Funding: 2019 LEADS Doctoral Summer Fellowship, DEADLINE 2/17/2019

Funding: 2019 LEADS Doctoral Summer Fellowship

Application Deadline Sunday, February 17, 2019 at 11:59 PM (EST).

The Metadata Research Center (MRC) at Drexel University’s College of Computing and Informatics (CCI) invites doctoral students to participate in the LIS Education and Data Science-4-the National Digital Platform (LEADS-4-NDP) program. This is a virtual fellowship program; applicants from any geographic location are eligible for consideration. The deadline for applications is Sunday, February 17 at 11:59 PM (EST). Information on last year’s (2018) LEADS Fellows can be found at: http://cci.drexel.edu/mrc/research/leads/leads-4-ndp-fellows/.

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Event: Movie in Wallingford on Big Data, 12/19

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http://www.meaningfulmovies.org/

Friday, Dec 19, 2015, 7:00 PM
Film: “THE HUMAN FACE OF BIG DATA”
(56 min, Sandy Smolan, 2014)

With the rapid emergence of online devices from cell phones to tablets to PCs, an unstoppable, invisible force is changing human lives in ways from the microscopic to the gargantuan: Big Data, a word that was barely used a few years ago but now governs the day for many of us. This massive gathering and analyzing of data in real time is allowing us to not only address some of humanity’s biggest challenges but is also helping create a new kind of planetary nervous system. The Human Face of Big Data captures the promise and peril of this extraordinary knowledge revolution. Join us following the film for a great discussion. (Event Is Open to the Public. Admission is by Donation.)

Event: UW Data Science Seminar, October 8

JureLeskovec

UW Data Science Seminar – Jure Leskovec – October 8th, 3:30pm – MGH 389

UW Data Science Seminar: Analysis, Visualization & Discovery
Wednesday October 8, 3:30pm
389 Mary Gates Hall
Can Cascades be Predicted?
Jure Leskovec, Assistant Professor of Computer Science, Stanford University

Social networks play a central role in spreading of information, ideas, behaviors, and products. As such “contagions” diffuse from a person to person they may go “viral,” and large cascades can form. However, a growing body of research has argued that virality and cascades may be inherently unpredictable. Thus, one of the central questions is whether information cascades can be predicted and possibly even engineered. In this talk, I will discuss a framework for predicting cascades and making them go viral.

We study large sample of cascades on Facebook and find strong performance in predicting whether a cascade will continue to grow in the future. The models we develop help us understand how to create viral social media content: by using the right title, for the right community, at the right time.

BIO
Jure Leskovec is assistant professor of Computer Science at Stanford University. His research focuses on mining large social and information networks. Problems he investigates are motivated by large scale data, the Web and on-line media. This research has won several awards including a Microsoft Research Faculty Fellowship, the Alfred P. Sloan Fellowship and numerous best paper awards. Leskovec received his bachelor’s degree in computer science from University of Ljubljana, Slovenia, and his PhD in in machine learning from the Carnegie Mellon University and postdoctoral training at Cornell University. You can follow him on Twitter @jure.

Event: Data Science Seminar: Yong-Yeol Ahn (IU, Bloomington) 3-19-14 @ 3:30pm, MGH 420

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Data Science Seminar: Yong-Yeol Ahn (Indiana University Bloomington) 3:30 PM, Wednesday, March 19, 2014 MGH 420

Please join us for the following talk:

Can We Predict Viral Memes?
Yong-Yeol Ahn, Indiana University Bloomington
Wednesday, March 19, 2014, 3:30-4:30pm
Mary Gates Hall, Room 420
University of Washington, Seattle campus

Some videos, pictures, and memes spread through billions of people while others quickly die out. Is it because of the innate quality of the memes or because of the celebrities like Justin Bieber? Or, is it just random? In this study we focus on the early diffusion pattern of memes in terms of underlying social network structure and demonstrate that the future success of a meme can be predicted by quantifying its early spreading pattern, particularly using the community structures in the network.

Bio: Yong-Yeol Ahn is an assistant professor of Informatics and Computing at Indiana University, Bloomington. He received his Ph.D. in Statistical Physics from KAIST, South Korea in 2008 and worked as a postdoctoral research associate at Northeastern University and a visiting researcher at the Dana-Farber Cancer Institute before moving to Indiana University. His research focuses on the structure of networks in various complex systems such as society, culture, and living organisms.

For more information on this and other talks please visit: http://data.uw.edu/seminar