[Colloquium] Talks Next Week @ TTI-C: Anders Logg 4/26, Nina Mishra 4/29

[Katherine Cumming]

Show and Tell Series (1)
Speaker:  Anders Logg, TTI-C
Speaker's Homepage:  http://www.tti-c.org//logg.html
 
Time:  Tuesday, April 26th @ 12:15pm-Lunch Provided
Place:  TTI-C Conference Room
Title:  Benchmark results for the FEniCS form compiler
Abstract:
I will discuss the current status of the FEniCS form compiler FFC and
present benchmark results for a selection of standard variational forms,
including Navier-Stokes and linear elasticity.  The new form compiler
significantly reduces the complexity of form evaluation compared to the
standard quadrature-based approach, resulting in quite impressive
speedups for many standard problems, typically a factor ranging between
10-1000.

This removes one major bottle-neck in finite element computation, where
the evaluation of variational forms usually accounts for a significant
portion of the total run-time. As a consequence, new bottle-necks are
revealed, giving directions for future research.

The talk will be hands-on and hopefully accessible to a large audience;
we will actually look at the C-code generated by the compiler and count
the flops. 
 
Guest Speaker (2)
 
Speaker:  Nina Mishra, Stanford University
Speaker's Homepage: http://theory.stanford.edu/~nmishra/
 
Time:  Friday, April 29th @ 3:00pm-Refreshments Provided
Place:  TTI-C Conference Room
Title:  Privacy-Preserving Auditing Algorithms
Abstract:
Organizations now maintain large quantities of personal information.
Consequently, there is a growing need to find ways to keep this
confidential information private. The need for privacy directly competes
with the need to use this data for the discovery of patterns. For
example, in a dataset containing the HIV status of patients, we would
like to keep private the HIV status of any particular patient but allow
the discovery of the total fraction of patients that are HIV+. We
describe auditing algorithms that monitor an online stream of queries
posed to a dataset and either deny the answer to a query if it breaches
privacy or give the true answer if it does not. We uncover a fundamental
problem that existing offline auditing algorithms cannot be used to
solve the online problem as denials leak information. We then propose a
new model of auditing, called simulatable auditing, where denials
provably do not leak information. Finally we provide new simulatable
auditing algorithms. 

Joint work with Krishnaram Kenthapadi and Kobbi Nissim. 

Bio: Nina Mishra currently holds a joint appointment as a Senior
Research Scientist at HP Labs and as an Acting Faculty member at
Stanford University. Her research interests are in the design and
analysis of data mining, machine learning and privacy-preserving
algorithms. She served as Program Chair for the ICML'03 conference
(International Conference on Machine Learning) and has served on
numerous data mining and machine learning program committees. She also
serves on the Editorial Board of the Machine Learning journal. She
earned a PhD in Computer Science from the University of Illinois at
Urbana-Champaign. 
 
If you have questions, or would like to meet the speaker, please contact
Katherine at 773-834-1994 or kcumming at tti-c.org. For information on
future TTI-C talks or events, please go to the TTI-C Events
<http://ttic.uchicago.edu/events/events_dyn.php>  page. 
 
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