Andrew Swarbrick

Recent Graduate with 6 months of industry experience (not including university) as a Software Engineer.

Industry Experience is on the frontend with JavaScript, Vue.js, HTML/CSS, and Three.js.

Personal development experience currently with React for my portfolio and Golang for a custom project designed to enable the user to automate and schedule tasks on other devices over a local network, as well as providing the tools to be able to orchestrate data pipelines on a system

Software Engineer - Copner Biotech Ltd. - 6 months:

● Led the architecture and development of a next-generation CAD platform for proprietary 3D and 4D Bioprinting technology, replacing legacy software.

● Designed and implemented a cross-platform, low-maintenance backend using Node.js and Express, enabling zero-setup client access over LAN or localhost.

● Developed a responsive SPA frontend using Vue.js, JavaScript, HTML/CSS, and Three.js, resulting in an intuitive, low-latency user experience.

BSc Computer Science - Cardiff University

Grade: 2:1

Including Large-Scale Databases, Informatics, Communication Networks, Combinatorial Optimisation, Object Orientation, Algorithms and Data Structures

Including projects

Personal Security System for a Night Time Economy

Part of Degree - 2nd Year Group Project

● Led the Android development sub-team for a real-time personal safety app targeting the night-time economy, as part of a larger group of 8.

● Built a secure Android application in Java, integrating Google Maps SDKs for journey tracking and using TLS-secured HTTP APIs for server communication.

● Implemented functionality to monitor user journeys and automatically send SMS alerts to emergency contacts if deviations were detected.

● Achieved a final project grade of 86%.

Automated Cyber Defence Through Deep Reinforcement Learning

Part of Degree - Final Year Individual Project

● Developed reinforcement learning models in Python to automate cyber defence tasks, using the TTCP CAGE Challenge 2 simulation environment.

● Focused on training blue team agents with PPO models making use of LSTM nodes with Stable Baselines 3, applying hyperparameter tuning through Optuna.

● Designed adversarial conditions by modifying red team strategies dynamically to assess model robustness.

● Conducted performance analysis on policies with nodes that allow decisions to be influenced by historic transitions; findings were inconclusive but informed recommendations for future research.

● Achieved a final project grade of 80%.