I'm a machine learning engineer and researcher specializing in probabilistic deep learning and digital signal processing. I work with images, speech, and text data, but also have experience with bioinformatics data.

I'm based in 🇫🇮 🇻🇳 and working as a Senior Machine Learning Engineer.

In my quasi-free time, I write open-sourced deep learning framework in both Tensorflow and Pytorch.

As a highly motivated and curious individual, I approach my work with unwavering responsibility and integrity.

🎯 Notable works:

• Covid-Cough-Detection: using transformer and self-supervised learning for detecting covid from recorded cough audio file.
• ODIN: 28 different implementation of variational autoencoder and disentangled representation learning.
• SISUA: semi-supervised single-cell modelling.
• bigarray: transparent memory-mapped array for multiprocessing (535 times faster than HDF5 data).

⚡ Fun projects:

• Neural style wedding card: I used neural style transfer to create design for my wedding card
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• Customer support chatbot: an "after-dinner" project, feeding all the blogs from Veri and make ChatGPT answer questions about the service
  
• Brick racing: Android version of the classic brick racing game
  
• Coconerd: code count for nerd.
• VSCode Autoflake: extension for removing unused imports and unused variables from Python code.

💡 Teaching:

• UEF summer schools: 2016, 2017 and 2018
• UEF workshops: speech processing (2017) and sequence learning (2018)
• Bayesian inference 2017: using PyMC and Edward.
• Python for Computational Intelligence (2017): collection of Colab notebooks, recommended if you start using python solve some simple machine learning problems.
• Deep learning course (2019): 7 homeworks and 3 mini-projects, if you are motivated to solve the problems, please contact me via email.

😎 Hobbies:

• Climbing 🗻
• I am also an avid reader of mountaineering and science history.
• I also using ML for algorithmic trading and quant (just for fun).

📫 Contact me at or via Linkedin