Charlie Chang
Scientific Systems Designer @Institut De Recherche D\'hydro-Québec Research Institute Of Hydro-Québec
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WORK HISTORY
Scientific Systems Designer @Institut De Recherche D\'hydro-Québec Research Institute Of Hydro-Québec
Varennes, QC, CA
Member of Scientific Information System team. Working closely with the researchers to design and implement different prototype solutions with latest technologies- Developed and fine-tuned Computer Vision model for electrical pole and equipment detection with 90% accuracy- Created auto annotation solutions to improve the quality of various datasets- Testing different solutions including SLM, VLM, 3D annotation, 3D reconstruction and end to end ML pipelines- Involved in robot development and simulation with Nvidia Isaac Sim / ROS2.• Designed and implemented a dynamic form system using Angular.• Working with researchers on a web-based forecast system to predict the lifecycle of the equipment depending on the inspection results.• Participated in the AR/VR research project to explore utilities of Microsoft HoloLens such as employee training, equipment detection and data collection.• Contributed on a hydrology forecast project using Python and different data assimilation tools.• Working on various IoT projects using Microsoft Azure IoT solution.
EDUCATION
McGill University
Bachelor, Computer Science and Mathematics
SKILLS
ABOUT CHARLIE CHANG
Solution Architect | AI, Computer Vision & RoboticsSolution Architect and Full Stack Developer with 20+ years in IT, currently focused on AI-driven systems, computer vision, and robotics technologies. Hands-on experience training 2D and 3D computer vision models, and exploring robotics simulation and autonomy using NVIDIA Isaac and modern robot models.Actively experimenting with state-of-the-art multimodal foundation models, vlm and slm to integrate AI capabilities into real-world, production-ready solutions.Strong background in cloud and service-oriented architecture, C#.NET Core, Python, Angular, Azure DevOps, and data-driven systems. Known for bridging enterprise-scale engineering with cutting-edge AI exploration, while maintaining clean architecture, testability, and reliability.
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