SOCR News ISS JSM 2025 - Revision history

Dinov: /* Innovations in Statistical, Machine Learning, and Deep Learning Methods for Complex Data */

2025-08-01T14:59:24Z

‎Innovations in Statistical, Machine Learning, and Deep Learning Methods for Complex Data

Dinov: /* Special Statistical Group Events */

2025-08-01T13:38:52Z

‎Special Statistical Group Events

Dinov: /* Special Statistical Group Events */

2025-08-01T13:33:42Z

‎Special Statistical Group Events

Dinov: /* Special Statistical Group Events */

2025-08-01T13:31:51Z

‎Special Statistical Group Events

Dinov: /* Special Statistical Group Events */

2025-08-01T13:28:42Z

‎Special Statistical Group Events

Dinov: /* Other relevant sessions/talks */

2025-08-01T13:25:17Z

‎Other relevant sessions/talks

Dinov: /* Session Logistics */

2025-07-16T19:20:52Z

‎Session Logistics

Dinov: /* Other relevant sessions/talks */

2025-05-22T13:02:51Z

‎Other relevant sessions/talks

Dinov: /* Other relevant sessions/talks */

2025-04-05T20:08:17Z

‎Other relevant sessions/talks

Dinov: /* Other relevant sessions/talks */

2025-04-05T20:08:01Z

‎Other relevant sessions/talks

@@ Line 55: / Line 55: @@
 '''Chair''': Zhengjun Zhang Chair (Chinese Academy of Sciences), Organizer: Chunming Zhang (University of Wisconsin-Madison)
-'''Date''': Monday, Aug 4: 10:30 AM - 12:20 PM
+'''Date''': Monday, Aug 4: 10:30 AM - 12:20 PM,  Music City Center Room: CC-104E
 ''Sponsors'': Section on Nonparametric Statistics, International Statistical Institute, Section on Statistical Learning and Data Science

@@ Line 102: / Line 102: @@
 * Tuesday, Aug 5, 2025 12:30-2:00PM: [https://community.amstat.org/jointscsg-section/home Statistical Computing/Graphics Mixer], Omni Nashville Hotel; H-Cumberland 6.
 <hr>
 {{translate|pageName=https://wiki.socr.umich.edu/index.php?title=SOCR_News_ISS_JSM_2025}}

← Older revision		Revision as of 13:33, 1 August 2025
Line 98:		Line 98:

	* Monday, August 4, 2025, 6pm to 8pm: [https://community.amstat.org/tshs/home TSHS Reception/Mixer and Award Ceremony], Music City Center, Room CC-401A		* Monday, August 4, 2025, 6pm to 8pm: [https://community.amstat.org/tshs/home TSHS Reception/Mixer and Award Ceremony], Music City Center, Room CC-401A
		+
		+	* Monday, August 4, 2025, 6:30 8:30 pm: JSM 2025 Informal Dinner [https://community.amstat.org/statisticsinimagingsection/home Statistics in Imaging Section], Location: Ella's by Christian Petroni (210 Molloy St, Nashville)

	* Tuesday, Aug 5, 2025 12:30-2:00PM: [https://community.amstat.org/jointscsg-section/home Statistical Computing/Graphics Mixer], Omni Nashville Hotel; H-Cumberland 6.		* Tuesday, Aug 5, 2025 12:30-2:00PM: [https://community.amstat.org/jointscsg-section/home Statistical Computing/Graphics Mixer], Omni Nashville Hotel; H-Cumberland 6.

← Older revision		Revision as of 13:31, 1 August 2025
Line 94:		Line 94:

	* Monday, August 4, 2025, 4:30 PM – 5:30 PM: [https://community.amstat.org/statisticsinimagingsection/home Statistics in Imaging (SII)] Business Meeting, Omni Nashville Hotel – Room H (Gibson Board Room)		* Monday, August 4, 2025, 4:30 PM – 5:30 PM: [https://community.amstat.org/statisticsinimagingsection/home Statistics in Imaging (SII)] Business Meeting, Omni Nashville Hotel – Room H (Gibson Board Room)
		+
		+	* Monday, August 4, 2025, 6:00 PM – 7:00 PM: FSU-2025-JSM-Reception, Music City Center, Room Name: CC-105A
		+
		+	* Monday, August 4, 2025, 6pm to 8pm: [https://community.amstat.org/tshs/home TSHS Reception/Mixer and Award Ceremony], Music City Center, Room CC-401A

	* Tuesday, Aug 5, 2025 12:30-2:00PM: [https://community.amstat.org/jointscsg-section/home Statistical Computing/Graphics Mixer], Omni Nashville Hotel; H-Cumberland 6.		* Tuesday, Aug 5, 2025 12:30-2:00PM: [https://community.amstat.org/jointscsg-section/home Statistical Computing/Graphics Mixer], Omni Nashville Hotel; H-Cumberland 6.

@@ Line 91: / Line 91: @@
 * Sunday, August 3, 2025 7:00 PM-8:30 PM: [https://www.nature.com/nm/statistics-advisory-panel Nature Medicine Statistical Advisory Board] reception: Omni Nashville Hotel; Room: H - Music Row 6.
-* Monday, Aug 4, 2025 12:30-2:00PM: Business Meeting of the ASA Statistical Computing Section, Omni Nashville Hotel H-Mockingbird 2.
+* Monday, Aug 4, 2025 12:30-2:00PM: Business Meeting of the [https://community.amstat.org/jointscsg-section/home ASA Statistical Computing Section], Omni Nashville Hotel H-Mockingbird 2.
 <hr>
 {{translate|pageName=https://wiki.socr.umich.edu/index.php?title=SOCR_News_ISS_JSM_2025}}

@@ Line 86: / Line 86: @@
 : Presenter: Jose Rodriguez-Acosta, Texas A&M University, co authors, Sharmistha Guha, Texas A&M University, and Ivo Dinov, Statistics Online Computational Resource (Michigan)
 <hr>
 {{translate|pageName=https://wiki.socr.umich.edu/index.php?title=SOCR_News_ISS_JSM_2025}}

@@ Line 5: / Line 5: @@
 ==Session Logistics==
 * '''Title''': [https://ww3.aievolution.com/JSMAnnual2025/Events/viewEv?ev=1049 ''Statistical Inference and AI Modeling of High-Dimensional Longitudinal Data'']
 * '''Organizer''': [https://www.socr.umich.edu/people/dinov/ Ivo Dinov (Michigan)]
 * '''Chair''': [https://pages.stat.wisc.edu/~cmzhang/ Chunming Zhang, University of Wisconsin-Madison]
@@ Line 10: / Line 12: @@
 * '''Discussant''': [https://www.socr.umich.edu/people/dinov/ Ivo Dinov (Michigan)], Statistics Online Computational Resource (SOCR)
 * '''Date/Time''': [https://ww3.aievolution.com/JSMAnnual2025/Events/viewEv?ev=1049 Tuesday, 8/5/2025, 2:00-4:00 PM] [https://time.is/ET US ET]
-* '''Venue''': [https://nashvillemusiccitycenter.com/ Music City Center], 201 Rep. John Lewis Way South, Nashville, TN 37203, [https://nashvillemusiccitycenter.com/maps-parking map and parking].
+* '''Venue''': [https://nashvillemusiccitycenter.com/ Music City Center, Room: CC-107B], 201 Rep. John Lewis Way South, Nashville, TN 37203, [https://nashvillemusiccitycenter.com/maps-parking map and parking].
 * '''Registration''': [https://ww2.amstat.org/meetings/jsm/2025/registration.cfm 2025 JSM Registration] (starts May 1, 2025)
 * '''Conference''': [https://ww2.amstat.org/meetings/jsm/2025/ 2025 Joint Statistical Meeting (JSM)]

@@ Line 58: / Line 58: @@
 : ''Speaker'': Ivo Dinov, Statistics Online Computational Resource (Michigan)
-: ''Title'': Complex-time Representation of Longitudinal Processes and Topological Kime-Surface Analysis
+: ''Title'': [https://wiki.socr.umich.edu/images/a/aa/Dinov_Spacekime_2025_Slidedeck_JSM25.pdf Complex-time Representation of Longitudinal Processes and Topological Kime-Surface Analysis]
 :: Complex-time (kime) extends the traditional representation of temporal processes into the complex plane and captures the dynamics of both classical longitudinal time and repeated-sampling process variability. Novel approaches for analyzing longitudinal data can be developed that build on the 2D parametric manifold representations of time-varying processes repeatedly observed under controlled conditions. Longitudinal processes that are typically modeled using time series are transformed into multidimensional surfaces called kime-surfaces, which jointly encode the internal dynamics of the processes as well as sampling variability. There are alternative strategies to transform classical time-courses to kime-surfaces. The spacekime framework facilitates the application of advanced topological methods, such as persistent homology, to these kime-surfaces. Topological kime-surface analysis involves studying the topological features of kime-surfaces, such as connected components, loops, and voids, which remain invariant under continuous deformations. These topological invariants can be used to classify different types of time-varying processes, detect anomalies, and uncover hidden patterns that are not apparent in traditional time-series analysis.

← Older revision		Revision as of 20:08, 5 April 2025
Line 61:		Line 61:
	:: Complex-time (kime) extends the traditional representation of temporal processes into the complex plane and captures the dynamics of both classical longitudinal time and repeated-sampling process variability. Novel approaches for analyzing longitudinal data can be developed that build on the 2D parametric manifold representations of time-varying processes repeatedly observed under controlled conditions. Longitudinal processes that are typically modeled using time series are transformed into multidimensional surfaces called kime-surfaces, which jointly encode the internal dynamics of the processes as well as sampling variability. There are alternative strategies to transform classical time-courses to kime-surfaces. The spacekime framework facilitates the application of advanced topological methods, such as persistent homology, to these kime-surfaces. Topological kime-surface analysis involves studying the topological features of kime-surfaces, such as connected components, loops, and voids, which remain invariant under continuous deformations. These topological invariants can be used to classify different types of time-varying processes, detect anomalies, and uncover hidden patterns that are not apparent in traditional time-series analysis.		:: Complex-time (kime) extends the traditional representation of temporal processes into the complex plane and captures the dynamics of both classical longitudinal time and repeated-sampling process variability. Novel approaches for analyzing longitudinal data can be developed that build on the 2D parametric manifold representations of time-varying processes repeatedly observed under controlled conditions. Longitudinal processes that are typically modeled using time series are transformed into multidimensional surfaces called kime-surfaces, which jointly encode the internal dynamics of the processes as well as sampling variability. There are alternative strategies to transform classical time-courses to kime-surfaces. The spacekime framework facilitates the application of advanced topological methods, such as persistent homology, to these kime-surfaces. Topological kime-surface analysis involves studying the topological features of kime-surfaces, such as connected components, loops, and voids, which remain invariant under continuous deformations. These topological invariants can be used to classify different types of time-varying processes, detect anomalies, and uncover hidden patterns that are not apparent in traditional time-series analysis.

−	: New AI models can be developed to predict, classify, tesselate, and forecast the behavior of high-dimensional longitudinal data, such as functional magnetic resonance imaging (fMRI), by leveraging complex-time representation of time-varying processes and topological analysis. Kime-surfaces represent mathematically-rich and computationally-tractable data objects that can be interrogated via statistical-learning and artificial intelligence techniques. Spacekime analytics has broad applicability, ranging from personalized medicine to environmental monitoring, and statistical obfuscation of sensitive information.	+	:: New AI models can be developed to predict, classify, tesselate, and forecast the behavior of high-dimensional longitudinal data, such as functional magnetic resonance imaging (fMRI), by leveraging complex-time representation of time-varying processes and topological analysis. Kime-surfaces represent mathematically-rich and computationally-tractable data objects that can be interrogated via statistical-learning and artificial intelligence techniques. Spacekime analytics has broad applicability, ranging from personalized medicine to environmental monitoring, and statistical obfuscation of sensitive information.

@@ Line 53: / Line 53: @@
 '''Chair''': Zhengjun Zhang Chair (Chinese Academy of Sciences), Organizer: Chunming Zhang (University of Wisconsin-Madison)
-'''Date''': Monday, Aug 4: 10:30 AM - 12:20 PM]
+'''Date''': Monday, Aug 4: 10:30 AM - 12:20 PM
 ''Sponsors'': Section on Nonparametric Statistics, International Statistical Institute, Section on Statistical Learning and Data Science
@@ Line 61: / Line 61: @@
 :: Complex-time (kime) extends the traditional representation of temporal processes into the complex plane and captures the dynamics of both classical longitudinal time and repeated-sampling process variability. Novel approaches for analyzing longitudinal data can be developed that build on the 2D parametric manifold representations of time-varying processes repeatedly observed under controlled conditions. Longitudinal processes that are typically modeled using time series are transformed into multidimensional surfaces called kime-surfaces, which jointly encode the internal dynamics of the processes as well as sampling variability. There are alternative strategies to transform classical time-courses to kime-surfaces. The spacekime framework facilitates the application of advanced topological methods, such as persistent homology, to these kime-surfaces. Topological kime-surface analysis involves studying the topological features of kime-surfaces, such as connected components, loops, and voids, which remain invariant under continuous deformations. These topological invariants can be used to classify different types of time-varying processes, detect anomalies, and uncover hidden patterns that are not apparent in traditional time-series analysis.
-New AI models can be developed to predict, classify, tesselate, and forecast the behavior of high-dimensional longitudinal data, such as functional magnetic resonance imaging (fMRI), by leveraging complex-time representation of time-varying processes and topological analysis. Kime-surfaces represent mathematically-rich and computationally-tractable data objects that can be interrogated via statistical-learning and artificial intelligence techniques. Spacekime analytics has broad applicability, ranging from personalized medicine to environmental monitoring, and statistical obfuscation of sensitive information.
+: New AI models can be developed to predict, classify, tesselate, and forecast the behavior of high-dimensional longitudinal data, such as functional magnetic resonance imaging (fMRI), by leveraging complex-time representation of time-varying processes and topological analysis. Kime-surfaces represent mathematically-rich and computationally-tractable data objects that can be interrogated via statistical-learning and artificial intelligence techniques. Spacekime analytics has broad applicability, ranging from personalized medicine to environmental monitoring, and statistical obfuscation of sensitive information.