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Amir
Emadzadeh
Director, Software Engineering
Genentech
Amir Emadzadeh is a distinguished technology leader and innovator in the life sciences and autonomous systems industries. Currently serving as Director, Software Engineering at Genentech (Roche), he leads transformative initiatives in clinical trial automation and AI-driven healthcare solutions. His groundbreaking work includes developing patent-pending platforms that have significantly accelerated clinical data workflows and enhanced trial efficiency. Prior to Genentech, Amir held leadership positions at Gilead Sciences, Waymo, and Nvidia, where he pioneered advances in autonomous vehicle technology and medical image analysis. His expertise spans machine learning, signal processing, & algorithm development, with particular focus on healthcare applications & autonomous systems. Amir holds a Ph.D. in Electrical Engineering from UCLA, where his research on spacecraft navigation using X-ray pulsars led to a book published by Springer. He has been granted numerous patents and published extensively in prestigious journals. Currently serving as an adjunct professor at California Science and Technology University, Amir frequently speaks at major industry conferences on AI and clinical trial innovation. His work consistently demonstrates a commitment to pushing technical boundaries while maintaining practical applicability, particularly in healthcare & autonomous systems. Amir is recognized for his ability to lead diverse teams & drive innovation in complex technical environments.
09 October 2025 13:00 - 13:30
Panel | Defining and ensuring "high-quality data” in the context of modern AI systems
In modern AI systems, “high-quality data” is more than accuracy - it’s about relevance, diversity, governance, and adaptability over time. This panel brings together data scientists, engineers, and AI leaders to define what quality truly means in the era of generative and agentic AI. Panelists will share practical methods for building robust data pipelines, leveraging synthetic data, and implementing governance frameworks that support scalable, compliant, and high-performing AI workloads.

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