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University Calendar
Events for August
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Computer Science MS Group Advisement Session
Tue, Aug 04, 2015 @ 01:30 PM - 03:20 PM
Thomas Lord Department of Computer Science
University Calendar
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Graduate
Contact: Lizsl De Leon
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Computer Science MS Group Advisement Session
Wed, Aug 05, 2015 @ 01:30 PM - 03:20 PM
Thomas Lord Department of Computer Science
University Calendar
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Graduate
Contact: Lizsl De Leon
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Computer Science MS Group Advisement Session
Thu, Aug 06, 2015 @ 01:30 PM - 03:20 PM
Thomas Lord Department of Computer Science
University Calendar
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Graduate
Contact: Lizsl De Leon
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Computer Science MS Group Advisement Session
Tue, Aug 11, 2015 @ 01:30 PM - 03:20 PM
Thomas Lord Department of Computer Science
University Calendar
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Graduate
Contact: Lizsl De Leon
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Computer Science MS Group Advisement Session
Wed, Aug 12, 2015 @ 01:30 PM - 03:20 PM
Thomas Lord Department of Computer Science
University Calendar
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Graduate
Contact: Lizsl De Leon
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PhD Defense - Huy Pham
Thu, Aug 13, 2015 @ 01:00 AM - 03:00 PM
Thomas Lord Department of Computer Science
University Calendar
Dissertation Title
Deriving Real-World Social Strength and Social Influence from Spatiotemporal Data
PhD Candidate
Huy Pham
Committee
Cyrus Shahabi (chair), Yan Liu, Daniel O'Leary (outside member)
Time and Place
Thursday, 13 Aug, 1:00pm
PHE 333 Conference Room
Abstract
The ubiquity of mobile devices and the popularity of location-based services have generated rich datasets of people's location information at a very high fidelity. These location datasets can be used for studying various social behaviors, including social connections and social influence. For example, social studies have shown that people, who are seen together frequently at the same places and the same time, are most probably socially related. Similarly, the fact that a person visits a location by following the recommendation of another person who has visited that same location in the past indicates influence that a person exerts on another.
Correspondingly, this thesis focuses on inferring the real-world social connections and social influence by analyzing people's location information, which are useful in a variety of application domains from sales and marketing to social/cultural studies and intelligence analysis. In particular, in the first two studies of this thesis we propose models (GEOSO and EBM) to not only infer social connections, but also to estimate their strengths quantitatively (aka social strength) by analyzing people's co-occurrences in space and time. In the third study, we first define followship to capture the phenomenon of an individual visiting a real-world location (e.g., restaurant) due the influence of another individual who has visited that same location in the past. Subsequently, we coin the term spatial influence as the concept of inferring pair-wise influence from spatiotemporal data by quantifying the amount of followship influence that an individual has on others, and devise the TLFM model for quantifying followship. In all these studies, we examine the impacts of different factors in the location behaviors of people on social strength and influence, including time, locations and coincidences. We conducted extensive experiments with real-world datasets, which demonstrate the effectiveness of the proposed models in quantifying social strength and influence, and their efficiency in working with large data.
Location: Charles Lee Powell Hall (PHE) - 333
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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Computer Science MS Group Advisement Session
Thu, Aug 13, 2015 @ 01:30 PM - 03:20 PM
Thomas Lord Department of Computer Science
University Calendar
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Graduate
Contact: Lizsl De Leon
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Computer Science MS Group Advisement Session
Mon, Aug 17, 2015 @ 01:30 PM - 03:20 PM
Thomas Lord Department of Computer Science
University Calendar
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Graduate
Contact: Lizsl De Leon
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Computer Science MS Group Advisement Session
Wed, Aug 19, 2015 @ 01:30 PM - 03:20 PM
Thomas Lord Department of Computer Science
University Calendar
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Graduate
Contact: Lizsl De Leon
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Computer Science MS Group Advisement Session
Thu, Aug 20, 2015 @ 01:30 PM - 03:20 PM
Thomas Lord Department of Computer Science
University Calendar
Location: Mark Taper Hall Of Humanities (THH) - 301
Audiences: Graduate
Contact: Lizsl De Leon
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PhD Defense - Kaveh Shahabi
Mon, Aug 31, 2015 @ 01:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
University Calendar
Defense: Kaveh Shahabi
Committee: Dr. John Wilson (chair), Dr. Aiichiro Nakano, and Dr. James Moore (outside)
Space / Time: SAL 213 / Aug 31st, 1-3pm
Title: Scalable Evacuation Routing in Dynamic Environments
Abstract:
In the face of a natural or man-made disaster, evacuation planning refers to the process of reallocating the endangered population to a set of safe places, often in a hurry. Such a task needs proper preparation, execution, and most definitely a post-disaster response. We have contributed a new taxonomy of the evacuation planning problem and categorized available solutions. Evacuation routing is part of the bigger problem that finds the best routes to relocate the endangered population to safety. We propose a new method to perform evacuation routing efficiently under capacity constraints. Traditionally, simulation software or shortest path routing combined with zonal scheduling have been used to solve routing problems. Our method utilizes a state-of-the-art algorithm to connect each source node to its nearest destination. It also intelligently takes into account transportation network capacity and traffic flow to minimize congestion and system-wide transportation times. We also define the dynamic evacuation routing problem and propose a solution. The dynamic solution is capable of updating routes if the network topology is changed during the evacuation process. Effectively, it can solve the evacuation problem for a moving disaster. Together these new developments pave the path for future researchers to study the evacuation problem and to integrate it into urban transportation services. Hopefully one day we can save more lives than before when future disasters occur.
Location: Henry Salvatori Computer Science Center (SAL) - 213
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
