[CSEE-colloq] Colloqium - Oleg Aulov on Human Sensor Networks; 1pm Fri 9/14, UMBC

[Yaacov Yesha]

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From: Tim Finin <finin at cs.umbc.edu>
Date: Mon, Sep 10, 2012 at 11:04 PM
Subject: [Csee-faculty-tt] [CSEE-colloq] Oleg Aulov on Human Sensor
Networks; 1pm Fri 9/14, UMBC
To: csee-colloquium-out at cs.umbc.edu


                         UMBC CSEE Colloquium

             Human Sensor Networks for Improved Modeling
                of Natural and Human-Caused Disasters

           Oleg Aulov, Computer Science Ph.D. Student, UMBC

           1:00pm Friday, 14 September 2012, ITE 227, UMBC


This talk will discuss the importance of different roles that social
media can play in management, monitoring, modeling and mitigation of
natural and human-caused disasters. We will present a novel approach
that views social media data as a human sensor network. These data can
serve as a low-cost augmentation to an observing system, which can be
incorporated into geophysical models together with other scientific
data such as satellite observations and sensor measurements. As a use
case scenario, we analyze the Deepwater Horizon oil spill disaster. We
gather the social media data that mention sightings of oil from
Flickr, geolocate them, and use them as boundary forcings in the
General NOAA Oil Modeling Environment (GNOME) software for oil spill
predictions. We show how social media data can be incorporated into
the GNOME model to obtain improved estimates of the model parameters
such as rates of oil spill, couplings between surface winds and ocean
currents, diffusion coefficient, and other model parameters. Other
social media mining and citizen science projects performed by groups
outside of UMBC, on air quality, earthquakes and the Fukushima
disaster will also be summarized as related work.


Oleg Aulov received B.S. degree in mathematics from the University of
Central Missouri, Warrensburg, MO, in 2004 and M.S. degree in Computer
Science with a concentration in Computer Security and Information
Assurance from George Washington University, Washington, DC, in
2006. He is currently working toward a Ph.D. degree in the Department
of Computer Science and Electrical Engineering at University of
Maryland, Baltimore County, Baltimore, MD. His topics of interest
include social media mining, citizen science, machine learning, trust
establishment and management, information assurance, and social
engineering.

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