Select a calendar:
Filter August Events by Event Type:
SUNMONTUEWEDTHUFRISAT
Events for August 13, 2015
-
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
-
New Ph.D. Student Welcome
Thu, Aug 13, 2015 @ 08:30 AM - 11:00 AM
Viterbi School of Engineering Doctoral Programs
Receptions & Special Events
New Viterbi Ph.D. students are invited to attend the Ph.D. Welcome on Thursday, August 13, 2015. RSVP requested by August 10 via https://gapp.usc.edu/events/viterbi-phd-welcome
8:30-8:45 a.m.: Student, Faculty and Staff Check-In
9:00 a.m.: Welcome
9:45 a.m.: Faculty panel
11:30 a.m.: Program concludes
Questions about the Ph.D. Welcome may be directed to Jennifer Gerson, Director, Doctoral Programs, at jgerson@usc.edu.
Location: Epstein Family Engineering Plaza
Audiences: New Ph.D. Students
Contact: Jennifer Gerson
-
Accelerated Dynamic MRI using Sparse, Low-Rank, and Manifold Models
Thu, Aug 13, 2015 @ 10:30 AM - 11:30 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Prof. Mathews Jacob, University of Iowa
Talk Title: Accelerated Dynamic MRI using Sparse, Low-Rank, and Manifold Models
Series: Medical Imaging Seminar Series
Abstract: The acquisition of dynamically evolving objects plays a central role in several MRI applications. The slow nature of MR image acquisition often makes it challenging to acquire the datasets with high spatio-temporal resolution and coverage. In this talk, efficient low-rank and blind compressed sensing algorithms to recover the datasets from highly under sampled measurements will be introduced. These methods learn the representation from the data itself, thus offering improved image representations; algorithms that rely on these adaptive representations will translate to better reconstructions. Image and patch manifold algorithms, which enables implicit motion resolved and motion compensated reconstructions will also be introduced for free breathing dynamic MRI applications
Biography: Mathews Jacob is an associate professor at the Department of Electrical and Computer Engineering and is heading the Computational Biomedical Imaging Group (CBIG) at the University of Iowa. His research interests include image reconstruction, image analysis and quantification in the context of magnetic resonance imaging.
He obtained his B.Tech in Electronics and Communication Engineering from National Institute of Technology, Calicut, Kerala and M.E in signal processing from the Indian Institute of Science, Bangalore. He received his Ph.D degree from the Biomedical Imaging Group at the Swiss Federal Institute of Technology.
He was a Beckman postdoctoral fellow at the University of Illinois at Urbana Champaign. He is the recipient of the CAREER award from the National Science Foundation and the Research Scholar Award from American Cancer Society. He is currently the associate editor of the IEEE Transactions on Medical Imaging.
Host: Professor Krishna Nayak
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Talyia White
-
AI SEMINAR
Thu, Aug 13, 2015 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Paul Groth, Disruptive Tech Director, Elsevier Labs
Talk Title: Provenance for Data Munging Environments
Series: AI Seminar
Abstract: Data munging is a crucial task across domains ranging from drug discovery and policy studies to data science. Indeed, it has been reported that data munging accounts for 60% of the time spent in data analysis. Because data munging involves a wide variety of tasks using data from multiple sources, it often becomes difficult to understand how a cleaned dataset was actually produced (i.e. its provenance). In this talk, I discuss our recent work on tracking data provenance within desktop systems, which addresses problems of efficient and fine grained capture. I also describe our work on scalable provence tracking within a triple store/graph database that supports messy web data. Finally, I briefly touch on whether we will move from adhoc data munging approaches to more declarative knowledge representation languages such as Probabilistic Soft Logic.
Biography: Paul Groth (pgroth.com) is Disruptive Technology Director at Elsevier Labs. He holds a Ph.D. in Computer Science from the University of Southampton (2007) and has done research at the University of Southern California (ISI!) and the VU University Amsterdam. His research focuses on dealing with large amounts of diverse contextualized knowledge with a particular focus on the web and science applications. This includes research in data provenance, data science, data integration and knowledge sharing. Paul was co-chair of the W3C Provenance Working Group that created a standard for provenance interchange. He is co-author of Provenance: an Introduction to PROV and The Semantic Web Primer: 3rd Edition as well as numerous academic articles. He blogs at http://thinklinks.wordpress.com. You can find him on twitter: @pgroth .
Host: Ashish Vaswani
Webcast: http://webcasterms1.isi.edu/mediasite/Viewer/?peid=b46b31a4e04f4f83a6da32bf8dd040271dLocation: Information Science Institute (ISI) - 6th fl Large CR (689)
WebCast Link: http://webcasterms1.isi.edu/mediasite/Viewer/?peid=b46b31a4e04f4f83a6da32bf8dd040271d
Audiences: Everyone Is Invited
Contact: Alma Nava / Information Sciences Institute
-
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
