Yashesh Savani
AI Researcher Engineer, Senior at Qualcomm
Vaughan, Ontario, Canada
Summary
👤
Senior🎓
Top SchoolYashesh Savani is a senior AI research engineer with eight years of experience building production-ready machine learning systems and computer vision applications, currently advancing AI research at Qualcomm. He combines a Master of Applied Computer Science from Dalhousie with hands-on expertise in Python, Flask, React, cloud services, and databases to deploy end-to-end solutions. His background spans startups to industry labs—shipping an on-device virtual personal trainer, developing YOLO-based visual inspection for steel manufacturing, and implementing facial-recognition and engagement analytics for real-world products. An active open-source contributor, he’s enhanced camera-driven interfaces to monitor model and camera FPS for real-time perception projects. Known for bridging research and engineering, he reliably turns novel models into performant, user-facing systems.
8 years of coding experience
3 years of employment as a software developer
Bachelor of Technology - BTech, Information Technology, Bachelor of Technology - BTech, Information Technology at Uka Tarsadia UniversityMaster of Applied Computer Science, Computer Science, Master of Applied Computer Science, Computer Science at Dalhousie University
English, Gujarati, Hindi
Github Skills (4)
opencv10
python10
performance-monitoring9
computer-vision9
Programming languages (5)
Github contributions (5)
quic/sense
Nov 2020 - Jul 2021
Enhance your application with the ability to see and interact with humans using any RGB camera.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:89 reviews, 415 commits, 51 PRs in 7 months
Contributions summary:Yashesh's commits focused on implementing performance metrics and display elements within the sense repository. Primarily, they introduced functionality to track and display camera and model frames per second (FPS) in the user interface. The user made modifications to the engine.py and display.py files, contributing to the user's ability to monitor FPS on the video screen.
pytorchdeep-learningcameracomputer-visionneural-networks
YasheshSavani/DAL-Serverless-learning-management-system
Aug 2020 - Apr 2021
Contributions:1 review, 1 PR, 2 pushes in 8 months
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