Hello! I'm
Luke Handley
Graduate Software Developer
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About Me
Education
First Class Honours - BSc Computer Science
BTEC Level 3 - ICT and Computing
(D*, D*, D)
Awards
The Henley College Education Award for ICT and Computing
Duke of Edinburgh Silver Award Scheme
I'm a First-Class Honours Computer Science graduate passionate about applying AI to create meaningful impact. I developed ZeroWasteCook, a full-stack AI application tackling food waste through image recognition and recipe recommendations, and led an Agile team building ChatAdvisor, an AI chatbot improving university support resources access for students with learning difficulties. Proficient in Python, Java, Django, PyTorch, and cloud-based AI services. I also recently completed certifications in responsible AI and prompt engineering whilst getting involved with the Thames Valley AI Hub and Reading Tech Cluster. Seeking my first opportunity as a Software Engineer or AI Engineer.
Interested In
Tech & Creative Interests
Web Application
Generative AI
LLMs
Agentic AI
Photography
Explore My
Experience
Frontend Development
HTML
Experienced
CSS
Experienced
JavaScript
Intermediate
AJAX
Intermediate
React.js
Basic
Flutter
Learning
Backend Development
Python
Experienced
Java
Experienced
C/C++
Intermediate
SQL
Intermediate
Google Cloud Platform
Basic
Microsoft Azure
Basic
REST APIs
Basic
Git/Github
Intermediate
AI/ML Development
R
Intermediate
Google Colab
Intermediate
PyTorch
Basic
Pandas
Basic
Matplotlib
Basic
NumPy
Basic
Pillow
Basic
CNN
Intermediate
KNN and Naive Bayes
Basic
Browse My Recent
Projects
Problem
Research shows university students are among the biggest contributors to household food waste in the UK. With rising living costs, many struggle to stretch their weekly shop and end up throwing away forgotten or unused ingredients. This wastes money, increases financial stress, and has a significant environmental impact.
Solution
ZeroWasteCook helps students save money and reduce food waste. Simply take a photo, upload or select the ingredients, and the app will recommend personalised recipes and add up estimated savings. It also tracks expiry dates, so nothing is forgotten before it goes bad. Turn leftover ingredients into simple, affordable recipes and cook smarter, not harder.
Key Features
- đĨĻ Ingredient Recognition (image upload & camera)
- đ Recipe Expiry Date Tracking with reminders
- đĢ Allergy & Preference Filters
- đ Smart Recipe Suggestions matched to available ingredients
- đŊī¸ Nutrition Insights per suggested recipe
- đą Environmental Impact Rating (A+ to F) for recipe choices
- đˇ Estimated Food Waste Savings
Core Technologies
- AI/Machine Learning: Python, PyTorch, InceptionV3 (CNN fine-tuning)
- Web App Framework: Django
- API Integration: Edamam Recipe Search API
- Development Methodology: Agile, Scrum, BDD (Behaviour-Driven Development)
- Dataset: Food Classification Kaggle dataset (61 ingredient classes) used for model fine-tuning
Achievements & Impact
- User research confirmed significant student interest (>70%) in the app's core features, highlighting potential for behaviour change toward sustainable cooking.
Problem
University students face difficulty navigating extensive support resources, especially when they have learning difficulties. Traditional methods like live chat, phone support or email are limited by staff availability, working hours, and cost, meaning students may not get timely help. This leaves many feeling stressed, unsupported, and unsure how to resolve concerns efficiently.
Solution
ChatAdvisor is an AI-powered chatbot that makes university support resources easy to access and understand. It summaries information, recommends relevant links based on individual queries, and provides personalised guidance anytime students need it. By reducing reading time and delivering tailored advice quickly, it helps students feel more confident, less stressed, and better prepared to handle their concerns.
My Contributions
- đ Integrated Apache Lucene to enable efficient information retrieval across institutional documents and FAQ pages.
- ⨠Leveraged Microsoft Azure AI Text Analytics for summarisation and entity extraction to produce concise, readable answers.
- đ¤ Configured and tuned OpenAI GPT-3.5 Turbo for empathetic, context-aware dialogue and fallback handling.
- đģ Led frontend UI development with Java Swing for a responsive, accessible desktop client used in pilot testing.
- âšī¸ Created a searchable FAQ page and UX flows to improve discoverability and reduce duplicate queries.
Core Technologies
- Apache Lucene (search & indexing)
- Microsoft Azure AI (Text Analytics)
- OpenAI GPT-3.5 Turbo (conversational responses)
- Java (backend & UI with Swing)
Achievements & Impact
- Delivered an AI chatbot prototype that improved information retrieval speed in testing and reduced time-to-answer for common support queries.
- Personalised responses and summarisation led to higher perceived usefulness and satisfaction, improving accessibility for students with learning difficulties.
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