Presented By O'Reilly and Cloudera
Make Data Work
September 25–26, 2017: Training
September 26–28, 2017: Tutorials & Conference
New York, NY
Daniel Kang

Daniel Kang
Graduate Student, Stanford University


Daniel Kang is a PhD student in the Stanford InfoLab, where he is supervised by Peter Bailis and Matei Zaharia. Daniel’s research interests lie broadly at the intersection of machine learning and systems. Currently, he is working on deep learning applied to video analysis.


Secondary topics:  Deep learning
Daniel Kang (Stanford University)
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Video is one of the fastest-growing sources of data with rich semantic information, and advances in deep learning have made it possible to query this information with near-human accuracy. However, inference remains prohibitively expensive: the most powerful GPU cannot run the state of the art at real time. Daniel Kang offers an overview of NoScope, which runs queries over video 1,000x faster. Read more.