Daniel Ferreira

• Web-based implementation of Conway's Game of Life with modern UI
• Multiple rule sets beyond the standard Conway rules
• Pattern library with classic structures
• Real-time simulation with adjustable speed
• Data visualisation of population metrics
• Customisable grid colours and appearance

• Collaborated closely with a team of 3 other members to develop an artificial intelligence (AI) research assistant built using Python to automate data retrieval, thereby increasing research productivity in computer science
• Improved the LongT5 model's ability for summarization by fine-tuning it on a 1,000-row dataset of computer science research papers
• Implemented document scraping, machine learning, and NLP techniques for comprehensive information retrieval, data analysis, and summarization, resulting in efficient processing of large volumes of data

• Utilised the Streamlit web framework to build a real-time fake news detection app.
• Implemented a machine learning model for text classification using the LinearSVC algorithm, trained on a dataset of 44,898 articles, enhancing detection accuracy up to 98.39%
• Used PySpark for analysis and matplotlib for graph visualization.

• Designed and developed a recipe management app using the Flutter framework, enhancing user experience through intuitive UI/UX design
• Implemented Cloud Firestore as a scalable NoSQL database to store recipe data, user carts, and order tracking information, resulting in improved app performance and scalability
• Integrated RazorPay as a payment gateway solution, enabling users to seamlessly purchase recipe ingredients directly through the app, enhancing user convenience and transaction efficiency Features: 1. Add their recipes 2. View recipes uploaded by other users 3. Purchase any ingredients from the recipe screen 4. View the ingredients added to the cart 5. View the Order history after their purchase 6. Favorite recipes made by other users

• Leveraged a Decision Tree model, trained on student data, to achieve an 88.2% accuracy in placement prediction
• Designed and built a Placement Prediction System to analyze student data and predict their placement chances
• Integrated machine learning with a MySQL database for user authentication, ensuring secure and efficient data handling

• Developed using Flutter framework to create robust and user-friendly mobile application.
• Utilized the SharedPreferences API to store user setting and scores.
• Implemented Material 3 (Material You) design to create more visually appealing application.

Hugging Face

Gradio Agents & MCP Hackathon 2025

Issued Jun 2025

Credential ID: GRADIO2025-7E3AA736

Microsoft

Microsoft Certified: Azure AI Fundamentals

Issued May 2025

Meta

Meta Back-End Developer Specialization

Issued Dec 2023

Credential ID: 98E6953LKYWE

Udemy

Flutter & Dart - The Complete Guide

Issued Sep 2022

Credential ID: UC-8f1c1be5-bd66-4e10-a92e-5e8568e68323