Call for Papers: JCDL 2019 Workshop on Organizing Data, Information, and
Knowledge in Big Data Environments
Deadline: Extensive summary submission due April 5, 2019
This workshop will provide an opportunity for participants – information and computing researchers and professionals who work in the areas of information organization, information retrieval, knowledge management, data mining, and Big Data analytics — to get together and exchange research ideas on organizing access to data, information, and knowledge in Big Data environments. Continue reading
Scholarship Applications Open 2019 Summer Institute in Statistical Genetics
Deadline: Applications due Sunday, March 31, 2019
Scholarship applications are now being accepted for the 2019 UW Biostatistics Summer Institute in Statistical Genetics (see below). Also, general registration is open for the Summer Institutes in Big Data, Clinical & Epidemiological Research, and Modeling in Infectious Diseases. Continue reading
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.)
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.
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.