SOCR News ISI WSC IPS35 2019

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SOCR News & Events: International Statistics Institute (ISI)

2019 World Statistics Congress (WSC)

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



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
... TBD Michelle Liou Institute of Statistical Science Academia Sinica
... TBD S. Ejaz Ahmed Brock University


Will be available in 2019...


Predictive Analytics of Big Neuroscience Data

This talk will present some of the Big Neuroscience Data research and education challenges and opportunities. Specifically, we will identify the core characteristics of complex neuroscience data, discuss strategies for data harmonization and aggregation, and show case-studies using large normal and pathological cohorts. Examples of methods that will be demonstrated include DataSifter (enabling secure sharing of data), compressive big data analytics (facilitating inference on multi-source heterogeneous datasets), and model-free prediction (forecasting of clinical features or derived computed phenotypes). Simulated data as well as clinical data (UK Biobank, Alzheimer’s Disease Neuroimaging Initiative, and amyotrophic lateral sclerosis case-studies) will be used for testing and validation of the techniques. In support of open-science, result reproducibility, and methodological improvements, all datasets, statistical methods, computational algorithms, and software tools are freely available online.

Large Brain Network Data Modeling using Deep Learning and Statistical Inference








Short Bios

Ivo D. Dinov

Ivo D. Dinov is a professor of Health Behavior and Biological Sciences and Computational Medicine and Bioinformatics at the University of Michigan. He directs the Statistics Online Computational Resource and co-directs the Center for Complexity and Self-management of Chronic Disease (CSCD) and the multi-institutional Probability Distributome Project. Dr. Dinov is an Associate Director for Education and Training, of the Michigan Institute for Data Science (MIDAS). He is a member of the American Statistical Association (ASA), the International Association for Statistical Education (IASE), the American Medical Informatics Association (AMIA), as well as an Elected Member of the Institutional Statistical Institute (ISI).

Eric Tatt Wei Ho


Yunjin Choi


Michelle Liou


S. Ejaz Ahmed


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