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Tutorial - Dr. Gary Weiss

Last modified 2014-07-04 17:45

Smartphone Sensor Mining Applications: Ubiquitous Possibilities
Dr. Gary Weiss
Director, Wireless Sensor Data Mining (WISDM) Lab
Department of Computer and Information Science
Fordham University in New York City, New York, USA

Date: July 23, 2014 - 5:40pm
Location: Ballroom 1


    Smart phones have exploded in popularity over the past half dozen years and these devices are now not just ubiquitous, but powerful and packed with sensors such as an accelerometer, gyroscope, GPS, compass, barometer, audio sensor (microphone), image sensor (camera), and light sensor. We are now also beginning to see these sensors migrate to smartwatches, which are primed to explode in popularity. These mobile sensors make exciting new applications possible. In this tutorial I will survey some of these sensor mining applications, discuss the underlying technology and technology challenges, and describe the role of data mining in these applications. The new breed of applications that have recently hit the market will allow our mobile devices to become truly intelligent and context-aware, and allow them to learn a lot about us and our immediate environment. This tutorial is intended as an introduction to the area and is appropriate for anyone who is interested in the area.


    Dr. Gary Weiss is a faculty member in the department of Computer and Information Science at Fordham University in New York City. He is the Director of the Wireless Sensor Data Mining (WISDM) Lab, which explores how smartphone and other mobile sensors can be used to support human activity recognition and related applications. The WISDM Lab, which is supported by grants from the U.S. National Science Foundation, Google, and several other corporations, recently released the actitracker activity tracking app ( Prior to coming to Fordham, Dr. Weiss worked at AT&T Labs as a software engineer, expert system developer, and finally as a data scientist. He received a B.S. degree in Computer Science from Cornell University, an M.S. degree in Computer Science from Stanford University, and a Ph.D. degree in Computer Science from Rutgers University. He has published over fifty papers in machine learning and data mining.
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