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Make Data Work
Oct 15–17, 2014 • New York, NY
Victor Fang

Victor Fang
Senior Data Scientist, Pivotal

Website

Dr. Chunsheng Victor Fang is a Senior Data Scientist at Pivotal. His Big Data expertise covers verticals e.g. large scale machine learning, video analytics, social network analysis, FDA approved medical imaging algorithms,etc. Prior to Pivotal / EMC, he has gained experience in real world machine learning and computer vision algorithm development and software architecture design, with a track of successful records in academia, leading eCommerce company, national laboratory, healthcare, etc. He holds a Ph.D of Computer Science and B.E of Electrical Engineering. What he believes: “Everybody lies” (Dr. Greg House), though data never does.

Sessions

9:00am–5:00pm Wednesday, 10/15/2014
Business & Industry
Location: 1 D03/1 D04
Jon Bruner (O'Reilly Media), Daniel Koffler (Rio Tinto Alcan), Ami Daniel (Windward), David Simchi-Levi (MIT), Victor Fang (Pivotal), Yu Cao (EMC), Nathan Oostendorp (Sight Machine), Alasdair Allan (Babilim Light Industries), Cameron Turner (The Data Guild), Leo Spiegel (Pivotal), Edy Liongosari (Accenture), Mark Grabb (General Electric Global Research Center)
Average rating: *****
(5.00, 3 ratings)
Big Data is reaching beyond the Internet and into the machines that drive our world. Visit Industrial Internet day to gain insights from the way that power plants, factories, cars, and airplanes make use of sensors and software intelligence to improve operations and help managers make good decisions. Read more.
11:45am–12:30pm Wednesday, 10/15/2014
Industrial Internet
Location: 1D03/1D04
Victor Fang (Pivotal), Yu Cao (EMC)
Average rating: *****
(5.00, 1 rating)
Video analytics is an important tool for traffic management, public security and more. A Video Analytics Data Lake (VADL) platform is the latest salvo in the constant battle to process and understand huge volumes of video data. The VADL enables advanced Data Science on video data including Real time analytics with In-Memory Databases, distributed queuing; Micro Batch and Mega batch with Hadoop. Read more.