Difference between revisions of "SOCR News APS Ohio Spacekime 2021"
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[[Image:Spacekime_TCIU_Trivial_PolarCylinder_WaveEqu_Phase.png|150px|thumbnail|right| [https://www.socr.umich.edu/TCIU/ Spacekime/TCIU Project] ]] | [[Image:Spacekime_TCIU_Trivial_PolarCylinder_WaveEqu_Phase.png|150px|thumbnail|right| [https://www.socr.umich.edu/TCIU/ Spacekime/TCIU Project] ]] | ||
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== Presenter== | == Presenter== | ||
: [https://umich.edu/~dinov Ivo Dinov], [https://www.umich.edu University of Michigan], [https://www.socr.umich.edu SOCR], [https://midas.umich.edu MIDAS]. | : [https://umich.edu/~dinov Ivo Dinov], [https://www.umich.edu University of Michigan], [https://www.socr.umich.edu SOCR], [https://midas.umich.edu MIDAS]. | ||
| − | :: 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 Center for Complexity and Self-management of Chronic Disease (CSCD Center), Co-Director of the multi-institutional Probability Distributome Project, Associate Director of the Michigan Institute for Data Science (MIDAS), and Associate Director of the Michigan Neuroscience Graduate Program (NGP). 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), and an Elected Member of the | + | :: 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 Center for Complexity and Self-management of Chronic Disease (CSCD Center), Co-Director of the multi-institutional Probability Distributome Project, Associate Director of the Michigan Institute for Data Science (MIDAS), and Associate Director of the Michigan Neuroscience Graduate Program (NGP). 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, and an Elected Member of the International Statistical Institute (ISI). |
==Session Logistics== | ==Session Logistics== | ||
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* '''URL''': [https://jcu.edu/calendar/ohio-region-section-american-physical-society-spring-2021-meeting Official APS Ohio-Region Section Meeting Website]. | * '''URL''': [https://jcu.edu/calendar/ohio-region-section-american-physical-society-spring-2021-meeting Official APS Ohio-Region Section Meeting Website]. | ||
* '''Conference''': [https://meetings.aps.org/Meeting/OSS21/Content/4035 Spring 2021 Meeting of the APS Ohio-Region Section]. | * '''Conference''': [https://meetings.aps.org/Meeting/OSS21/Content/4035 Spring 2021 Meeting of the APS Ohio-Region Section]. | ||
| + | * '''Session''': [https://meetings.aps.org/Meeting/OSS21/Session/B02 Session B02] and [https://meetings.aps.org/Meeting/OSS21/Session/B02.3 B02.00003 talk link]. | ||
| + | * '''Zoom''': [https://johncarrolluniversity.zoom.us/j/93403737045 Zoom Link] | ||
* '''Session Format''': Online virtual meeting (due to SARS-CoV-2 Pandemic). | * '''Session Format''': Online virtual meeting (due to SARS-CoV-2 Pandemic). | ||
* [https://wiki.socr.umich.edu/index.php/SOCR_News_APS_Ohio_Spacekime_2021 Session URL]. | * [https://wiki.socr.umich.edu/index.php/SOCR_News_APS_Ohio_Spacekime_2021 Session URL]. | ||
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We will illustrate how the kime-magnitude (longitudinal time order) and kime-direction (phase) affect the subsequent predictive analytics and the induced scientific inference. The mathematical foundation of spacekime calculus reveal various statistical implications including inferential uncertainty and a Bayesian formulation of spacekime analytics. Complexifying time allows the lifting of all commonly observed processes from the classical 4D Minkowski spacetime to a 5D spacekime manifold, where a number of interesting mathematical problems arise. Direct data science applications of spacekime analytics will be demonstrated using simulated data and clinical observations (e.g., structural and fMRI). | We will illustrate how the kime-magnitude (longitudinal time order) and kime-direction (phase) affect the subsequent predictive analytics and the induced scientific inference. The mathematical foundation of spacekime calculus reveal various statistical implications including inferential uncertainty and a Bayesian formulation of spacekime analytics. Complexifying time allows the lifting of all commonly observed processes from the classical 4D Minkowski spacetime to a 5D spacekime manifold, where a number of interesting mathematical problems arise. Direct data science applications of spacekime analytics will be demonstrated using simulated data and clinical observations (e.g., structural and fMRI). | ||
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| + | '''Slidedeck''': [https://socr.umich.edu/docs/uploads/2021/Dinov_Spacekime_APS_2021.pdf PDF Slides]. | ||
==Background== | ==Background== | ||
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Latest revision as of 12:42, 26 January 2022
Contents
SOCR News & Events: Data Science, Time Complexity, and Spacekime Analytics
Presenter
- 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 Center for Complexity and Self-management of Chronic Disease (CSCD Center), Co-Director of the multi-institutional Probability Distributome Project, Associate Director of the Michigan Institute for Data Science (MIDAS), and Associate Director of the Michigan Neuroscience Graduate Program (NGP). 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, and an Elected Member of the International Statistical Institute (ISI).
Session Logistics
- Date/Time: Saturday, April 10, 2021, 8:00 AM–9:12 AM, Central Daylight Savings Time, CT (UTC-5)
- Title: Data Science, Time Complexity, and Spacekime Analytics
- Registration: Registration Link.
- URL: Official APS Ohio-Region Section Meeting Website.
- Conference: Spring 2021 Meeting of the APS Ohio-Region Section.
- Session: Session B02 and B02.00003 talk link.
- Zoom: Zoom Link
- Session Format: Online virtual meeting (due to SARS-CoV-2 Pandemic).
- Session URL.
Abstract
There is a substantial need to develop, validate, productize, and support novel mathematical techniques, advanced statistical computing algorithms, transdisciplinary tools, and effective artificial intelligence applications. Extracting actionable information from complex, multi-source, and time-varying observable processes uncovers an interesting synergy between quantum mechanics, artificial intelligence (AI) and data science. Spacekime analytics is a new technique for modeling high-dimensional longitudinal data. This approach relies on extending the physical notions of time, events, particles, and wavefunctions to their AI counterparts; complex-time (kime), complex-events (kevents), data, and inference-functions.
We will illustrate how the kime-magnitude (longitudinal time order) and kime-direction (phase) affect the subsequent predictive analytics and the induced scientific inference. The mathematical foundation of spacekime calculus reveal various statistical implications including inferential uncertainty and a Bayesian formulation of spacekime analytics. Complexifying time allows the lifting of all commonly observed processes from the classical 4D Minkowski spacetime to a 5D spacekime manifold, where a number of interesting mathematical problems arise. Direct data science applications of spacekime analytics will be demonstrated using simulated data and clinical observations (e.g., structural and fMRI).
Slidedeck: PDF Slides.
Background
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