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[CCICADA-announce] DIMACS/CCICADA Interdisciplinary Seminar Series Presents

Linda Casals lindac at dimacs.rutgers.edu
Mon Nov 22 15:20:56 EST 2010


 
    Upcoming DIMACS/CCICADA Interdisciplinary Seminars
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November 29, 2010 - DIMACS/CCICADA Interdisciplinary Seminar Series Presents

Title: [Title removed for Anonymity]

Speaker: Graham Cormode, AT & T

Date: Monday, November 29, 2010 12:00 - 1:00 pm

Location: DIMACS Center, CoRE Bldg, Room 431, Rutgers University, 
          Busch Campus, Piscataway, NJ

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Abstract:

Differential privacy is a powerful privacy paradigm. The
differentially private output of a (randomized) algorithm guarantees
that the outputs on two "close" datasets (one including an individual
and another excluding an individual) are hard to distinguish. The
theory surrounding differential privacy has arisen in recent years,
but there are still challenges in applying it.

In this talk, I'll talk about two aspects of applying differential privacy:

(1) Applying privacy when the data is drawn from a high dimensional
    space and direct application of standard approaches produce vast
    amounts of noisy output. How can we make privacy scale to high
    dimensions?

(2) Understanding the limits of the definition: when does
    differentially private output still reveal information about
    individuals?

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(**This seminar was rescheduled from November 22, 2010**)

December 6, 2010 - DIMACS/CCICADA Interdisciplinary Seminar Series Presents
               
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Title: Interactive Model Learning from High-Dimensional Data: A Visual Analytics Approach

Speaker: Klaus Mueller, Stony Brook University

Date: Monday, December 6, 2010 12:00 - 1:00 pm

Location: DIMACS Center, CoRE Bldg, Room 431, Rutgers University                 
             Busch Campus, Piscataway, NJ
                          
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Abstract:

The growth of digital data is tremendous. Any aspect of life and
matter is being recorded and stored on cheap disks, either in the
cloud, in businesses, or in research labs. We can now afford to
explore very complex relationships with many variables playing a
part. But for this we need powerful tools that allow us to be
creative, to sculpt this intricate insight from the raw block of data
and finally create a formal model capturing this insight. This process
of learning models from raw data typically requires a substantial
amount of user input during the model initialization
phase. High-quality visual feedback can play a decisive role here. To
this end, I will present an assistive visualization system which
greatly reduces the load on the users and makes the process of model
initialization and refinement more interactive and efficient. In
addition, I will also discuss various platforms we have developed over
the years to make the exploration of multivariate (high-dimensional)
data more intuitive and direct. Here I will discuss our recent work on
illustrative parallel coordinates, space embedding, and multivariate
scatterplots.




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