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MRC Event – NCDS Webinar: Toward Machine Oblivious Graph Analysis
Start Date: 10/6/2015Start Time: 12:00 PM
End Date: 10/6/2015End Time: 1:00 PM

Event Description
MRC Event – NCDS Webinar: Toward Machine Oblivious Graph Analysis

Title: Toward Machine Oblivious Graph Analysis
Date: Tuesday, October 6, 2015
Time: 12:00 – 1:00 PM
Location: Rush Building, Room 213 (or register for the online webinar for free)

MRC Community Friends, including Drexel students, professional staff and faculty are invited to watch this NCDS webinar at the Metadata Research Center (MRC) on October 6th at noon.

The webinar, Toward Machine Oblivious Graph Analysis, will be presented by Erik Saule, NCDS Data Fellow and Assistant Professor in the Department of Computer Science at UNC Charlotte. In this talk, Saule will focus on his research as an NCDS Data Fellow regarding machine oblivious graph analysis. Graphs are a popular tool used to model a wide range of phenomena and to show the relationships among various entities. For example, in medicine, researchers use graphs to model regulatory pathways and gene expression, predict conditions, and identify the best drugs to use in treatments. Unfortunately, the explosion of digital data has led to a similar explosion in the computational costs of running graph analyses. New algorithms to deal with this challenge are usually inflexible, requiring the researcher to use a specific graph representation or a particular type of computer system for analysis. This talk will focus on Saule's work to develop a framework for performing efficient graph analysis regardless of the type of analysis being performed or the computer system used.

Please RSVP here: https://sites.cci.drexel.edu/rsvp/mrc-ncds-webinar-10-6-15/ - space is limited.

You may also independently register for this free Webinar at:
http://data2discovery.org/databytes-webinars/.

Contact Information:
Name: Adrian Ogletree
Email: aogletree@drexel.edu
rush building
Location:
Rush Building, Room 213 (or register for the online webinar for free)
Audience:
  • Undergraduate Students
  • Graduate Students
  • Faculty
  • Staff
  • Special Features:
  • Online Access

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