Koorosh Moslemi is a PhD candidate and research assistant at the University of Toronto with eight years of experience in reinforcement learning and robotics, focusing on multi-agent systems, team formation, hierarchical planning, and test-time adaptation. He blends rigorous academic training in industrial engineering with a strong computer science foundation from Amirkabir University, graduating near the top of his class. His contributions to an open-source machine learning course—enhancing linear algebra, calculus, and optimization material—reflect a talent for clarifying foundational math for ML practitioners. At U of T he applies theoretical methods to robotic settings, aiming to bridge algorithmic advances and real-world deployment. Colleagues describe him as research-driven yet hands-on, with an uncommon knack for translating complex reinforcement learning ideas into teachable, reusable content.
8 years of coding experience
Amirkabir University of Technology
Doctor of Philosophy - PhD, Industrial Engineering, Doctor of Philosophy - PhD, Industrial Engineering at University of Toronto
High School Diploma, Mathematics and Computer Science, High School Diploma, Mathematics and Computer Science at National Organization for Development of Exceptional Talents (Sampad)
Machine Learning Course, Sharif University of Technology
Role in this project:
Data Scientist
Contributions:2 reviews, 6 commits, 5 pushes in 21 days
Contributions summary:Koorosh contributed significantly to the machine learning course by adding and updating content related to linear algebra, calculus, and optimization. These additions include detailed explanations, examples, and references within Jupyter Notebooks, forming a core part of the course material. Furthermore, the user also updated the calculus section with proper referencing and enhanced the overall content.
A complete store web application with laravel and vue.js
Contributions:7 commits, 6 pushes, 1 branch in 1 day
vue-jslaravel-8web-applicationvuelaravel
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