From awd at cs.cmu.edu  Mon Mar  2 11:54:28 2009
From: awd at cs.cmu.edu (Artur Dubrawski)
Date: Mon, 02 Mar 2009 11:54:28 -0500
Subject: [Research] Upcoming Lab meetings: March 3rd and March 10th
Message-ID: <49AC0F44.5050305@cs.cmu.edu>

March 3rd, 12 noon, NSH 1507:

Speaker: Tzu-Kuo Huang
Auton Lab

Title: Learning Linear Dynamical Systems without Sequence Information

Abstract:
Virtually all methods of learning dynamic systems from data start from the
same basic assumption: that the learning algorithm will be provided with a
sequence, or trajectory, of data generated from the dynamic system.  In
this paper we consider the case where the data is not sequenced.  The
learning algorithm is presented a set of data points from the system's
operation but with no temporal ordering.  The data are simply drawn as
individual disconnected points.
While making this assumption may seem absurd at first glance, we observe
that many scientific modeling tasks have exactly this property.  In this
paper we restrict our attention to learning linear, discrete time models.
We propose several algorithms for learning these models based on optimizing
approximate likelihood functions and test the methods on several synthetic
data sets.

----

March 10th, 12 noon, NSH 1507:

Speaker: Marek Druzdzel
Associate Professor, Decision Systems Laboratory, School of Information Sciences, U. of Pittsburgh

Title: Cool Things That You Can Do With Graphical Probabilistic Models

Abstract:
In this introductory-level talk, I will give an overview of some of the 
research done in the last decade at the Decision Systems Laboratory, 
focusing on directed probabilistic graphs, such as Bayesian networks.  I 
will review basic knowledge engineering and inference techniques for 
Bayesian networks, influence diagrams, dynamic Bayesian networks, 
Bayesian networks involving equations and continuous distributions, 
along with applications in diagnosis, prognosis, data analysis, 
learning, and strategic planning.  I will show some user interface 
tricks for making the results of computation digestible for users.  As 
most of these topics are best explained "real-time", I will rely heavily 
on GeNIe, a graphical modeling environment developed at the Decision 
Systems Laboratory, and available at http://genie.sis.pitt.edu/.




From awd at cs.cmu.edu  Tue Mar  3 11:33:47 2009
From: awd at cs.cmu.edu (Artur Dubrawski)
Date: Tue, 03 Mar 2009 11:33:47 -0500
Subject: [Research] [Fwd: Thesis Defense - Kaustav Das - 3/16/09]
Message-ID: <49AD5BEB.2080102@cs.cmu.edu>


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From awd at cs.cmu.edu  Fri Mar  6 10:22:24 2009
From: awd at cs.cmu.edu (Artur Dubrawski)
Date: Fri, 06 Mar 2009 10:22:24 -0500
Subject: [Research] [Fwd: Academic post in learning based vision (Edinburgh)]
Message-ID: <49B13FB0.3010001@cs.cmu.edu>


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From awd at cs.cmu.edu  Tue Mar 10 08:39:50 2009
From: awd at cs.cmu.edu (Artur Dubrawski)
Date: Tue, 10 Mar 2009 08:39:50 -0400
Subject: [Research] Auton Lab meeting today
Message-ID: <49B65F96.4010106@cs.cmu.edu>

March 10th, 12 noon, NSH 1507:

Speaker: Marek Druzdzel
Associate Professor, Decision Systems Laboratory, School of Information 
Sciences, U. of Pittsburgh

Title: Cool Things That You Can Do With Graphical Probabilistic Models

Abstract:
In this introductory-level talk, I will give an overview of some of the
research done in the last decade at the Decision Systems Laboratory,
focusing on directed probabilistic graphs, such as Bayesian networks.  I
will review basic knowledge engineering and inference techniques for
Bayesian networks, influence diagrams, dynamic Bayesian networks,
Bayesian networks involving equations and continuous distributions,
along with applications in diagnosis, prognosis, data analysis,
learning, and strategic planning.  I will show some user interface
tricks for making the results of computation digestible for users.  As
most of these topics are best explained "real-time", I will rely heavily
on GeNIe, a graphical modeling environment developed at the Decision
Systems Laboratory, and available at http://genie.sis.pitt.edu/.


From awd at cs.cmu.edu  Sun Mar 15 12:44:26 2009
From: awd at cs.cmu.edu (Artur Dubrawski)
Date: Sun, 15 Mar 2009 12:44:26 -0400
Subject: [Research] [Fwd: Thesis Defense - Kaustav Das - 3/16/09]
In-Reply-To: <49AD5BEB.2080102@cs.cmu.edu>
References: <49AD5BEB.2080102@cs.cmu.edu>
Message-ID: <49BD306A.8070209@cs.cmu.edu>

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
>
> ------------------------------------------------------------------------
>
> _______________________________________________
> Research mailing list
> Research at autonlab.org
> https://www.autonlab.org/mailman/listinfo/research
>   



From schneide at cs.cmu.edu  Mon Mar 16 11:21:34 2009
From: schneide at cs.cmu.edu (Jeff Schneider)
Date: Mon, 16 Mar 2009 11:21:34 -0400
Subject: [Research] dinner to celebrate Kaustav's defense
Message-ID: <49BE6E7E.4080200@cs.cmu.edu>


Hi Everyone,

I guess its a bit premature to make plans before finding out the results of 
the defense, but I would like to invite everyone (my treat) to Bangkok 
Balcony (5846 Forbes) at 7pm tonight to celebrate Kaustav's defense. 
Please let me know if you can make it so I can call ahead and make sure 
they have a table for us.

Jeff.




From awd at cs.cmu.edu  Tue Mar 24 13:31:14 2009
From: awd at cs.cmu.edu (Artur Dubrawski)
Date: Tue, 24 Mar 2009 13:31:14 -0400
Subject: [Research] looking for an OLAP specialist
Message-ID: <49C918E2.7010603@cs.cmu.edu>

Hello,
Please let me and Robin know if you know someone who would be capable
of setting up what we believe must be a straightforward performance 
measurement
experiment in Oracle and/or Microsoft OLAP environments.
Thanks
Artur