On Classification with Unreliable Labels for Environmental and Medical Applications
Mar 07, 2012
from 11:00 AM to 01:00 PM
|Where||ENGR. IV Bldg. Shannon Room 54-134|
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Nabil Hajj Chehade
Advisor: Gregory J. Pottie
Information technology is undergoing yet another revolution, dubbed 'the data revolution'. The recent advancements in sensing technology, storage systems, computational systems, and mathematical tools are enabling the realization of systems that can observe the world at low cost, store large amounts of data, and run complex algorithms efficiently to process these large datasets. A key component in the design of such systems is the validation process where we need to evaluate the system on datasets representative of real life. In this work, we consider environmental and medical classification problems where the validation process is challenging due to the difficulty of collecting class labels and ground truth.
The talk is divided into three parts. In the first part, we present a system for tree type classification using satellite or aerial images. The system is used to update the current forest maps of the National French Forest Inventory (IFN).
In the second part, we present three motion recognition systems using wearable accelerometers designed for healthcare and medical applications. The first system is designed to monitor the workplace activities and study the seated posture habits of the user. The second one is designed to recognize the activity of the user from a set of 14 common daily activities. The third system is designed for stumble detection in analyzing the gait of the user, and studying the effect of frequent stumbles on the risk of falling. We also present two large datasets collected for the validation of the systems.
In the third part, we present a novel algorithm to collect data that optimizes the model selection in the maximum likelihood framework, for linear regression models used in spatial process estimation.
Nabil Hajj Chehade is a PhD student in Electrical Engineering, working with Prof. Greg Pottie. Nabil received his B.E. degree in Computer and Communications Engineering from the American University of Beirut in 2004, and his M.S. degree in Electrical Engineering from UCLA in 2007. Nabil spent 6 months in 2008 and 2009 at the ARIANA group of INRIA in France, and visited the Australian Center for Field Robotics (ACFR) of the University of Sydney for 3 months in 2011. His main research focus is on machine learning, data analysis, pattern recognition, large-scale optimization, and their applications in medical informatics and sensor networks.