Spatial-Temporal Brain: the spatial-temporal big data mining platform of Baidu

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                        Joint Seminar
Big Data Institute and Department of Computer Science & Engineering
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Speaker:        Dr. Haishan Wu
                Senior Data Scientist
                Baidu Research

Title:          "Spatial-Temporal Brain: the spatial-temporal big data
                 mining platform of Baidu"

Date:           Friday, 21 April 2017

Time:           3:00pm to 4:00pm

Venue:          Lecture Theater K (near lifts 31/32), HKUST

Abstract:

Each day Baidu handles more than 20 billion location requests from 600
million mobile users. In order to transform such large-scale data into
business insights, they develop a spatial-temporal data mining platform in
Baidu called STEB (Spatial-Temporal Brain). In this talk, Dr. Wu will
first show how STEB is used in user profile modeling, location based
advertising, cross-device tracking, user privacy quantification and credit
scoring system. STEB has also been applied in Smarter City applications,
and he will demonstrate two cases: chain store location selection system
and human crowd forecasting system for public safety. Most recently, STEB
underpins their data-driven economic measurement and investment decision
system MobiMetrics. He will show how MobiMetrics demystifies China’s
economy and shed light on answering the following questions: where are the
ghost cities in China exactly located? Can we measure the unemployment
rate of China? Is China shifting its economy from investment-led to
consumption driven? How does STEB accurately forecasts the Apple’s revenue
in Greater China for hedge fund investors?


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Biography:

Dr. Haishan Wu is a senior data scientist in Big Data Lab of Baidu
Research. He got his PhD from computer science department of Fudan
University in China in 2011. He then joined IBM Research focusing on
business data mining and analytics. Since 2012, he worked in Princeton
University as a postdoc researcher. He joined Baidu in 2014 and leads a
spatial-temporal data mining group. His research has been widely reported
by MIT Technology Review, New Scientist, Communication of ACM, The
Economist, Wall Street Journal, Bloomberg, BusinessWeek, Forbes, CNN
Money, Washington Post, NPR and so on.