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ChiehAnChang/README.md

Hi there πŸ‘‹

I'm Chieh-An (Andy) Chang, a Master's student in Data Science and Artificial Intelligence at the University of Waterloo πŸŽ“, with an expected graduation date between December 2026 and April 2027.

Before Waterloo, I graduated with High Distinction from the University of Toronto, where I completed a double major in Computer Science and Statistics.

A little about me

  • πŸ’» I'm interested in Data Engineering, Data Science, Machine Learning, and AI Engineering.

  • πŸ—οΈ I'm currently working as a Data Engineer Intern at a financial institution, where I help build and improve automated data pipelines. I wanted to gain hands-on data engineering experience because strong and reliable data is the foundation of any good machine learning system. Fortunately, I also had the opportunity to extend my internship until December 2026, giving me more time to learn and grow in this area.

  • 🀝 Outside of technical work, I enjoy meeting new people, sharing what I know, and helping others learn. During my time at the University of Toronto, I worked as a Teaching Assistant for computer science and statistics courses.

  • πŸš€ I'm currently exploring more of machine learning, deep learning, and AI through my Master's coursework and personal projects, mainly using PyTorch, TensorFlow, and Scikit-learn.

  • πŸ› οΈ My technical background includes Python, SQL, C#, C/C++, Spark, Databricks, Snowflake, and Azure.

  • πŸ” I'm looking for new graduate opportunities starting around April 2027, especially in Data Science, Machine Learning, AI, or Data Engineering.

  • πŸ† Fun fact: I ranked 6th in a Kaggle competition, made the Dean's List four times, and was nominated for the Vector Institute Scholarshipβ€”proof that statistics and coffee go well together β˜•.

Thanks for stopping by! Feel free to explore my repositories or reach out. I'm always happy to connect, learn something new, or build something interesting together!

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  1. Naive-RAG Naive-RAG Public

    Naive RAG implementation practice

    Python

  2. Reinforcement-Learning-Logic-Synthesis Reinforcement-Learning-Logic-Synthesis Public

    Exploring reinforcement learning for logic synthesis optimization using Berkeley ABC and DQN.