About this Event
Cannot attend in person? Join us online: https://utoronto.zoom.us/j/9296385639
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Abstract:
Artificial intelligence (AI) is growing quickly in the technology market, and machine learning is an integral part. Recent advancements in machine learning provide new and innovative ways to solve traditional engineering problems for scientific discoveries.
Dr. Yao Fehlis will present major considerations for efficiency in AI and HPC applications, focusing on scientific computing. Then she will discuss a potential collaboration between AMD and Acceleration Consortium with an existing collaboration that AMD has with another research institution. Finally, Dr. Fehlis will open up discussion on potential synergy between efficiency in AI-for-Science and self-driving laboratories.
Bio:
Dr. Yao Fehlis works at AMD Research, and her focus involves AI-for-Science and AI for manufacturing. In AI-for-Science, she works internally and externally with academia to enhance traditional HPC applications with machine learning to accelerate scientific discoveries. In AI for manufacturing, she works internally with product teams to use machine learning to uncover optimal parameters and configurations in AMD designs. Prior to joining AMD, she worked as a data scientist at KUKA Robotics where she led predictive maintenance projects for industrial KUKA robots and worked on deep learning projects such as teaching robots to pick up objects. She holds a PhD in computational chemistry from Rice University.
Event Venue & Nearby Stays
Sandford Fleming Building (and online), 10 King's College Road, Toronto, Canada