[Research] [Fwd: Thesis Defense - Kaustav Das - 3/16/09]

[Artur Dubrawski]

Dear Autonians,

This week we will not have a lab meeting.

Instead, if you can please attend Kaustav's defense on Monday.

Artur


Artur Dubrawski wrote:
>
>
> ------------------------------------------------------------------------
>
> Subject:
> Thesis Defense - Kaustav Das - 3/16/09
> From:
> Diane Stidle <diane+ at cs.cmu.edu>
> Date:
> Tue, 03 Mar 2009 11:31:03 -0500
> To:
> ml-seminar at cs.cmu.edu
>
> To:
> ml-seminar at cs.cmu.edu
>
>
> Thesis Defense
>
> Date: 3/16/09
> Time: 2:00pm
> Place: 4625 Wean Hall
>
> PhD Candidate: Kaustav Das
>
> Title: *Detecting Patterns of Anomalies*
>
> Abstract:
>
> An anomaly is an observation that does not conform to the expected 
> "normal"
> behavior. With the ever increasing amount of data being collected
> universally, automatic surveillance systems are becoming more popular and
> are increasingly using data mining methods to detect patterns anomalies.
> Detecting anomalies can provide useful and actionable information in a
> variety of real-world scenarios. For example, in disease monitoring, a
> timely detection of an epidemic can potentially save many lives.
>
> The diverse nature of real-world datasets, and the difficulty of 
> obtaining
> labeled training data make it challenging to develop a universal 
> framework
> for anomaly detection. We focus on a key feature of most real world
> scenarios, that multiple anomalous records are usually generated by a 
> common
> anomalous process. In this thesis we develop methods that utilize the
> self-similarity of these "groups" or "patterns" of anomalies to perform
> better detection. We also investigate new methods for detection of
> individual record anomalies, which we then incorporate into the group
> detection methods.
>
> A recurring feature of our methods is combinatorial search over some 
> space
> (e.g. over all subsets of attributes, or over all subsets of records). We
> use a variety of computational speedup tricks and approximation 
> techniques
> to make these methods scalable to large datasets. Since, most of our
> motivating problems involve datasets having categorical or symbolic 
> values,
> we focus on categorical valued datasets. Apart from this, we make few
> assumptions about the data, and our methods are very general and 
> applicable
> to a wide variety of domains.
>
> Additionally, we investigate anomaly pattern detection in data 
> structured by
> space and time. Our method generalizes the popular method of 
> spatio-temporal
> scan statistics to learn and detect specific, time-varying spatial 
> patterns
> in the data. Finally, we show an efficient and easily interpretable
> technique for anomaly detection in multivariate time series data. We
> evaluate our methods on a variety of real world data sets including both
> real and synthetic anomalies.
>
> Thesis Committee:
> Jeff Schneider (Chair)
> Greg Cooper (Pitt)
> Christos Faloutsos
> Geoff Gordon
> Daniell Neill
>
> http://www.cs.cmu.edu/~kaustav/thesis/kaustav_thesis.pdf
>
> ------------------------------------------------------------------------
>
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