Haanvid Lee
Machine Learning Researcher at RBC Borealis
Old Toronto, Ontario, Canada
Summary
👤
Senior🎓
Top SchoolHaanvid Lee is a Machine Learning Researcher at Borealis AI with 11 years of experience applying reinforcement learning and offline evaluation techniques to recommendation systems and financial services at RBC. He holds a PhD in Computer Science from KAIST, where his dissertation advanced off-policy evaluation methods, and an MS in Electrical Engineering focused on deep learning for video recognition. At Borealis he designs and evaluates offline contextual bandit and offline RL policies, then validates them through rigorous off-policy evaluation and online A/B testing. His background bridges theoretical RL research and practical deployment, enabling robust model selection under distributional shifts common in finance. Based in Toronto, he combines academic rigor with production-minded experimentation to turn research ideas into measurable business impact. An under-the-radar strength is his dual expertise in video-based deep learning and RL, which informs creative approaches to feature engineering and representation learning in recommendation tasks.
11 years of coding experience
Ph.D., Computer Science, Ph.D., Computer Science at 한국과학기술원(KAIST)Bachelor of Science (BS), Electrical and Electronic Engineering, Bachelor of Science (BS), Electrical and Electronic Engineering at 연세대학교
Github Skills (34)
gymnasium10
q-learning10
reinforcement-learning9
kubernetes9
python9
deep-learning8
google-colab8
machine-learning8
openai-gym8
theano7
simulations7
stable-baselines7
libtorch7
deep-reinforcement-learning7
risk-assessment6
Programming languages (6)
Github contributions (5)
KAIST-AILab/deeprl_practice_colab
Apr 2019 - Nov 2019
Preparation for Deep Reinforcement Learning using Google Colab
Contributions:10 commits, 1 PR, 47 pushes in 7 months
preparationdeep-learninggoogle-colabreinforcement-learningdeep-reinforcement-learning
haanvid/hello-world
May 2015 - Feb 2017
Contributions:1 release, 57 pushes, 1 branch in 1 year 9 months
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