Jay Ree

• Conducting weekly applied sessions for FIT1045: Introduction to Programming, guiding students through hands-on coding exercises and reinforcing core programming concepts.
• Assessing portfolio assignments and providing constructive feedback to support student learning outcomes.

AI-Powered Personal Executive Assistant for Productivity & Time Management

• Led the end-to-end architecture and development of a full-stack AI assistant platform — building the backend in Node.js (Express) with Supabase (PostgreSQL) and the mobile client in Flutter (iOS) — integrating third-party services including Google Calendar, Notion, and Google Cloud voice APIs to deliver a unified productivity tool.
• Owned the AI feature pipeline, designing a conversational assistant using Google Gemini that autonomously manages calendar scheduling, task prioritisation, and personalised recommendations through long-term memory of user preferences.
• Directed technical strategy and sprint planning across frontend and backend teams, driving code reviews, API design decisions, and release cycles while coordinating cross-functional collaboration between developers and designers.

Dynamic Platoon Formation of Multi-Type Autonomous Vehicles for Sustainable Urban Mobility

• Co-authored an IEEE ITSC-targeted research paper with Dr. Mohammed Eunus Ali, introducing a novel system where smaller Passive Vehicles (PVs) are physically towed by larger Active Vehicles (AVs) on shared highway segments, formulating the platoon formation problem as a constrained optimisation model with multi-segment matching and point-wise capacity constraints.
• Designed and implemented two matching algorithms — a Greedy Maximum-Weight Matching baseline and an Iterative Linear Assignment (ILA) method using the Jonker–Volgenant solver — achieving 25–52% energy-proportional distance coverage across synthetic scenarios with up to 400 PVs and 80 AVs.
• Built an end-to-end experimental framework in Python (NumPy, SciPy, Pandas, Matplotlib) with parameter sweep experiments, automated data pipelines, and comparative visualisation tools for algorithm benchmarking.

• Built core features for an AI-powered job application SaaS, including resume–job matching and automated application workflows using React, TypeScript, and Firebase.
• Improved deployment reliability by setting up CI/CD with GitHub Actions and Husky.
• Integrated RESTful APIs and optimised database queries for better scalability.

• Coordinated hardware lifecycle management across multiple Coles Divisions, ensuring smooth deployments, upgrades, and resolution of hardware-software integration issues across functional teams.
• Automated manual reporting processes using VBA (Microsoft Macro), reducing processing time from 4 hours to 30 minutes, streamlining asset tracking and improving data accuracy across the team.
• Optimised vendor management and asset processes, securing cost-saving agreements and contributing to over $90,000 in operational savings through lifecycle planning.

• GPA: 3.769 / 4.000 (High Distinction)
• 2023 & 2024 Monash Engineering Dean’s Honours recipient (Top 3% of engineering students)
• Working proficiency in Python, Java, TypeScript/JavaScript, Tailwind CSS, MongoDB, SQL, and PHP for software development; strong understanding of algorithms, data structures, and databases. Experienced with Linux, Angular, React, Meteor, Firebase, and Git. Industry experience developing Microsoft Excel Macros for system automation and debugging hardware using measurement tools.

• Graduated with GPA of 4.0 / 4.0 (High Distinction)
• Recipient: Engineering International Undergraduate Excellence Scholarship (awarded for ATAR equivalent >96 and exceptional academic performance)