SOCR News DCMB ToolsTechSeminar 2020 - Revision history

Dinov: /* SOCR News & Events: Spacekime Analytics */

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‎SOCR News & Events: Spacekime Analytics

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Dinov at 13:32, 7 July 2020

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 [[Image:SOCR_UMich_2020a.png|150px|thumbnail|right| [http://socr.umich.edu SOCR Resource] ]]
-The [https://medicine.umich.edu/dept/dcmb/events/tools-technology-seminar-series DCMB Tools and Technology Seminar Series] highlights tools and nascent technologies that are under development or in current use and are of interest to UM R&D community. These seminars are streamed live, using BlueJeans Events, and are also archived and made available for future viewing via video streaming via the below video archive which links to the DCMB YouTube Chanel.
+The [https://medicine.umich.edu/dept/dcmb/events/tools-technology-seminar-series DCMB Tools and Technology Seminar Series] highlights tools and nascent technologies that are under development or in current use and are of interest to UM R&D community. These seminars are streamed live, using Zoom/BlueJeans Events, and are also archived and made available for future viewing via video streaming via the below video archive which links to the DCMB YouTube Chanel.
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 * This work is sponsored in part by NIH Grants [http://projectreporter.nih.gov/project_info_details.cfm?aid=8975330&icde=25689118 P30 DK089503], [http://projectreporter.nih.gov/project_info_description.cfm?aid=8821268&icde=22205726 P20 NR015331], [https://projectreporter.nih.gov/project_info_description.cfm?aid=9816658 R01CA233487], and [https://projectreporter.nih.gov/project_info_details.cfm?aid=9842024 R01MH121079], as well as, NSF Grants [http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1916425 1916425], [http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1734853 1734853] and [http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1636840 1636840].
 * Dinov, ID and Velev, MV (2021) [https://www.degruyter.com/view/title/576646 Data Science: Time Complexity, Inferential Uncertainty, and Spacekime Analytics], De Gruyter (STEM Series), Berlin/Boston, ISBN 9783110697803 / 3110697807.
-* SOCR Home page: http://www.socr.umich.edu
+* SOCR Home page: https://www.socr.umich.edu
-{{translate|pageName=http://wiki.socr.umich.edu/index.php/SOCR_News_DCMB_ToolsTechSeminar_2020}}
+{{translate|pageName=https://wiki.socr.umich.edu/index.php/SOCR_News_DCMB_ToolsTechSeminar_2020}}

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 ==Logistics==
 * [https://medicine.umich.edu/dept/dcmb/events/tools-technology-seminar-series DCMB Tools and Technology Seminar Series]
-* '''Date/Times''': Thursday, October 15, 2020, 12 Noon (ET)
+* '''Date/Times''': Thursday, October 8, 2020, 12 Noon (ET)
-* '''Place''': [https://maps.studentlife.umich.edu/building/palmer-commons 2036 Palmer Commons]
+* '''Place''': [https://maps.studentlife.umich.edu/building/palmer-commons 2036 Palmer Commons], [https://umich-health.zoom.us/j/94886745590?pwd=LzhLU243K2ZhbXNzU1BJRHQ5V25BZz09 Zoom Event]
 * '''Title''':  ''Spacekime Analytics''
 * '''Presenter''':  [http://umich.edu/~dinov Ivo Dinov], joint work with [http://socr.umich.edu/people/Milen_Velev.html Milen V. Velev] (Burgas University)

← Older revision		Revision as of 17:10, 4 October 2020
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	: Direct data science applications of spacekime analytics will be demonstrated using simulated data, clinical observations (e.g., fMRI, UK Biobank), and environmental air quality data.		: Direct data science applications of spacekime analytics will be demonstrated using simulated data, clinical observations (e.g., fMRI, UK Biobank), and environmental air quality data.
		+
		+	* '''Slidedeck''': [https://socr.umich.edu/docs/uploads/2020/Dinov_Spacekime_DCMB_ToolsTechSeminar_October_2020.pdf Presentation Slides]

	== Objectives ==		== Objectives ==

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 * '''Presenter''':  [http://umich.edu/~dinov Ivo Dinov], joint work with [http://socr.umich.edu/people/Milen_Velev.html Milen V. Velev] (Burgas University)
 [[Image:Spacekime_TCIU_Trivial_PolarCylinder_WaveEqu_Phase.png|150px|thumbnail|right| [https://www.socr.umich.edu/TCIU/ Spacekime/TCIU Project] ]]
-* '''Abstract''': Large amounts of heterogeneous information immersed in all human experiences presents important challenges of managing, modeling, analyzing, interpreting, and visualizing complex information. There are substantial opportunities to develop, validate, and productize novel mathematical techniques, advanced statistical computing algorithms, transdisciplinary tools, and effective artificial intelligence apps.
+* '''Abstract''': The large amounts of heterogeneous information, which is immersed in all human experiences, present important challenges of managing, modeling, analyzing, interpreting, and visualizing complex information. There are substantial opportunities to develop, validate, and productize novel mathematical techniques, advanced statistical computing algorithms, transdisciplinary tools, and effective artificial intelligence apps.
 : Spacekime analytics is a new technique for modeling high-dimensional longitudinal data. This approach relies on extending the notions of ''time'', ''events'', ''particles'', and ''wavefunctions'' to 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 reveals 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 spacetime manifold, where a number of interesting mathematical problems arise.

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 * '''Abstract''': Larege amounts of heterogeneous information imersed in all human experiences presents important challenges of managing, modeling, analyzing, interpreting, and visualizing complex information. There are substantial opportunities to develop, validate, and productize novel mathematical techniques, advanced statistical computing algorithms, transdisciplinary tools, and effective artificial intelligence apps.
-Spacekime analytics is a new technique for modeling high-dimensional longitudinal data. This approach relies on extending the notions of ''time'', ''events'', ''particles'', and ''wavefunctions'' to 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 reveals 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 spacetime manifold, where a number of interesting mathematical problems arise.
+: Spacekime analytics is a new technique for modeling high-dimensional longitudinal data. This approach relies on extending the notions of ''time'', ''events'', ''particles'', and ''wavefunctions'' to 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 reveals 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 spacetime manifold, where a number of interesting mathematical problems arise.
-Direct data science applications of spacekime analytics will be demonstrated using simulated data, clinical observations (e.g., fMRI, UK Biobank), and environmental air quality data.
+: Direct data science applications of spacekime analytics will be demonstrated using simulated data, clinical observations (e.g., fMRI, UK Biobank), and environmental air quality data.
 == Objectives ==