I'm passionate about researching, developing data processing and AI engineering packages, writing blogs, and participating in AI competitions.

🌟 Key Skills:

• Programming: Proficient in Python and R.
• Frameworks: Skilled in PyTorch, TensorFlow, and scikit-learn.
• Deep Learning: Experienced in implementing CNNs and Transformers for various tasks, including Image Segmentation, Object Detection, Optical Character Recognition (OCR), and Image Retrieval Model (with CLIP).
• Research Skills: Actively researched statistics, machine learning, and deep learning. I have three papers submitted to conferences and academic journals, with one as the first author.
• Data Pipeline: Experienced in SQL (BigQuery) and Airflow for building robust data pipelines, conducting A/B testing, and developing recommendation modules,
• Data Analytics: Proficient in e-commerce product and customer segmentation.
• Other Experiences:
  • Developed a package for missing data imputation, available at DIMVImputation.
  • Initiated a data-focused blog covering topics related to working with data, including semi-supervised learning and domain adaptation, available at maianh-learning.com.
  • Part-time involvement in logo detection projects (Object Detection).
  • Participated in an Information Retrieval competition involving CLIP models.

🚀 Packages:

• DIMV - A Data Imputation Package: DIMV is a powerful data imputation package designed to handle missing data efficiently. It offers robust imputation with regularization, ensuring reliable results. DIMV is easy to implement using a scikit-learn-style API and can be installed with a simple pip command. Explore the GitHub repository for detailed documentation and examples. Enjoy seamless data imputation with DIMV! Yay!

📝 Blog:

• Check out my latest articles on maianh-learning.com where I explore Semi-Supervised Method (to exploit small data for more general case) and Domain Adaptation (when the data distribution of the test set is not similar to train set) .
• VAE tutorials : I write a tutorial about Variational Auto Encoder (VAEs) (implementation) with illustration the differences compare to Auto Encoder and include example of application of VAEs in Topic Modeling
• Domain Adaptation tutorials : Another Tutorial of how to deal with domain shift problem by using re-weighting sample technique.

🤝 Let's Collaborate:

• I'm always open to new collaborations and exciting projects. Feel free to reach out to me at [email protected] or connect on LinkedIn.

📫 Contact Me:

• 📧 Email: Your Email
• 🌐 Website: maianh-leaning.com

📊 GitHub Stats:

Thanks for visiting my profile! Let's create something amazing together. 🚀