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Make Data Work
September 11, 2018: Training & Tutorials
September 12–13, 2018: Keynotes & Sessions
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.


11:20am–12:00pm Wednesday, 09/12/2018
Location: 1A 08 Level: Advanced
Secondary topics:  Media, Marketing, Advertising
Daniel Kang (Stanford University)
Average rating: ****.
(4.00, 2 ratings)
Daniel Kang offers an overview of exploratory video analytics engine BlazeIt, which offers FrameQL, a declarative SQL-like language for querying video, and a query optimizer for executing these queries. You'll see how FrameQL can capture a large set of real-world queries ranging from aggregation and scrubbing and how BlazeIt can execute certain queries up to 2,000x faster than a naive approach. Read more.