Mohamad Danesh
Research Assistant at Mila - Quebec Artificial Intelligence Institute
Montreal, Quebec, Canada
Summary
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Rockstar🎓
Top SchoolMohamad Danesh is a research-focused machine learning engineer and PhD candidate at McGill with a decade of experience applying deep reinforcement learning to robotics and autonomy. Currently a research assistant at Mila and McGill, he bridges theory and practice—recent internships include work on sim-to-real dexterous manipulation with NVIDIA Isaac and investigations into multi-agent control dynamics at CHAI. He contributes to open-source tooling that eases ML framework interoperability, notably extending Ivy’s torch frontend with dozens of tensor operations to simplify cross-framework code translation. His background spans explainability and robustness in RL, motion planning under uncertainty, and applied ML engineering in industry, giving him a rare blend of academic rigor and production experience. Based in Montreal, he combines hands-on systems work with research that targets real-world robotic deployment.
10 years of coding experience
7 years of employment as a software developer
Amirkabir University of TechnologyMaster of Science - MS Computer Science, Master of Science - MS Computer Science at Oregon State UniversityDoctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at McGill University
Persian, English, French
Github Skills (10)
pytorch10
machine-learning10
translation10
python10
converter10
neural-network8
deeplearning-ai4
deep-learning4
jax4
tensorflow4
Programming languages (6)
Github contributions (5)
ivy-llc/ivy
May 2022 - Dec 2022
Convert Machine Learning Code Between Frameworks
Role in this project:
ML Engineer
ML EngineerContributions:11 reviews, 39 commits, 66 PRs in 7 months
Contributions summary:Mohamad's contributions center around modifying and adding functionalities within the Ivy framework, which focuses on converting machine learning code between frameworks. They primarily focused on updating and expanding the integration of the torch frontend. They added functions like permute, mean, transpose, flatten, cumsum, eq, inverse, neg, int, ne, squeeze, and flip. These changes involve implementing the torch frontend features for the Ivy project, which involves modifying existing code to incorporate new functionalities or adapt existing ones.
pythontensorflowframework-learningtemplatedata-science
modanesh/ObjectTracking
Aug 2017 - Jul 2019
Contributions:244 pushes, 1 branch in 2 years
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