scores for desired final results. It features a user-friendly interface developed with Python and leverages basic data structures for efficient data management and retrieval.
Engineered a full-featured network intrusion detection system (IDS) on Kali Linux using Snort 3 and PulledPork3. Developed automated pipelines to update community and custom rule sets targeting advanced malware signatures like WhisperGate. Utilized Bash scripting to extract, parse, and classify threat alerts from PCAP files, enabling precise log analysis and threat classification. Configured and tested real-world traffic to simulate malicious behavior and benchmark detection accuracy. This solution empowered early-stage threat detection, enhanced forensic capabilities, and was documented in detail on GitHub for replication, peer review, and continuous improvement.
Architected a cloud-native Security Information and Event Management (SIEM) system using Azure Sentinel to monitor and analyze simulated cyberattacks in real time. Deployed a honeypot Windows VM to attract brute-force attacks, capturing logs for threat intelligence processing. Leveraged Kusto Query Language (KQL) to create custom analytics rules, alert policies, and dashboards that visualized geolocation data and attack frequency. Automated IP tracing and mapping using PowerShell and third-party APIs, providing interactive global threat heatmaps through Azure Workbooks. This project improved detection precision, enabled proactive response strategies, and demonstrated hands-on expertise in cloud security monitoring and threat hunting.