SOCR News CMStatistics2022

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SOCR News & Events: Quantum Mechanics Uncertainty, Data Science Inference, and AI in Complex Time (Kime)

Presenters

Ivo Dinov (University of Michigan, SOCR, MIDAS), Yueyang Shen (University of Michigan), and Milen V. Velev (BTU).
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).

Session Logistics

  • Date/Time: Thursday, March 17, 2022, 3:00 PM – 5:36 PM, GMT
  • Talk Title: Quantum Mechanics Uncertainty, Data Science Inference, and AI in Complex Time (Kime)
  • Registration: Registration Link
  • Conference: CMStatistics 2022 meeting website
  • Session: Statistical Neuroimaging
  • Zoom: TBD
  • Talk Format: Hybrid/In-Person/Online presentation; specific time:TBD


Abstract

This talk will translate quantum mechanical uncertainty principles to address data science challenges by using complex time (kime) to lift the classical 4D spacetime into the 5D spacekime manifold. We extend the laws of velocity, momentum, Lorentz transformations, and 4D solutions of Einstein’s equations to their corresponding counterparts in 5D spacekime. Direct AI 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 longitudinal data to kimesurfaces using neuroimaging and macroeconomics data.


Slidedeck: TBD.

Background

References




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