Rasool Fakoor
Member Of Technical Staff at Boson AI
United States
Summary
👤
Senior🎓
Top SchoolRasool Fakoor is a research-focused machine learning engineer with a decade of experience leading reinforcement learning teams and building large language models at scale, most recently heading RL research at AWS AI Research and contributing to AWS’s Large Models Initiative via RLHF. He combines deep academic training (PhD-level work) with hands-on engineering, having implemented practical RL modules for widely adopted open-source resources like the d2l-en deep learning book used at hundreds of universities. Rasool has a track record of transitioning research into production-grade systems across top labs and industry teams (Microsoft Research, Amazon/Alexa, Fundamental Research Labs, Boson AI). He specializes in efficient RL and agentic model training for real-world, large-scale challenges and is known for integrating research rigor with pragmatic engineering changes (e.g., gym integration and framework tooling).
11 years of coding experience
9 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at The University of Texas at Arlington
Kurdish, Persian, English
Github Skills (10)
gymnasium10
openai-gym10
notebook10
pytorch10
machine-learning10
deeplearning-ai10
deep-learning10
python10
reinforcement-learning10
data-science9
Programming languages (6)
Github contributions (5)
d2l-ai/d2l-en
Jan 2022 - Dec 2022
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
Role in this project:
ML Engineer
ML EngineerContributions:9 reviews, 18 commits, 16 PRs in 11 months
Contributions summary:Rasool primarily contributed to the reinforcement learning (RL) chapter of the deep learning book. They implemented and updated notebooks related to RL concepts, including Markov Decision Processes (MDP) and value iteration. The user integrated the gym library and made corresponding code modifications to the d2l/torch.py and setup.py files, indicating a focus on practical RL implementations within the existing deep learning framework. Several commits involved updating and refining the content based on feedback and fixing issues related to the gym library and mathematical equations.
deep-learningmachine-learningnotebookcomputer-visionnatural-language-processing
zhanghang1989/p3o
Jun 2019 - Jun 2019
Contributions:30 commits in 3 days
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