SOCR Events JMM BigData Jan2014

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== Organizer==
== Organizer==
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* [http://www.stat.ucla.edu/~dinov/ Ivo Dinov], [http://www.ucla.edu UCLA] [http://www.stat.ucla.edu Statistics].
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* [http://www.socr.umich.edu/people/dinov/ Ivo Dinov], [http://www.umich.edu UMich]/[http://www.socr.umich.edu/ UMSN/SOCR], and [http://www.ucla.edu UCLA]/[http://www.stat.ucla.edu Statistics].
==Session Logistics==
==Session Logistics==
* '''Date/Time''': Thursday January 16, 2014, 1:00-3:50 PM
* '''Date/Time''': Thursday January 16, 2014, 1:00-3:50 PM
* '''Venue''': [http://www.bccenter.org Baltimore Convention Center], 1 W Pratt St  Baltimore, MD 21201, (410) 649-7000
* '''Venue''': [http://www.bccenter.org Baltimore Convention Center], 1 W Pratt St  Baltimore, MD 21201, (410) 649-7000
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* '''Room''': TBD
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* '''Room''': Room 317, [http://www.bccenter.org Baltimore Convention Center]
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* '''Conference''': [http://jointmathematicsmeetings.org/meetings/national/jmm2014/2160_intro 2014 JMM Meeting]
* '''Equipment''': Computer projector and screen. No computer/laptop, blackboard or whiteboard in session room
* '''Equipment''': Computer projector and screen. No computer/laptop, blackboard or whiteboard in session room
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* [http://jointmathematicsmeetings.org/meetings/abstracts/abstract.pl?type=jmm '''Paper Abstract Submission'''] - choose session '''SS 18A''' (due date: Sept 01, 2013)
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* [http://jointmathematicsmeetings.org/meetings/abstracts/abstract.pl?type=jmm '''Paper Abstract Submission'''] - choose session [http://jointmathematicsmeetings.org/meetings/national/jmm2014/2160_program_ss18.html SS 18A] (due date: Sept 01, 2013)
==Speakers==
==Speakers==
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* TBD
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<center>
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{| class="wikitable" style="text-align:center; width:75%" border="1"
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|+
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|-
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| '''Time ''' ||  '''Presenter '''  ||  '''Title '''
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|-
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| 1:00-1:20 || Ivo D. Dinov [http://wiki.stat.ucla.edu/socr/uploads/c/c1/Dinov_BigDataChallenges_AMS_JMM_2014.pdf PDF Slides]  || [http://www.ams.org/amsmtgs/2160_abstracts/1096-68-27.pdf Big Data Challenges in Neuroimaging, Informatics and Genomics Computing (1096-68-27)]
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|-
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| 1:30-1:50 || Elias Bareinboim || [http://www.ams.org/amsmtgs/2160_abstracts/1096-62-2468.pdf Inference with Emphasis on Transportability and External Validity (1096-62-2468)]
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|-
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| 2:00-2:20 || Nguyet T Nguyen [http://wiki.stat.ucla.edu/socr/uploads/b/bd/NguyetNguyen_JMM_2014.pdf PDF Slides]  || [http://www.ams.org/amsmtgs/2160_abstracts/1096-62-2312.pdf Hidden Markov Model for High Frequency Data (1096-62-2312)] 
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|-
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| 2:30-2:50 || Jeff Randell Knisley [http://wiki.stat.ucla.edu/socr/uploads/d/d0/Knisley_AMSSpecSessConsensusSpectral_2014.pdf PDF slides]  || [http://www.ams.org/amsmtgs/2160_abstracts/1096-62-1122.pdf Consensus Spectral Techniques and Machine Learning (1096-62-1122)] 
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|-
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| 3:00-3:20 || Catherine A Bliss  [http://wiki.stat.ucla.edu/socr/uploads/f/fc/Bliss_link_prediction_JMM_2014.pdf PDF Slides]  || [http://www.ams.org/amsmtgs/2160_abstracts/1096-65-1009.pdf Covariance Matrix Adaptation Evolution Strategy for Link Prediction in Dynamic Social Networks (1096-65-1009)] 
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|-
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| 3:30-3:50 || John Ensley [http://wiki.stat.ucla.edu/socr/uploads/4/4a/JohnEnsley_JMM_2014_Twitter.pdf PDF slides]  || [http://www.ams.org/amsmtgs/2160_abstracts/1096-68-2249.pdf My Life as a Tweet Word (1096-68-2249)] 
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|}
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</center>
==Resources==
==Resources==
* Big Data Videos [http://socr.ucla.edu/docs/videos/BigData_AMS_JMM_2014.mp4 mp4] and [http://socr.ucla.edu/docs/videos/BigData_AMS_JMM_2014.mpg mpeg]
* Big Data Videos [http://socr.ucla.edu/docs/videos/BigData_AMS_JMM_2014.mp4 mp4] and [http://socr.ucla.edu/docs/videos/BigData_AMS_JMM_2014.mpg mpeg]
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* Slides/papers: TBD
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* Slides/papers: (see PDF links in table above)
<hr>
<hr>
{{translate|pageName=http://wiki.stat.ucla.edu/socr/index.php?title=SOCR_Events_JMM_BigData_Jan2014}}
{{translate|pageName=http://wiki.stat.ucla.edu/socr/index.php?title=SOCR_Events_JMM_BigData_Jan2014}}

Current revision as of 17:32, 18 January 2014

Contents

SOCR News & Events: 2014 JMM/AMS Special Session on Big-Data: Mathematical and Statistical Modeling, Tools, Services, and Training

Overview

The volume and diversity of biomedical data is exponentially increasing with Peta bytes of imaging and genetics data acquired annually. At the same time, tens-of-thousands of computational algorithms are developed and reported in the literature along with thousands of software tools and services. Scientists demand intuitive, quick and platform-agnostic access to data, software tools, and infrastructure from millions of hardware devices. This explosion of information, scientific techniques, computational models, and technological advances leads to enormous challenges in data management, data interrogation, evidence-based biomedical inference, and reproducibility of findings. Novel mathematical algorithms, statistical analyses and computational tools are necessary to cope with this avalanche of data and hardware devices.

Organizer

Session Logistics

Speakers

Time Presenter Title
1:00-1:20 Ivo D. Dinov PDF Slides Big Data Challenges in Neuroimaging, Informatics and Genomics Computing (1096-68-27)
1:30-1:50 Elias Bareinboim Inference with Emphasis on Transportability and External Validity (1096-62-2468)
2:00-2:20 Nguyet T Nguyen PDF Slides Hidden Markov Model for High Frequency Data (1096-62-2312)
2:30-2:50 Jeff Randell Knisley PDF slides Consensus Spectral Techniques and Machine Learning (1096-62-1122)
3:00-3:20 Catherine A Bliss PDF Slides Covariance Matrix Adaptation Evolution Strategy for Link Prediction in Dynamic Social Networks (1096-65-1009)
3:30-3:50 John Ensley PDF slides My Life as a Tweet Word (1096-68-2249)

Resources

  • Big Data Videos mp4 and mpeg
  • Slides/papers: (see PDF links in table above)





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