Big Data Chalk & Talk/Brown Bag: Nagi Gebraeel

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Date:
February 20, 2014 12:30 pm - 2:00 pm
Location:
MiRC 102 A&B

Four Georgia Tech research hubs have launched a new “chalk & talk” brown bag lunch series on Big Data. The weekly series, sponsored jointly by the Institute for Data & High Performance Computing (IDH), Institute for Materials (IMaT)Center for Data Analytics (CDA) and Center for High Performance Computing (HPC) will be held on most Thursdays during the Fall and Spring Semesters and feature a mix of topics, including those related to big data for materials and manufacturing, as well as other topics critical to the broader area of big data.

All meetings are held on Thursdays during lunchtime. 

Date: February 20
Topic: “Predictive Analytics for Improving the Reliability and Sustainability of Engineering Systems”
Presenter: Nagi Gebraeel

Abstract: 

Many high-valued engineering assets used in the manufacturing and service sectors are today being monitored by hundreds and perhaps thousands of sensors. Typically, the goal is to monitor system performance and degradation for numerous purposes, one of which is the prevention of unexpected failures. This talk focuses on how to effectively utilize sensor data to predict future system degradation and remaining lifetime (aka. prognostics).

The talk will begin by introducing a basic prognostic framework and how it has been implemented (through a live streaming demo, if possible). Next, the talk will focus on some scalability challenges that arise once we start dealing with big (sensor) data, which will be followed by a moderately technical discussion about some of the recently developed state-of-the-art modeling techniques that have targeted a few facets of this problem. 

Bio:

Nagi Gebraeel is the Chandler Family Associate Professor in the School of Industrial and Systems Engineering at Georgia Tech. He received his M.S. and Ph.D. degrees in industrial engineering from Purdue University in 1998 and 2003, respectively. Gebraeel’s research interests focus on leveraging condition-based sensor data streams to improve the predictability of unexpected failures of engineering systems and on improving subsequent operational and logistical decision making processes. He is a member of the Institute of Operations Research and Management Science (INFORMS), the Institute of Industrial Engineers (IIE), and the American Institute of Aeronautics and Astronautics (AIAA).