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* Nov 01, 2016: Ivo Dinov is presenting Predictive Big Data Analytics Workshop, School of Nursing University of Michigan Analytics Seminar Series, 1:00PM, 1240 SNB.
* Nov 01, 2016: Ivo Dinov is presenting Predictive Big Data Analytics Workshop, School of Nursing University of Michigan Analytics Seminar Series, 1:00PM, 1240 SNB.
* Sept 20-21, 2016:  Ivo Dinov, Rich Gonzales, George Alter (University of Michigan), Franco Pestilli, Olaf Sporns, Andrew Saykin (Indiana University), Dhabaleswar Panda, Khaled Hamidouche, Xiaoyi Lu, Hari Subramoni (OSU), Satya Sahoo (CWRU), Daniel Marcus (Washington University), and Lei Wang (Northwestern University) are organizing a 2-day [http://www.neurosciencenetwork.org/ACNN_Workshop_2016.html Midwest Workshop on Big Neuroscience Data, Tools, Protocols & Services], Ann Arbor, MI.
* Sept 20-21, 2016:  Ivo Dinov, Rich Gonzales, George Alter (University of Michigan), Franco Pestilli, Olaf Sporns, Andrew Saykin (Indiana University), Dhabaleswar Panda, Khaled Hamidouche, Xiaoyi Lu, Hari Subramoni (OSU), Satya Sahoo (CWRU), Daniel Marcus (Washington University), and Lei Wang (Northwestern University) are organizing a 2-day [http://www.neurosciencenetwork.org/ACNN_Workshop_2016.html Midwest Workshop on Big Neuroscience Data, Tools, Protocols & Services], Ann Arbor, MI.
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* In October 2016, Ivo Dinov participated in an [http://zika.smartercrowdsourcing.org/anlisis-predictivo-conference.html expert International panel including government officials of a dozen countries affected by the Zika virus]. The panel reviewed evidence and made recommendations to public officials on strategies to combat the spread of the Zika virus and the associated microcephaly in newborns.
* Aug 04, 2016 (8:30-10:30 AM): Ivo Dinov is presenting [http://www.amstat.org/meetings/jsm/2016/onlineprogram/AbstractDetails.cfm?abstractid=319094 Big, Deep, and Dark Data: Fundamentals, Research Challenges, and Opportunities], at the [http://www.amstat.org/meetings/jsm/2016/onlineprogram/ActivityDetails.cfm?SessionID=212981 Recent advances in massive imaging data analysis, Section on Statistics in Imaging], at the [http://www.amstat.org/meetings/jsm/2016/ Joint Statistics Meeting (JSM)], Chicago, IL, (CC-W McCormick Place Convention Center, West Building, Session: 659, Thu, 8/4/2016, 8:30 AM - 10:20 AM, '''CC-W187b''').  
* Aug 04, 2016 (8:30-10:30 AM): Ivo Dinov is presenting [http://www.amstat.org/meetings/jsm/2016/onlineprogram/AbstractDetails.cfm?abstractid=319094 Big, Deep, and Dark Data: Fundamentals, Research Challenges, and Opportunities], at the [http://www.amstat.org/meetings/jsm/2016/onlineprogram/ActivityDetails.cfm?SessionID=212981 Recent advances in massive imaging data analysis, Section on Statistics in Imaging], at the [http://www.amstat.org/meetings/jsm/2016/ Joint Statistics Meeting (JSM)], Chicago, IL, (CC-W McCormick Place Convention Center, West Building, Session: 659, Thu, 8/4/2016, 8:30 AM - 10:20 AM, '''CC-W187b''').  
* July 08, 2016: Ivo Dinov was [http://www.pbs.org/wgbh/nova/next/body/theres-hope-for-fmri-despite-major-software-flaws/ interviewed by NOVA (WGBH/PBS)] on a [http://www.pnas.org/content/early/2016/06/27/1602413113 recent PNAS Report identifying significant potential shortfalls of Big Data functional magnetic resonance imaging (fMRI) studies], of which there may be over 35,000 in the past quarter century. The article used 500 normal control subjects (null data) to generate 3 million simulation studies where every experiment included randomly chosen subjects, either resting state or task activation fMRI, and found false-positive discoveries (significant grouping effects where there were none) up to 70% of the simulations. Although this does not discredit any specific previously published fMRI findings, the investigations suggests the need for novel Big Data analytics methods, and scalable software tools, that can reduce the false-positive rate.
* July 08, 2016: Ivo Dinov was [http://www.pbs.org/wgbh/nova/next/body/theres-hope-for-fmri-despite-major-software-flaws/ interviewed by NOVA (WGBH/PBS)] on a [http://www.pnas.org/content/early/2016/06/27/1602413113 recent PNAS Report identifying significant potential shortfalls of Big Data functional magnetic resonance imaging (fMRI) studies], of which there may be over 35,000 in the past quarter century. The article used 500 normal control subjects (null data) to generate 3 million simulation studies where every experiment included randomly chosen subjects, either resting state or task activation fMRI, and found false-positive discoveries (significant grouping effects where there were none) up to 70% of the simulations. Although this does not discredit any specific previously published fMRI findings, the investigations suggests the need for novel Big Data analytics methods, and scalable software tools, that can reduce the false-positive rate.

Revision as of 20:17, 2 December 2016

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