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