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WORLDCOMP'11 Tutorial: Dr. Gary Weiss

Last modified 2011-07-06 06:13


Smart Phone-Based Data Mining
Dr. Gary M. Weiss
Fordham University, USA

Date: July 19, 2011
Time: 6:00pm - 7:30pm
Location: Ballroom 1


DESCRIPTION

    Smart phones have exploded in popularity in recent years and are now the most common computing devices, having surpassed personal computers. While smart phones, and other related devices such as tablet computers, now run sophisticated operating systems and include substantial processing power and memory, they are more than computing and communication devices—they are sophisticated sensors. This becomes clear when you realize that these devices typically contain a: GPS sensor, acceleration sensor (accelerometer), audio sensor (microphone), image sensor (camera), light sensor, direction sensor (compass), proximity sensor, temperature sensor, and pressure sensor. The availability of these sensors in mass-marketed mobile devices creates exciting new opportunities for data mining and data mining applications. In this tutorial I will survey the data mining applications that can be built using these sensors, the data mining methods used to extract information from these sensors, and the practical and architectural issues that relate to data mining of sensor data from devices with relatively limited resources (e.g., battery life). I will also discuss how sensor data from a population of smart phones can be pooled (crowdsourcing) to provide useful knowledge and interesting applications. This tutorial is intended for anyone interested in the topic and those from other research areas (e.g., wireless networks) should be able to learn much from the tutorial.

BIOGRAPHY

    Gary Weiss is a faculty member in the department of Computer and Information Science at Fordham University. He earned his B.S degree from Cornell University, his M.S. degree from Stanford University, and his Ph.D. from Rutgers University. Prior to coming to Fordham he worked for over 15 years at AT&T Bell Labs and AT&T Labs. Until recently, his research has focused on how various real-world factors, such as class imbalance, affects the ability to learn from data. This led to several KDD workshops on Utility-Based Data Mining and a special issue of the Data Mining and Knowledge Discovery journal on this topic. For the past two years Dr. Weiss has led a dozen students on the WISDM (Wireless Sensor Data Mining) project. Recent work has focused on mining accelerometer data from smart phones and this has led to publications on cell phone-based activity recognition and cell-phone based biometric identification. Dr. Weiss has I have published over forty papers in the areas of machine learning and data mining as well as several in the area of expert systems and object-oriented programming.

Academic Co-Sponsors
The Berkeley Initiative in Soft Computing (BISC)
University of California, Berkeley, USA

Biomedical Cybernetics Laboratory, HST of Harvard University
Massachusetts Institute of Technology (MIT)

Intelligent Data Exploration and Analysis Laboratory
University of Texas at Austin, Austin, Texas, USA

Collaboratory for Advanced Computing and Simulations (CACS)
University of Southern California, USA

Minnesota Supercomputing Institute
University of Minnesota, USA

Knowledge Management & Intelligent System Center (KMIS)
University of Siegen, Germany

UMIT, Institute of Bioinformatics and Translational Research, Austria
BioMedical Informatics & Bio-Imaging Laboratory
Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA


Hawkeye Radiology Informatics, Department of Radiology, College of Medicine, University of Iowa, Iowa, USA

Supercomputer Software Department (SSD), Institute of Computational Mathematics & Mathematical Geophysics, Russian Academy of Sciences
SECLAB of University of Naples Federico II
University of Naples Parthenope, & Second University of Naples, Italy

Medical Image HPC & Informatics Lab (MiHi Lab)
University of Iowa, Iowa, USA

Intelligent Cyberspace Engineering Lab., ICEL, Texas A&M; University (Com./Texas), USA
Model-Based Engineering Laboratory, University of North Dakota, North Dakota, USA


Corporate Sponsor

Intel Corporation



Altera Corporation

Pico Computing

High Performance Computing for Nanotechnology (HPCNano)

International Society of Intelligent Biological Medicine

World Academy of Biomedical Sciences and Technologies
The International Council on Medical and Care Compunetics
The UK Department for Business, Enterprise & Regulatory Reform
Scientific Technologies Corporation

HoIP - Health without Boundaries


 


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