As a fourth-year Computer Science student with a deep passion for Artificial Intelligence and Networking, my journey into the world of AI began during high school when I first witnessed Tesla unveil its autopilot driving technology. This moment sparked a curiosity that has since grown into a relentless drive to understand how AI can transform our lives and reshape the future.

Fueled by this passion, I pursued a degree in Computer Science with a specialization in AI, building a strong foundation in programming languages such as Python, Java, and C, as well as expertise in data structures, algorithms, and computer vision. My academic journey has allowed me to explore the fascinating intersection of AI and software engineering, and I am particularly intrigued by how AI can be applied to solve real-world problems and enhance everyday life.

As I transition into my final year, I am eager to deepen my understanding of Artificial Intelligence and Networking and contribute to innovative projects that leverage cutting-edge technologies. My ultimate goal is to advance the boundaries of AI, working on impactful solutions that bridge technology and human experiences.

With a passion for continuous learning and a commitment to fostering growth and innovation, I am excited about the opportunities to make meaningful contributions to this rapidly evolving industry as an AI Engineer.

Here's a bit more about myself:

• Artificial Intelligence (AI): I have a keen interest in AI, from machine learning algorithms to machine learning and computer vision.
• Networking: Understanding the intricacies of network protocols and architectures fascinates me.

I'm proficient in a variety of programming languages and AI/ML libraries:

• Python
• Java
• C++
• TensorFlow
• PyTorch
• Pandas
• NumPy
• Matplotlib

• National University of Singapore (NUS)
  • Bachelor of Computing in Computer Science
  • Focus Area: Artificial Intelligence and Computing Network
  • Student Exchange Program: Nanjing University
  • Expected Graduation: August 2025

Here’s an updated Project Showcase section for your README, including your requested projects with key contributions, learning points, and key features:

Key Contributions:

• Developed both frontend and backend components using ReactJS and FastAPI.
• Designed user authentication with Firebase and implemented secure admin rights management for question handling.
• Integrated a real-time collaboration tool using Socket.IO for smooth, low-latency coding between users.

Key Features:

• Real-Time Collaboration: Allows users to collaborate in a shared code editor with instant synchronization using Socket.IO.
• Question System: Includes question filtering by difficulty, real-time status updates on coding questions, and an integration with Judge0 for code execution.
• Matching System: Matches users based on skill level and provides in-session chat and video calling features to enhance collaboration.

Learning Points:

• Learned the intricacies of real-time systems and how to efficiently manage concurrent connections.
• Gained hands-on experience with microservices architecture for better modularity and scalability.
• Deepened understanding of continuous deployment pipelines using GitHub Actions for automated testing and deployment.

Feel free to check out my PeerCode Repository for a detailed look into the project!

Key Contributions:

• Built a CNN model using PyTorch to recognize and classify handwritten digits (MNIST) and images (CIFAR-10).
• Implemented data augmentation techniques to improve model generalization.
• Used Class Activation Maps (CAM) to visualize the parts of the image that influenced the model’s predictions.

Key Features:

• Model Architecture: Includes Conv2D, MaxPooling, Dropout, and Leaky ReLU layers, with Softmax for classification.
• Performance Visualization: Confusion matrices were generated to evaluate prediction accuracy across different classes.
• Data Augmentation: Employed techniques such as rotation, flipping, and scaling to improve training performance.

Learning Points:

• Learned how to fine-tune hyperparameters and optimize CNN architectures for improved model accuracy.
• Gained practical experience with PyTorch and deep learning libraries like torchvision and Matplotlib.
• Enhanced skills in model interpretability through CAM and understanding neural network predictions.

Key Contributions:

• Developed a web crawler using BeautifulSoup to scrape tech news websites and index articles for searching.
• Built an inverted index and implemented a basic TF-IDF-based search for document retrieval.
• Integrated txtai to enable advanced semantic search for enhanced query understanding.

Key Features:

• Crawling and Indexing: Automatically collects and processes tech news articles from various sources for indexing.
• Basic and Advanced Search: TF-IDF for keyword-based search, and txtai for advanced semantic search based on query intent.
• User Interface: Streamlit frontend for a user-friendly, clean interface that allows users to query and retrieve tech news articles.

Learning Points:

• Improved understanding of information retrieval techniques, including inverted indexing and TF-IDF ranking.
• Learned how to implement advanced search capabilities with AI libraries for semantic understanding of queries.
• Gained experience in designing simple yet effective frontends using Streamlit to display search results.

Key Contributions:

• Developed an AI agent to play the strategic game "Breakthrough" using a 6×6 board.
• Implemented a minimax search algorithm with alpha-beta pruning to optimize decision-making.
• Created an evaluation function to assess board states and make strategic moves.

Key Features:

• Minimax Algorithm: Optimized with alpha-beta pruning to reduce search space and improve decision efficiency.
• Evaluation Function: Designed to evaluate game board states for intelligent move selection.
• Scalability: Future improvements planned for enhancing the AI's performance and adding more complex game mechanics.

Learning Points:

• Gained experience in implementing game-playing AI algorithms like minimax and alpha-beta pruning.
• Improved my understanding of search algorithms, particularly in reducing computational complexity for real-time decisions.
• Enhanced my skills in Python and algorithmic problem-solving through iterative development and testing of the AI agent.

Feel free to explore my AI-Mini-Project-Breakthrough-Game Repository for a deeper dive into the project!

Apart from coding, I'm interested in:

• Reading about the latest advancements in AI and networking.
• Exploring new technologies and frameworks.
• Doing sports during free time

Here are some of the certifications I've completed related to AI:

• Introduction to Generative AI (Duke University, Jan 2025)
• Transfer Learning for NLP with TensorFlow Hub (Coursera, Jan 2025)
• Basic Image Classifications with TensorFlow (Coursera, Jan 2025)
• Deep Learning Applications for Computer Vision (Coursera, Sep 2024)
• Deep Learning with Python and PyTorch (edX, Feb 2024)
• Data Science: Machine Learning (edX, Dec 2023)
• Google AI for JavaScript Developers with TensorFlow.js (edX, Sep 2023)
• Creating Multi-Task Models With Keras (Coursera, Aug 2023)
• Deep Learning with PyTorch: Image Segmentation (Coursera, Jul 2023)

• LinkedIn: Foo Dun Liang
• Email: dunliangfoo0513@gmail.com

Feel free to explore my repositories and connect with me! I'm always open to collaboration and new opportunities.