Difference between revisions of "SOCR News ASA StatsInImaging 2022"

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== [[SOCR_News | SOCR News & Events]]:  ''Complex-time (Kime) Representation of Longitudinal Data and Spacekime Analytics'' ==
 
== [[SOCR_News | SOCR News & Events]]:  ''Complex-time (Kime) Representation of Longitudinal Data and Spacekime Analytics'' ==
  
[[Image:Spacekime_TCIU_Trivial_PolarCylinder_WaveEqu_Phase.png|150px|thumbnail|right| [https://www.socr.umich.edu/TCIU/ Spacekime/TCIU Project] ]]
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[[Image:WaveEquationSolution_Spacekime_anim.gif|right| [https://www.socr.umich.edu/TCIU/ Spacekime/TCIU Project] ]]
  
 
== Presenter==
 
== Presenter==

Revision as of 14:37, 23 September 2022

SOCR News & Events: Complex-time (Kime) Representation of Longitudinal Data and Spacekime Analytics

Spacekime/TCIU Project

Presenter

Ivo Dinov (University of Michigan, SOCR, MIDAS, PH).
Dr. Dinov is a professor of Health Behavior and Biological Sciences and Computational Medicine and Bioinformatics at the University of Michigan. He is a member of the Michigan Center for Applied and Interdisciplinary Mathematics (MCAIM) and a core member of the University of Michigan Comprehensive Cancer Center. Dr. Dinov serves as Director of the Statistics Online Computational Resource, Co-Director of the multi-institutional Probability Distributome Project, and Associate Director of the Michigan Institute for Data Science (MIDAS). He is a member of the American Statistical Association (ASA), International Association for Statistical Education (IASE), American Mathematical Society (AMS), American Association for the Advancement of Science (AAAS), American Physical Society (APS), and an Elected Member of the International Statistical Institute (ISI).
Joint work with
Yueyang Shen (Michigan),
Milen V. Velev (BTU).

Session Logistics

Abstract

This presentation will describe the new complex-time (kime) representation of longitudinal data and the induced space-kime analytics. This approach translates quantum mechanics concepts into data science and lifts the classical 4D spacetime problems into a 5D spacekime manifold. Direct AI and statistical inference applications include translation of classical random sampling in spacetime to spacekime phase-uncertainty and a Bayesian formulation of spacekime analytics. We will show examples translating time-series data into kimesurfaces and present examples in neuroimaging and macroeconomics data.


Slidedeck: TBD.

Background

References




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