Difference between revisions of "SOCR News APS Dinov Spacekime March 2022"

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: [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 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, and an Elected Member of the International Statistical Institute (ISI).
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:: 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==
 
==Session Logistics==
* '''Date/Time''': Saturday, April 10, 2021, 8:00 AM–9:12 AM, Central Daylight Savings Time, [https://www.timeanddate.com/time/zones/cdt CT (UTC-5)]
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* '''Date/Time''': Thursday, March 17, 2022, 3:00 PM – 5:36 PM, [https://www.timeanddate.com/time/zones/ct US Central Time, (UTC-6)]
* '''Title''': D''ata Science, Time Complexity, and Spacekime Analytics''
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* '''Talk Title''': ''Quantum Physics Interface to Data Science, Artificial Intelligence, and Spacekime Analytics''
* '''Registration''': [https://my.aps.org/NC__Event?id=a0l5G00000D7bkKQAR Registration Link].
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* '''Registration''': [https://march.aps.org/ Registration Link]
* '''URL''': [https://jcu.edu/calendar/ohio-region-section-american-physical-society-spring-2021-meeting Official APS Ohio-Region Section Meeting Website].
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* '''URL''': [https://meetings.aps.org/Meeting/MAR22/Session/W66.5 APS session W66 meeting website]
* '''Conference''': [https://meetings.aps.org/Meeting/OSS21/Content/4035 Spring 2021 Meeting of the APS Ohio-Region Section].
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* '''Conference''': [https://march.aps.org/ March 2022 APS Meeting]
* '''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].
+
* '''Session''': [https://meetings.aps.org/Meeting/MAR22/Session/W66 Session W66 (Frontiers in Fundamental Physics II)]
* '''Zoom''': [https://johncarrolluniversity.zoom.us/j/93403737045 Zoom Link]
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* '''Zoom''': <Zoom Link provided to registered participants>
* '''Session Format''':  Online virtual meeting (due to SARS-CoV-2 Pandemic).
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* '''Talk Format''':  Online virtual presentation; specific time: 3:48 PM–4:00 PM CT
* [https://wiki.socr.umich.edu/index.php/SOCR_News_APS_Ohio_Spacekime_2021 Session URL].
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* [https://wiki.socr.umich.edu/index.php/SOCR_News_APS_Dinov_Spacekime_March_2022 This URL]
  
 
== Abstract==
 
== 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.  
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Physics is in the core of many data-intensive research activities; from governing molecular interactions, to modeling social behavior networks, enabling solid-state data storage, facilitating Ising modeling of numerical simulations, and underpinning Metropolis–Hastings estimation and optimization in machine learning (ML) and artificial intelligence (AI) applications. This talk will present a direct connection between quantum mechanical principles, data science foundations, and statistical inference on longitudinal processes.
  
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).
+
By extending the physical concepts of time, events, particles, and wavefunctions to their AI counterparts – complex-time (kime), complex-events (kevents), data, and inference-functions – spacekime analytics provides a new foundation for representation, modeling, analyzing, and interpreting dynamic high-dimensional data. We will show the effects of kime-magnitude (longitudinal time order) and kime-direction (phase) on AI predictive analytics, forecasting, regression, classification, and scientific inference.
  
 +
The mathematical foundation of spacekime analytics also provide mechanisms to introduce spacekime calculus, expand Heisenberg’s uncertainty principle to reveal statistical implications of inferential uncertainty, and a develop a Bayesian formulation of spacekime inference. Lifting the dimension of time opens a number of challenging theoretical, experimental, and computational data science problems. It leads to a new representation of commonly observed processes from the classical 4D Minkowski spacetime to a 5D spacekime manifold. Using simulated data and clinical observations (e.g., structural and functional MRI), we will demonstrate alternative strategies to transform time-varying processes (time-series) to kime-surfaces and show examples of spacekime analytics.
  
'''Slidedeck''': [https://socr.umich.edu/docs/uploads/2021/Dinov_Spacekime_APS_2021.pdf PDF Slides].
+
 
 +
'''Slidedeck''': [https://socr.umich.edu/docs/uploads/2022/Dinov_Spacekime_APS_2022.pdf PDF Slides].
  
 
==Background==
 
==Background==
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** [https://dspa.predictive.space Data Science and Predictive Analytics]
 
** [https://dspa.predictive.space Data Science and Predictive Analytics]
  
 +
==References==
 +
* Dinov, ID and Velev, MV (2022) [https://doi.org/10.1515/9783110697827 Data Science: Time Complexity, Inferential Uncertainty, and Spacekime Analytics], De Gruyter (STEM Series), Berlin/Boston, ISBN 9783110697803 / 3110697807, DOI 10.1515/9783110697827.
 
* [https://www.socr.umich.edu/spacekime/ Spacekime website]
 
* [https://www.socr.umich.edu/spacekime/ Spacekime website]
 
* [https://tciu.predictive.space/ Time-Complexity and Inferential Uncertainty (TCIU)]
 
* [https://tciu.predictive.space/ Time-Complexity and Inferential Uncertainty (TCIU)]

Revision as of 13:55, 26 January 2022

SOCR News & Events: Data Science, Time Complexity, and Spacekime Analytics

Presenter

Ivo Dinov, University of Michigan, SOCR, 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 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

Abstract

Physics is in the core of many data-intensive research activities; from governing molecular interactions, to modeling social behavior networks, enabling solid-state data storage, facilitating Ising modeling of numerical simulations, and underpinning Metropolis–Hastings estimation and optimization in machine learning (ML) and artificial intelligence (AI) applications. This talk will present a direct connection between quantum mechanical principles, data science foundations, and statistical inference on longitudinal processes.

By extending the physical concepts of time, events, particles, and wavefunctions to their AI counterparts – complex-time (kime), complex-events (kevents), data, and inference-functions – spacekime analytics provides a new foundation for representation, modeling, analyzing, and interpreting dynamic high-dimensional data. We will show the effects of kime-magnitude (longitudinal time order) and kime-direction (phase) on AI predictive analytics, forecasting, regression, classification, and scientific inference.

The mathematical foundation of spacekime analytics also provide mechanisms to introduce spacekime calculus, expand Heisenberg’s uncertainty principle to reveal statistical implications of inferential uncertainty, and a develop a Bayesian formulation of spacekime inference. Lifting the dimension of time opens a number of challenging theoretical, experimental, and computational data science problems. It leads to a new representation of commonly observed processes from the classical 4D Minkowski spacetime to a 5D spacekime manifold. Using simulated data and clinical observations (e.g., structural and functional MRI), we will demonstrate alternative strategies to transform time-varying processes (time-series) to kime-surfaces and show examples of spacekime analytics.


Slidedeck: PDF Slides.

Background

References




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