Ji Yang
Partner (Tech And AI) at AltaML
Edmonton, Alberta, Canada
Summary
🤩
Rockstar🎓
Top SchoolJi Yang is a Partner in Tech and AI with 11 years of engineering and research experience, currently pursuing a PhD in Computer Vision and Machine Learning at the University of Alberta. A Kaggle Competition Master (world rank ~300) with multiple medals, he blends top-tier competitive ML skills with applied research in 3D pose, shape, and object reconstruction. He co-founded a generative AI healthcare startup and helps build AI products for international students at CollegeLand, demonstrating a strong founder-to-product mindset. His open-source contributions span deep learning education and practical ML projects—ranging from LSTM stock predictors to UI improvements in an Electron-based WeChat client—highlighting both backend model work and frontend UX polish. Comfortable moving between research, production ML, and product engineering, he brings rare cross-domain fluency useful for turning complex vision models into usable systems.
11 years of coding experience
3 years of employment as a software developer
Doctor of Philosophy - PhD Computer Vision and Machine Learning, Doctor of Philosophy - PhD Computer Vision and Machine Learning at University of AlbertaDeep Learning Nano Degree Artificial Intelligence, Deep Learning Nano Degree Artificial Intelligence at UdacityIPP, IPP at China Foreign Affairs University
Stackoverflow
881reputation
79kreached
5answers
16questions
Github Skills (28)
javascript10
python10
electron10
css10
pandas10
machine-learning10
numpy10
data-preprocessing10
keras10
lstm10
deep-learning10
html10
logistic-regression10
linux9
time-series9
Programming languages (12)
Github contributions (5)
Kulbear/deep-learning-coursera
Aug 2017 - Jun 2018
Deep Learning Specialization by Andrew Ng on Coursera.
Role in this project:
ML Engineer
ML EngineerContributions:43 commits, 10 PRs, 35 pushes in 10 months
Contributions summary:Ji implemented and demonstrated a logistic regression model with a neural network mindset. The user worked on logistic regression with a neural network implementation with the creation of several functions to initialize parameters, calculate the cost function, and compute its gradient. The user combined the functions into a main model function to recognize cats.
deep-learning
Kulbear/stock-prediction
Apr 2017 - Jul 2018
Stock price prediction with recurrent neural network. The data is from the Chinese stock.
Role in this project:Data Scientist
Data ScientistContributions:10 commits, 1 PR, 9 pushes in 1 year 3 months
Contributions summary:Ji contributed significantly to a stock price prediction project. Their work involved data preparation, including importing and manipulating stock data using the `tushare` library in Python, as demonstrated by the "Data Preparation" notebook and supporting script. Furthermore, the user implemented and documented an LSTM-based recurrent neural network model for stock price prediction.
recurrent-neural-networksstock-price-prediction
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