From awd at cs.cmu.edu  Mon Jul  6 10:22:21 2015
From: awd at cs.cmu.edu (Artur Dubrawski)
Date: Mon, 06 Jul 2015 10:22:21 -0400
Subject: Fwd: Thesis Oral: Prateek Tandon
In-Reply-To: <559A8DAD.80705@cmu.edu>
References: <559A8DAD.80705@cmu.edu>
Message-ID: <559A8F1D.403@cs.cmu.edu>

Dear Autonians,

Please come and cheer Prateek on his big day next Monday!

Thanks
Artur


-------- Forwarded Message --------
Subject: 	Thesis Oral: Prateek Tandon
Date: 	Mon, 06 Jul 2015 10:16:13 -0400
From: 	Suzanne Lyons Muth <lyonsmuth at cmu.edu>
To: 	ri-people at cs.cmu.edu



Date: 13 July 2015
Time: 4:00 p.m.
Place: NSH 1305
Type: Thesis Oral
Who:  Prateek Tandon
Topic: Bayesian Aggregation of Evidence for Detection and Characterization
          of Patterns in Multiple Noisy Observations
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From awd at cs.cmu.edu  Mon Jul 13 15:18:46 2015
From: awd at cs.cmu.edu (Artur Dubrawski)
Date: Mon, 13 Jul 2015 15:18:46 -0400
Subject: It will start in 40 minutes [Thesis Oral: Prateek Tandon]
In-Reply-To: <559A8DAD.80705@cmu.edu>
References: <559A8DAD.80705@cmu.edu>
Message-ID: <55A40F16.6010106@cs.cmu.edu>

Date: 13 July 2015
Time: 4:00 p.m.
Place: NSH 1305
Type: Thesis Oral
Who:  Prateek Tandon
Topic: Bayesian Aggregation of Evidence for Detection and Characterization
          of Patterns in Multiple Noisy Observations

Abstract:
Effective use of Machine Learning to support extracting maximal 
information from limited sensor data is one of the important research 
challenges in robotic sensing. This thesis develops techniques for 
detecting and characterizing patterns in noisy sensor data. Our Bayesian 
Aggregation (BA) algorithmic framework can leverage data fusion from 
multiple low Signal-To-Noise Ratio (SNR) sensor observations to boost 
the capability to detect and characterize the properties of a signal 
generating source or process of interest.

We illustrate our research with application to the nuclear threat 
detection domain. Developed algorithms are applied to the problem of 
processing the large amounts of spectrometry data that can be produced 
in real-time by mobile radiation sensors. The thesis experimentally 
shows BA's capability to boost sensor performance in detecting radiation 
sources of interest, even if the source is faint, partially-occluded, or 
enveloped in the noisy and variable radiation background characteristic 
of urban scenes.

In addition, BA provides simultaneous inference of source parameters 
such as the source intensity or source type while detecting it. The 
thesis demonstrates this capability as well as develops techniques to 
efficiently optimize these parameters over large possible setting 
spaces. Methods developed in this thesis are demonstrated both in 
simulation and in a radiation-sensing backpack that applies robotic 
localization techniques to enable indoor surveillance of radiation sources.

The thesis further improves the BA algorithm's capability to be robust 
under various detection scenarios. First, we augment BA with appropriate 
statistical models to improve estimation of signal components in low 
photon count detection, where the sensor may receive limited photon 
counts from either source and/or background. Second, we develop methods 
for online sensor reliability monitoring to create algorithms that are 
resilient to possible sensor faults in a data pipeline containing one or 
multiple sensors.

Finally, we develop Retrospective BA, a variant of BA that allows 
reinterpretation of past sensor data in light of new information about 
percepts. These Retrospective capabilities include the use of Hidden 
Markov Models in BA to allow automatic correction of a sensor pipeline 
when sensor malfunction may be occur, an Anomaly-Match search strategy 
to efficiently optimize source hypotheses, and prototyping of a 
Multi-Modal Augmented PCA to more flexibly model background and nuisance 
source fluctuations in a dynamic environment.


Thesis Committee Members:
Artur Dubrawski, Chair
Manuela Veloso
Paul Scerri
Simon Labov, Lawrence Livermore National Laboratory

A copy of the thesis document is available at:
https://goo.gl/zAEoXj




From awd at cs.cmu.edu  Wed Jul 22 16:42:43 2015
From: awd at cs.cmu.edu (Artur Dubrawski)
Date: Wed, 22 Jul 2015 16:42:43 -0400
Subject: Maria wins the best presentation award
Message-ID: <55B00043.4070603@cs.cmu.edu>

Team,

I am pleased to report that our own Maria De Arteaga has won the best 
student presentation award at the
Annual Academic Research Initiative Conference organized by the Domestic 
Nuclear Detection Office,
US Department of Homeland Security. The event took place earlier this 
month in Dallas, Texas.
Maria's paper was titled "Leveraging Multidimensional Autocorrelations 
to Boost Sensitivity of Spectral Anomaly Detection."

Congrats Maria!

Artur
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From bapoczos at cs.cmu.edu  Wed Jul 22 17:32:28 2015
From: bapoczos at cs.cmu.edu (Barnabas Poczos)
Date: Wed, 22 Jul 2015 17:32:28 -0400
Subject: Maria wins the best presentation award
In-Reply-To: <55B00043.4070603@cs.cmu.edu>
References: <55B00043.4070603@cs.cmu.edu>
Message-ID: <CAHFiJZt+Rd=Dc4ZSqnBHu5kDWSkdAqNpns39=GVcb8e9BA4BCw@mail.gmail.com>

This is awesome! Congrats Maria! :-)

Best,
Barnabas
======================
Barnabas Poczos, PhD
Assistant Professor
Machine Learning Department
Carnegie Mellon University


On Wed, Jul 22, 2015 at 4:42 PM, Artur Dubrawski <awd at cs.cmu.edu> wrote:
> Team,
>
> I am pleased to report that our own Maria De Arteaga has won the best
> student presentation award at the
> Annual Academic Research Initiative Conference organized by the Domestic
> Nuclear Detection Office,
> US Department of Homeland Security. The event took place earlier this month
> in Dallas, Texas.
> Maria's paper was titled "Leveraging Multidimensional Autocorrelations to
> Boost Sensitivity of Spectral Anomaly Detection."
>
> Congrats Maria!
>
> Artur

From rk2x at cmu.edu  Wed Jul 22 17:39:36 2015
From: rk2x at cmu.edu (Ramayya Krishnan)
Date: Wed, 22 Jul 2015 21:39:36 +0000
Subject: Maria wins the best presentation award
In-Reply-To: <55B00043.4070603@cs.cmu.edu>
References: <55B00043.4070603@cs.cmu.edu>
Message-ID: <499434d2-3464-47c4-a4df-c04f3858168c@PGH-MSGHT-02.andrew.ad.cmu.edu>

Congratulations Maria!

This is excellent.

Krishnan

From: Artur Dubrawski [mailto:awd at cs.cmu.edu]
Sent: Wednesday, July 22, 2015 4:43 PM
To: research at autonlab.org
Cc: Rahul Telang; Daniel B. Neill; Geoff Gordon; Ramayya Krishnan; Tom Mitchell
Subject: Maria wins the best presentation award

Team,

I am pleased to report that our own Maria De Arteaga has won the best student presentation award at the
Annual Academic Research Initiative Conference organized by the Domestic Nuclear Detection Office,
US Department of Homeland Security. The event took place earlier this month in Dallas, Texas.
Maria's paper was titled "Leveraging Multidimensional Autocorrelations to Boost Sensitivity of Spectral Anomaly Detection."

Congrats Maria!

Artur
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From awm at cs.cmu.edu  Wed Jul 22 18:11:56 2015
From: awm at cs.cmu.edu (Andrew Moore)
Date: Wed, 22 Jul 2015 18:11:56 -0400
Subject: Maria wins the best presentation award
In-Reply-To: <499434d2-3464-47c4-a4df-c04f3858168c@PGH-MSGHT-02.andrew.ad.cmu.edu>
References: <55B00043.4070603@cs.cmu.edu>
 <499434d2-3464-47c4-a4df-c04f3858168c@PGH-MSGHT-02.andrew.ad.cmu.edu>
Message-ID: <CAPQV5HTQxWGTB8PCpQzQUiyQPeXLUw_SjENhCxzTUcP3Se06yQ@mail.gmail.com>

Wonderful!


On Wed, Jul 22, 2015 at 5:39 PM, Ramayya Krishnan <rk2x at cmu.edu> wrote:

>  Congratulations Maria!
>
>
>
> This is excellent.
>
>
>
> Krishnan
>
>
>
> *From:* Artur Dubrawski [mailto:awd at cs.cmu.edu]
> *Sent:* Wednesday, July 22, 2015 4:43 PM
> *To:* research at autonlab.org
> *Cc:* Rahul Telang; Daniel B. Neill; Geoff Gordon; Ramayya Krishnan; Tom
> Mitchell
> *Subject:* Maria wins the best presentation award
>
>
>
> Team,
>
> I am pleased to report that our own Maria De Arteaga has won the best
> student presentation award at the
> Annual Academic Research Initiative Conference organized by the Domestic
> Nuclear Detection Office,
> US Department of Homeland Security. The event took place earlier this
> month in Dallas, Texas.
> Maria's paper was titled "Leveraging Multidimensional Autocorrelations to
> Boost Sensitivity of Spectral Anomaly Detection."
>
> Congrats Maria!
>
> Artur
>
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