Saqib Azim is a research engineer based in San Francisco with a decade of experience building AI and computer vision systems across industry and academia. He holds an MS in Artificial Intelligence from UC San Diego and has applied ML, reinforcement learning, and robot learning to problems ranging from adversarial robustness of CLIP to visual SLAM and navigation at Hitachi and Guide Labs. Saqib blends hands-on engineering with research rigor—training provably robust classifiers, devising adversarial attacks, and improving real-world perception stacks for navigation and hazardous-activity detection. He’s an active open-source contributor to projects like the cross-platform Python UI framework Kivy, where he’s fixed image saving, UI selection behavior, and other subtle bugs. Comfortable teaching core ML and signal courses, he also has a track record of translating sensor-noisy problems into practical solutions, such as achieving high accuracy in smartwatch-based handwriting recognition. This mix of production-minded engineering, published research experience, and open-source contribution makes him effective at moving novel ML ideas into deployable systems.
Open source UI framework written in Python, running on Windows, Linux, macOS, Android and iOS
Role in this project:
Full-stack Developer
Contributions:21 commits, 20 PRs, 64 comments in 2 months
Contributions summary:Saqib contributed to various aspects of the Kivy framework, including fixing image saving errors and updating app configuration files. They also worked on UI-related issues by addressing selection behaviors in `CompoundSelectionBehavior` and text input bubble functionalities. Additionally, the user addressed multiple pep8 errors and fixed a bug with action view maximizing/minimizing.
Contributions:177 commits, 257 pushes, 1 branch in 4 years 2 months
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