Domain Adaptation and Generative Models for Single Cell Genomics with Gerald Quon

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Today we’re joined by Gerald Quon, assistant professor in the Molecular and Cellular Biology department at UC Davis.

Gerald presented his work on Deep Domain Adaptation and Generative Models for Single Cell Genomics at GTC this year, which explores single cell genomics as a means of disease identification for treatment. In our conversation, we discuss how Gerald and his team use deep learning to generate novel insights across diseases, the different types of data that was used, and the development of ‘nested’ Generative Models for single cell measurement.

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This week’s shows are drawn from some of the great conversations I had at the recent NVIDIA GPU Technology Conference, and they’re brought to you by Dell.

If you caught my tweets from GTC, you may already know that one of the announcements this year was a new reference architecture for Data Science Workstations, powered by high-end GPUs and accelerated software such as NVIDIA’s RAPIDS. Dell was among the key partners showcased during the launch, and offers a line of workstations designed for modern ML and AI workloads.

To learn more about Dell Precision workstations, and some of the ways they’re being used by customers in industries like Media and Entertainment, Engineering and Manufacturing, Healthcare and Life Sciences, Oil and Gas, and Financial services, visit Dellemc.com/Precision.

About Gerald

Mentioned in the Interview

“More On That Later” by Lee Rosevere licensed under CC By 4.0

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