I’m a professional Machine Learning Engineer and experienced Data Scientist with a strong foundation in control systems, planning, and decision-making. My work lives at the intersection of ML, NLP, CV, and DL — driving the development of practical, intelligent applications that push the boundaries of what machines can learn and do.

With hands-on experience in large-scale retail business environments, I’ve applied advanced machine learning techniques to optimize recommendation systems, customer personalization, and operational efficiency in real-world commercial settings.

Fluent in Python, Java, JavaScript, and C/C++, I build robust AI-powered systems and scalable software solutions. My contributions span from deploying production-grade ML pipelines to refining algorithmic efficiency and system design.

I’m also an advocate for open source, regularly contributing to and maintaining projects focused on NLP, algorithms, and applied machine learning.

• 📝 Visit C.Cui Blog to find interesting articles.
• 🔭 I’m currently a Tech Lead and working on recommendation systems for the largest retail business in Australia.
• 🌱 I have been practicing Software Product Management (SPM) for a few private projects related the stock market and funds analysis since 2019.
• 😎 Keep building on my project on Quantitative Trading and Stock Ranking Software.
• ⚡ Fun Fact: I originally planned to be an artist/painter, but accidentally got on the ship of Science and Technology~
• 🤔 2025 Goals:
  • Keep being role model and targeting the top 10% in Teams.
  • Keep supporting academic communities by offering feedbacks as reviewer and associate editor, please send review invitation to caihao.cui[at]ieee.org.
  • Keep writing my blogs of technology or algorithm applications on my website.
  • Publish 12 long blogs in 2025 on AI application in industry.
  • Contribute more to the Deep Learning Open Source projects (TensorFlow, PyTorch and PaddleX).

📕 Latest 10 Posts on My Blog

• Our Future with AI: Three Strategies to Ensure It Stays on Our Side
• 2024 Guest Lecture Notes: AI, Machine Learning and Data Mining in Recommendation System and Entity Matching
• Is the AI PC a Gimmick or a Faster Carriage?
• AI Revolutionizes Industry and Retail: From Production Lines to Personalized Shopping Experiences
• The Future of Coding: Will Generative AI Make Programmers Obsolete?
• Enigma – Mission X Challenge Accomplished with Python
• Prompt Engineering for LLM
• Technical Review 04: Human-Computer Interface from In-Context Learning to Instruct Understanding
• Technical Review 03: Scale Effects & What happens when LLMs get bigger and bigger
• Technical Review 02: Data and Knowlege for Large Language Model (LLM)

👨🏻‍💻 Open Source Project

SplitRaster

SplitRaster is a Python Package to split a large image into small tiles. It is useful for deep learning and computer vision tasks. The package can also be used to split a large image into small tiles with geo-information embedded, like tif, tiff.

• Github: https://github.com/cuicaihao/split_raster
• Pypi: https://pypi.org/project/splitraster/
• Tutorial with Docs: https://cuicaihao.github.io/split_raster/

📖 Tech Stack & Tools