Anaximander Gartside

Summary

I am a recent Computer Science graduate of Weber State University—seeking to further develop my skills in cybersecurity, data science, system administration, and software engineering.

Through several hands-on projects, I have gained practical experience in machine learning (ML), artificial intelligence (AI), software development, cloud and Linux administration, and network engineering.

I am currently pursuing a career in IT and cybersecurity—eager to expand my technical skill set.

Qualified for: IAT II and IAM I roles.

Experience

Cashier

Ocean Mart
May, 2022 – July, 2025

• Processed customer transactions accurately as cashier while assisting with management tasks.
• Stocked shelves, organized merchandise displays, maintaining store cleanliness, adequate inventory—assuring customer satisfaction.
• Met stocking and sales quotas while coordinating closely with coworkers to complete tasks efficiently and maintain store standards.

Cashier & Cook

Kneaders Bakery and Cafe
March 2021 - May, 2022

• Cleaned, prepared, and maintained kitchen and service stations; prepared ingredients, cooked fresh menu items, and set up workstations for daily operations.
• Met production and service quotas while coordinating closely with coworkers to complete orders efficiently and accurately.
• Fast-paced position with heavy emphasis on teamwork, time management, and multitasking.

Skills

• Programming C/C++, Java, SQL, Python, TensorFlow, NumPy, Pandas, AI/ML, Bash, Git, Agile/Scrum
• Systems: AWS, Proxmox, VMWare ESXi, RHEL, Debian, Podman/Docker, Active Directory
• Cloud & Networking: Routing/Switching, Wireless Access Points, VPN (WireGuard/OpenVPN), VLAN, DNS, DHCP, TCP/IP
• Professional: Problem Solving, Troubleshooting, Collaboration, Adaptability, Leadership

Education

BS, Computer Science

Weber State University
August, 2022 – April, 2026

AAS, Computer Science

Weber State University
August, 2022 – April, 2025

Certifications

Professional Certifications

• CompTIA Security+ — 416TLEP6MJQ4QG9B

Projects

VM-TUNE — KVM/QEMU Automation

• Developed VM-TUNE, a collection of Bash scripts for automated deployment and performance tuning of QEMU/KVM virtual machines on Fedora, including VFIO-based GPU passthrough, explicit 2MiB hugepage configuration, CPU core pinning & isolation, and dynamic cpufreq governor management.
• Created the primary vmtune.sh command-line interface with Polkit authorization rules, plus backend scripts for VM creation, libvirt XML modification, Looking Glass artifact compilation, and Sway hypervisor environment setup.
• Engineered custom libvirt hooks (qemu dispatcher + hugepages/isolatecpus/performancegovernor scripts) that dynamically allocate hugepages, isolate host processes from VM cores to minimize scheduling noise, and optimize CPU performance states at VM runtime.

Verification

Job Alert Notification System — Python/SQL Stack

• Developed a custom Python-based Job Alert System that automates the discovery of jobs and internships by scraping listings in real time with Selenium, storing data in a relational database using SQL and SQLite3, and delivering categorized notifications.
• Built and trained a supervised Naive Bayes text classification model leveraging natural language processing; performed end-to-end data science work including self-directed data gathering, exploratory data analysis to identify high-impact keywords, preprocessing, and cleaning.
• Achieved an excellent F1 score of approximately 90% while creating a practical, ongoing tool designed for real-world personal use rather than a one-time academic project.

Verification

Dog Image Classification - TensorFlow/NumPy Neural Network

• Fine-tuned Google’s pre-trained MobileNetV2 convolutional neural network using transfer learning for specialized dog image classification.
• Built and implemented an end-to-end computer vision pipeline with TensorFlow and NumPy, including data preprocessing, model adaptation, training, and evaluation on a custom dataset of dog images.
• Completed a hands-on deep learning project focused on practical application of modern CNN architectures in a Jupyter Notebook environment.

Verification

Diabetes Classification

• Developed a binary classification model to predict diabetes diagnosis from patient clinical data (age, BMI, HbA1c_level, blood glucose_level, hypertension, heart disease, smoking history, etc.) using machine learning techniques in Python.
• Conducted full exploratory data analysis including correlation heatmap generation, statistical summaries, data table inspection, and visualizations such as mean age comparison between diabetic and non-diabetic patients.
• Trained and evaluated the model on a toy medical dataset, achieving 90% accuracy and balanced F1-scores of 0.90 across both classes, with detailed performance analysis via confusion matrix and classification report.

Verification