rockenzo

👋 Hello there, I'm Rock! 🙋

Welcome to my profile! As an aspiring Software Engineer with a solid foundation in web development, software engineering, and machine learning, I have a passion for solving complex problems and building scalable applications. Eager to collaborate with diverse teams, I aim to contribute to impactful projects and push the boundaries of tech.

🛠 Skills 👨‍💻

1.  🔧 Software Development 📱

• Languages:      
• Frameworks:  

2.  🌐 Web Development 💻

• Frontend:                

• Backend:      

• Databases:        

• Tools:      

  3.  🧠 Machine Learning 🤖

• Frameworks:      

• Experience: Neural network training, optimization, predictive modeling, computer vision.

4.  📊 Data Analysis 🧐

• Libraries:    

• Experience: Data manipulation and analysis.

5.   💬 LLM APIs and Model Localization 😶‍🌫️

• APIs:    
• Platforms:  
• Experience: Proficient in utilizing LLM APIs for integrating conversational AI into applications, and localizing models using platforms like Ollama for on-device or private cloud deployment.

6.  📋 Project Management 🗂️

• Methodologies: Agile, Scrum
• Experience: Leading projects, coordinating teams, meeting deadlines.

🛠 Project Experience 📑

• Engineered a web scraper leveraging LLM Ollama 3.1 for data parsing, extracting content from over 500 web pages on-premise with high precision.
• Developed a user-friendly interface with Streamlit, allowing users to input URLs, start scraping, and parse data seamlessly.
• Employed BeautifulSoup, Selenium, and lxml to manage various content sources, decreasing data extraction errors.
• Enhanced and tested the application, reducing errors and bolstering overall reliability.

• Conceptualized and built a web application to boost visibility and streamline administrative functions for volunteer groups, featuring user sign-ups, location management, event creation, and role-based profile management.
• Spearheaded the database schema design and led the development of the user management module, ensuring efficient data storage, retrieval, and seamless frontend integration for effective role and access management.
• Implemented secure authentication mechanisms using Argon2 for password hashing and Google OAuth for third-party login.
• Coordinated with frontend and backend teams to achieve a 20% reduction in development time ahead of schedule.
• Performed comprehensive testing and debugging to ensure high performance and user satisfaction.

• Created and honed a model for detecting football players in videos with the YOLOv5 framework for training and YOLOv8 for inference, achieving 92% accuracy.
• Sourced and pre-processed a dataset of 663 annotated images from Roboflow, enhancing model precision.
• Leveraged Google Colab’s GPU resources for efficient model training and tuning, reaching 97.5% precision in generating real-time bounding boxes around detected players.
• Minimized manual video analysis time by 85% through model performance optimizations.

• Developed a weather application using SwiftUI, providing real-time weather updates based on user location.
• Integrated CoreLocation to fetch and manage user location data for accurate weather information.
• Implemented a responsive UI with animated loading and launch screens to enhance user experience.
• Utilized the OpenWeather API to fetch reliable and up-to-date weather data.
• Designed custom views and extensions for streamlined UI components and improved code reusability.
• Conducted thorough testing and debugging to ensure application stability and performance.

• Developed a semantic segmentation pipeline for autonomous driving scenes using PyTorch.
• Implemented custom data loaders, augmentation strategies, and advanced network architectures to boost segmentation accuracy.
• Conducted ablation studies and tracked results with mIoU and FLOPs metrics for model efficiency.
• Documented findings and methods in a comprehensive report, highlighting improvements over the baseline.

• Engineered a reinforcement learning agent using Deep Q-Networks (DQN) to autonomously play Flappy Bird, achieving high scores and advancing through increasing difficulty levels.
• Refined the agent’s state representation and reward function, leveraging game environment parameters (e.g., bird position, pipe gap, velocity) to optimize learning and decision-making.
• Implemented experience replay and target network updates for stable and efficient training, following best practices from modern RL research.
• Utilized PyTorch for neural network modeling and training, ensuring scalable and reproducible experiments.
• Validated agent performance across multiple game levels, with automated evaluation and model checkpointing for robust benchmarking.

🎓 Education 📖

Bachelor of Information Technology: February 2023 – Present
University of Adelaide

• Relevant coursework:
  • Artificial Intelligence Technologies (Distinction)
  • Computer Systems
  • Object-Oriented Programming
  • Programming for IT Specialists (Distinction)
  • Web & Database Computing (Distinction)
  • Introduction to Applied Programming (High Distinction)
  • Introduction to Computer Systems, Networks, and Security (High Distinction)
  • Database and Ethical Data
  • Secure Programming
  • Using Machine Learning Tools
  • Computer Vision

📜 Certification 🏆

• HackerRank Software Engineer Certificate
  Credential ID: 1D18ED03135B
  • Skills: Problem-solving, Python programming, SQL databases, REST APIs
  • Issued: September 2024

📈 GitHub Stats 📊

🔗 Connect with Me 🕸️

Thanks for stopping by! Feel free to check out my repositories and get in touch if you’re interested in collaborating on any projects.