Difference between revisions of "SOCR News ISI WSC IPS35 2019"

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| ... || Large Brain Network Data Modeling using Deep Learning and Statistical Inference || [https://scholar.google.com/citations?user=_PZTBqEAAAAJ&hl=en Eric Tatt Wei Ho] || [https://www.utp.edu.my/The-University/Directories/Pages/Dr%20Ho%20Tatt%20Wei.aspx Universiti Teknologi PETRONAS (UTP), Malaysia]
 
| ... || Large Brain Network Data Modeling using Deep Learning and Statistical Inference || [https://scholar.google.com/citations?user=_PZTBqEAAAAJ&hl=en Eric Tatt Wei Ho] || [https://www.utp.edu.my/The-University/Directories/Pages/Dr%20Ho%20Tatt%20Wei.aspx Universiti Teknologi PETRONAS (UTP), Malaysia]
 
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| ... || TBD || [https://brocku.ca/mathematics-science/mathematics/directory/syed-ejaz-ahmed/ S. Ejaz Ahmed] || [https://brocku.ca/brock-news/tag/syed-ejaz-ahmed Brock University]
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| ... || TBD || [ Yunjin Choi] || [https://sites.google.com/site/yunjinchoistat/ National University of Singapore]
 
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| ... || ... || ... || ...
 
| ... || ... || ... || ...

Revision as of 15:56, 1 October 2018

SOCR News & Events: International Statistics Institute (ISI)

2019 World Statistics Congress (WSC)

Invited Paper Session (IPS35): Imaging Statistics and Predictive Data Analytics

Logistics

Abstract

Petabytes of imaging, clinical, biospecimen, genetics and phenotypic biomedical data are acquired annually. Tens-of-thousands of new methods and computational algorithms are developed and reported in the literature and thousands of software tools and data analytic services are introduced each year. This Imaging Statistics and Predictive Data Analytics session will include presentations of leading experts in biomedical imaging, computational neuroscience, and statistical learning focused on streamlining big biomedical data methodologies as well as techniques for management, aggregation, manipulation, computational modeling, and statistical inference. The session will blend innovative model-based and model-free techniques for representation, analysis and interpretation of large, heterogeneous, multi-source, incomplete and incongruent imaging and phenotypic data elements.

Session focus

This session will be of interest to many theoretical statisticians and applied biomedical researchers for the following reasons:

  • The digital revolution demands substantial quantitative skills, data-literacy, and analytical competence: Health science doctoral programs need to be revised and expanded to build basic-science (STEM) expertise, emphasize team-science, rely on holistic understanding of biomedical systems and health problems, and amplify dexterous abilities to handle, interrogate and interpret complex multisource information.
  • The amount of newly acquired biomedical imaging data is increasing exponentially. This demands innovative statistical and computational strategies to aggregate, process, and interpret the deluge of imaging, clinical and phenotypic information.
  • Trans-disciplinary training and inter-professional education is critical for ethical and collaborative research involving complex biomedical imaging and health conditions.
  • Exploratory and predictive Big Data analytics is pivotally important and complementary to traditional hypothesis-driven confirmatory analyses.


Session Program

Time Title Presenter Affiliation
... Predictive Analytics of Big Neuroscience Data Ivo D Dinov SOCR, University of Michigan
... Large Brain Network Data Modeling using Deep Learning and Statistical Inference Eric Tatt Wei Ho Universiti Teknologi PETRONAS (UTP), Malaysia
... TBD [ Yunjin Choi] National University of Singapore
... ... ... ...
... ... ... ...

Materials

Will be available in 2019...





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