Data Science Talk: Learning, Mining and Graphs, Tina Eliassi-Rad, Monday Jan 12 2PM in E2 180

[Lise Getoor]

Please join us for a  Data Science Talk on "Learning, Mining and Graphs" by Tina Eliassi-Rad, Rutgers University next Monday 2PM in E2 180.   Light refreshments will be served beforehand. 

If you would like to meet with Tina, please let me know and fill out your availability here:
http://doodle.com/x8uppfkgxksteay4

Additional details below; here is the link:
https://www.soe.ucsc.edu/events/event/3731

cheers and happy new year!

==lise

Tina Eliassi-Rad, Professor, Rutgers University
Monday, January 12, 2:00 PM to 3:00 PM
Location: E-2 180, Simularium
Abstract: In this talk, I will discuss two problems on graph data.
(1) Measuring tie-strength: Given a set of people and a set of events
attended by them, how should we measure connectedness or tie strength between each
pair of persons? The underlying assumption is that attendance at mutual
events produces an implicit social network between people. I will describe
an axiomatic solution to this problem. (2) Network similarity: Given two
networks (without known node-correspondences), how should we measure
similarity between them? This problem occurs frequently in many real-world
applications such as transfer learning, re-identification, and change
detection. I will present a guide on how to select a network-similarity method.

Short bio: Tina Eliassi-Rad is an Associate Professor of Computer Science
at Rutgers University. Before joining academia, she was a Member of
Technical Staff and Principal Investigator at Lawrence Livermore National
Laboratory. Tina earned her Ph.D. in Computer Sciences (with a minor in
Mathematical Statistics) at the University of Wisconsin-Madison. Her
current research lays at the intersection of graph mining, network science,
and computational social science. Within data mining and machine learning,
Tina's research has been applied to the World-Wide Web, text corpora,
large-scale scientific simulation data, complex networks, fraud detection,
and cyber situational awareness. She has published over 70 peer-reviewed
papers (including a best paper runner-up award at ICDM'09 and a best
interdisciplinary paper award at CIKM'12); and has given over 100 invited
presentations. Tina is an action editor for the Data Mining and Knowledge
Discovery Journal and a member of the editorial board for the Springer
Encyclopedia of Machine Learning and Data Mining. In 2010, she received an
Outstanding Mentor Award from the US DOE Office of Science. For more
details, visit http://eliassi.org.