Worked in a Data and Systems Analysis (DSA) team collaborating with system engineers, data scientists, and machine learning specialists to develop fundamental product-facing and system-supporting architectures and tools to improve the surgeon experience, product adoption, and overall robotics architecture while connecting it to the hardware design.
- Built a machine learning framework by integrating machine learning libraries and operational frameworks into an end-to-end environment for data modeling, functional-space exploration, and guidance systems to improve patient outcomes, enhanced access and integration for surgeons and staff, and greater hospital efficiency.
- Engineered an ML pipeline to support development, experimentation, continuous integration, continuous delivery, verification/validation, and monitoring of AI/ML models.
- Collaborated with CE/RC/SI teams to improve the analysis efficacy and deliver actionable intelligence to improve procedure development and procedure adoption.
- Created documentation and best practices to share with the AI/ML community.