SOCR Events JMM BigData Jan2014

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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)]
| 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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| 2:00-2:20 || Nguyet T Nguyen || [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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| 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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| 2:30-2:50 || Jeff Randell Knisley || [http://www.ams.org/amsmtgs/2160_abstracts/1096-62-1122.pdf Consensus Spectral Techniques and Machine Learning (1096-62-1122)]   
| 2:30-2:50 || Jeff Randell Knisley || [http://www.ams.org/amsmtgs/2160_abstracts/1096-62-1122.pdf Consensus Spectral Techniques and Machine Learning (1096-62-1122)]   

Revision as of 12:13, 16 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 Consensus Spectral Techniques and Machine Learning (1096-62-1122)
3:00-3:20 Catherine A Bliss Covariance Matrix Adaptation Evolution Strategy for Link Prediction in Dynamic Social Networks (1096-65-1009)
3:30-3:50 John Ensley 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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