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[nl-uiuc] Reminder: AIIS talk by Jing Gao at 2 pm


Chronological Thread 
  • From: Rajhans Samdani <rsamdan2 AT illinois.edu>
  • To: dais AT cs.uiuc.edu, nl-uiuc AT cs.uiuc.edu, aivr AT cs.uiuc.edu, cogcomp AT cs.uiuc.edu, vision AT cs.uiuc.edu, eyal AT cs.uiuc.edu, aiis AT cs.uiuc.edu, aistudents AT cs.uiuc.edu, "Girju, Corina R" <girju AT illinois.edu>, "Catherine Blake" <clblake AT illinois.edu>, "Efron, Miles James" <mefron AT illinois.edu>
  • Subject: [nl-uiuc] Reminder: AIIS talk by Jing Gao at 2 pm
  • Date: Fri, 11 Feb 2011 12:00:14 -0600 (CST)
  • List-archive: <http://lists.cs.uiuc.edu/pipermail/nl-uiuc>
  • List-id: Natural language research announcements <nl-uiuc.cs.uiuc.edu>

This week we have Jing Gao from the DAIS group as our speaker at AIIS. She is
a
student of Prof. Jiawei Han. Details:

When: Friday, Feb 11, 2 pm.

Where: 3405, Siebel Center.

Title: Exploring the Power of Heterogeneous Information Sources

Abstract:
Nowadays, a vast ocean of data is collected from trillions of connected
devices
everyday. Useful knowledge is usually buried in multiple genres of data,
which are
from different sources, in different formats, and with different types of
representation.
Many interesting patterns cannot be extracted from a single data collection,
but have
to be discovered from the integrative analysis of all heterogeneous data
sources
available. Although many algorithms have been developed to analyze multiple
information sources, real applications continuously pose new challenges: Data
can be
gigantic, noisy, unreliable, dynamically evolving, highly imbalanced, and
heterogeneous. Meanwhile, users provide limited feedback, have growing
privacy
concerns, and ask for actionable knowledge. In this talk, I will discuss my
thesis work
on exploring the power of multiple heterogeneous information sources in
challenging
learning scenarios. I will present two perspectives of learning from multiple
sources,
i.e., exploring their similarities (knowledge integration) or their
differences
(inconsistency detection). First, for knowledge integration, I proposed a
graph based
consensus maximization framework to combine multiple supervised and
unsupervised
models, which greatly improves classification accuracy. Second, I developed
approaches based on probabilistic models and spectral embedding techniques to
detect objects performing inconsistently across multiple sources as a new
type of
outliers. I will show the effectiveness of these general learning techniques
with a few
sample applications in social networks, Internet, multimedia, and
cyber-security.

See you!
Best,
Rajhans


Rajhans Samdani,
Graduate Student,
Dept. of Computer Science,
University of Illinois at Urbana-Champaign.



  • [nl-uiuc] Reminder: AIIS talk by Jing Gao at 2 pm, Rajhans Samdani, 02/11/2011

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