SOCR News APS GPS 2026 - Revision history

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‎Other Sessions

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‎Other Sessions

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‎Course Coverage

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‎Course Coverage

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 * Sunday 3/15 at 6:00pm (location: TBD): GDS (off-site) executive committee meeting
-* GDS GPS general meeting: Tue, March 17, 6:45 p.m. Convention Center, Meeting Room 507
+* GDS GPS general meeting: Tue, March 17, 6:45 p.m. Convention Center, Meeting Room 507, EVT-AA58, GDS Unit Business Meeting
 ==Resources==

@@ Line 127: / Line 127: @@
 * Sunday 3/15 at 6:00pm (location: TBD): GDS (off-site) executive committee meeting
-* TBD: GDS GPS general meeting.
+* GDS GPS general meeting: Tue, March 17, 6:45 p.m. Convention Center, Meeting Room 507
 ==Resources==

← Older revision		Revision as of 01:07, 9 March 2026
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	: [https://tciu.predictive.space/ Additional theoretical developments, experimental demonstrations, computational algorithms, and data science applications are available on the '''spacekime/TCIU website'''].		: [https://tciu.predictive.space/ Additional theoretical developments, experimental demonstrations, computational algorithms, and data science applications are available on the '''spacekime/TCIU website'''].
		+
		+	: [https://socr.umich.edu/DSPA2/DSPA2_notes/01_Introduction.html#2_Foundations_of_R More information about editing, interpreting, knitting/compiling and running ''R''-markdown electronic notebooks (*.Rmd) is available on the SOCR DSPA2 site].

	===Spacekime analytics themes===		===Spacekime analytics themes===

@@ Line 37: / Line 37: @@
 * '''Part I (8:00-8:55 AM)''': Course overview with a discussion of the basic mathematical-physics principles underlying the complex-time (''kime'') representation of repeated measurement longitudinal data.
-:: [https://socr.umich.edu/docs/uploads/2026/APS_GPS__2026_KPT_long_Dinov_Tutorial_P1.pdf Part I.1: Kime-Phase Tomography (KPT) (slidedeck)],
+:: [https://socr.umich.edu/docs/uploads/2026/APS_GPS__2026_KPT_long_Dinov_Tutorial_P1.pdf Kime-Phase Tomography (KPT) (slidedeck)],
 * '''Part II (9:00-9:55 AM)''': Translation of fundamental quantum mechanics principles into statistical inference models of time-varying processes. Specifically, we will demonstrate generalizing the classical 4D spatiotemporal sampling to a 5D spacekime manifold, where the phase of complex-time (an extra degree of freedom) encodes repeated random sampling at fixed spatiotemporal locations.
 :: ...
 * '''Part III (10:00-10:55 AM)''': Alternative strategies for transforming observed time-series into kime-surfaces, which are richer, computationally tractable, objects amenable to tensor-based statistical linear modeling and AI model-free inference.

@@ Line 30: / Line 30: @@
 : The course will provide an active learning environment to build competency in the following areas: methods for transforming time-series to kime-surfaces, which are much richer computational objects; mathematical foundations of spacekime analytics; and AI algorithms with applications. A set of end-to-end R-markdown notebooks will provide reproducible and independently validated protocols for data ingestion, mathematical representation and scientific visualization of longitudinal time-courses mapped as 2D parametric manifolds (kime-surfaces), model-based statistical inference, and model-free AI analytics.
-: Heuristic overview of complex-time representation, kime-phase tomography, and spacekime analytics:
+: A ''heuristic overview'' of complex-time representation, kime-phase tomography, and spacekime analytics.
 :: [https://socr.umich.edu/spacekime/img/Spacekime_TCIU_Podcast1_2025.mp3 A brief, 7-minute, spacekime ''podcast'' (audio)].
 :: [https://socr.umich.edu/spacekime/img/ComplexTimeReps_KPT_Spacekime_FieldEquations_video.mp4 A short, 5-minute ''video'' describing complex-time representation, kime-phase tomography (KPT), and spacekime gravitational field equations].

← Older revision		Revision as of 00:43, 9 March 2026
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	: The course will provide an active learning environment to build competency in the following areas: methods for transforming time-series to kime-surfaces, which are much richer computational objects; mathematical foundations of spacekime analytics; and AI algorithms with applications. A set of end-to-end R-markdown notebooks will provide reproducible and independently validated protocols for data ingestion, mathematical representation and scientific visualization of longitudinal time-courses mapped as 2D parametric manifolds (kime-surfaces), model-based statistical inference, and model-free AI analytics.		: The course will provide an active learning environment to build competency in the following areas: methods for transforming time-series to kime-surfaces, which are much richer computational objects; mathematical foundations of spacekime analytics; and AI algorithms with applications. A set of end-to-end R-markdown notebooks will provide reproducible and independently validated protocols for data ingestion, mathematical representation and scientific visualization of longitudinal time-courses mapped as 2D parametric manifolds (kime-surfaces), model-based statistical inference, and model-free AI analytics.
		+
		+	: Heuristic overview of complex-time representation, kime-phase tomography, and spacekime analytics:
		+	:: [https://socr.umich.edu/spacekime/img/Spacekime_TCIU_Podcast1_2025.mp3 A brief, 7-minute, spacekime ''podcast'' (audio)].
		+	:: [https://socr.umich.edu/spacekime/img/ComplexTimeReps_KPT_Spacekime_FieldEquations_video.mp4 A short, 5-minute ''video'' describing complex-time representation, kime-phase tomography (KPT), and spacekime gravitational field equations].

	===Course Coverage===		===Course Coverage===

@@ Line 45: / Line 45: @@
 * '''Part IV (11:00-11:55 AM)''': Simulated and real neuroimaging and macroeconomics data to demonstrate applications of spacekime analytics.
 :: [https://www.socr.umich.edu/TCIU/HTMLs/Chapter6_TCIU_Basic_SpacekimePredictiveAnalytics.html TCIU/Spacekime Analytics Tutorial: Basic TCIU Protocol for Predictive Spacekime Analytics using Longitudinal Data].
 ===Spacekime analytics themes===

@@ Line 32: / Line 32: @@
 ===Course Coverage===
-* '''Part I (8:00-8:55 AM)''': Course overview with a discussion of the basic mathematical-physics principles underlying the complex-time (''kime'') representation of repeated measurement longitudinal data, [https://socr.umich.edu/docs/uploads/2026/APS_GPS__2026_KPT_long_Dinov_Tutorial_P1.pdf Part 1: Kime-Phase Tomography (KPT) (slidedeck)].
+* '''Part I (8:00-8:55 AM)''': Course overview with a discussion of the basic mathematical-physics principles underlying the complex-time (''kime'') representation of repeated measurement longitudinal data, [https://socr.umich.edu/docs/uploads/2026/APS_GPS__2026_KPT_long_Dinov_Tutorial_P1.pdf Part I.1: Kime-Phase Tomography (KPT) (slidedeck)], [https://www.socr.umich.edu/TCIU/HTMLs/Kepler_KPT_Implementation_V5.html Part I.2: Kime-Phase Tomography: (V.5) KPT Validation with Kepler Stellar Light Curves], and [https://www.socr.umich.edu/TCIU/HTMLs/TCIU_SK_Appendix04_Spacekime_Gravity_FieldEquations_V2.html Part I.3: 5D Spacekime Einstein-Hilbert Gravitational Field Equations].
 * '''Part II (9:00-9:55 AM)''': Translation of fundamental quantum mechanics principles into statistical inference models of time-varying processes. Specifically, we will demonstrate generalizing the classical 4D spatiotemporal sampling to a 5D spacekime manifold, where the phase of complex-time (an extra degree of freedom) encodes repeated random sampling at fixed spatiotemporal locations.
-* '''Part III (10:00-10:55 AM)''': Alternative strategies for transforming observed time-series into kime-surfaces, which are richer, computationally tractable, objects amenable to tensor-based statistical linear modeling and AI model-free inference. [https://www.socr.umich.edu/TCIU/HTMLs/Chapter6_TCIU_Basic_SpacekimePredictiveAnalytics.html TCIU/Spacekime Analytics Tutorial: Basic TCIU Protocol for Predictive Spacekime Analytics using Longitudinal Data].
+* '''Part III (10:00-10:55 AM)''': Alternative strategies for transforming observed time-series into kime-surfaces, which are richer, computationally tractable, objects amenable to tensor-based statistical linear modeling and AI model-free inference. [https://www.socr.umich.edu/TCIU/HTMLs/Chapter6_TCIU_MappingLongitudinalTimeseries_2_Kimesurfaces.html Strategies for Mapping Repeated Measurement Longitudinal Data (Time-series) to 2D Manifolds (Kimesurfaces)].
-* '''Part IV (11:00-11:55 AM)''': Simulated and real neuroimaging and macroeconomics data to demonstrate applications of spacekime analytics.
+* '''Part IV (11:00-11:55 AM)''': Simulated and real neuroimaging and macroeconomics data to demonstrate applications of spacekime analytics. [https://www.socr.umich.edu/TCIU/HTMLs/Chapter6_TCIU_Basic_SpacekimePredictiveAnalytics.html TCIU/Spacekime Analytics Tutorial: Basic TCIU Protocol for Predictive Spacekime Analytics using Longitudinal Data].
 ===Spacekime analytics themes===