SOCR News APS GDS 2024 - Revision history

Dinov: /* Overview */

2024-02-18T21:15:34Z

‎Overview

Dinov at 21:06, 18 February 2024

2024-02-18T21:06:16Z

Dinov at 14:14, 16 February 2024

2024-02-16T14:14:03Z

Dinov: /* Session Logistics */

2024-02-15T21:47:52Z

‎Session Logistics

Dinov: /* Invited Special Session Organizer */

2023-12-23T20:53:50Z

‎Invited Special Session Organizer

Dinov at 20:53, 23 December 2023

2023-12-23T20:53:20Z

Dinov: /* Speakers, Titles, and Abstracts */

2023-12-23T20:11:45Z

‎Speakers, Titles, and Abstracts

Dinov: /* Logistics */

2023-12-23T19:56:51Z

‎Logistics

Dinov: /* Logistics */

2023-12-22T20:01:37Z

‎Logistics

Dinov: /* Logistics */

2023-12-22T20:01:14Z

‎Logistics

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 : There are significant recent advances linking model-based statistical inference, quantum physics, computable data science, and model-free artificial intelligence (AI). Interlacing research from these areas has significant potential to lead to novel techniques for understanding and interpreting the physical world. Statistical physics methods model the behavior and interactions of large systems of objects and are applicable far beyond elementary particles. Computational data science algorithms facilitate the pre- and post-processing and analysis of heterogeneous types of information, including structured and unstructured, static and longitudinal, high- and low-dimensional, and complete and missing datasets. Model-free machine learning and AI techniques provide complementary approaches for exploratory, hypotheses-generating, data-mining, clustering, and predictive analytics based on few a priori assumptions. This session will bring a diverse pool of experts to discuss the theoretical challenges, empirical evidence, and potential opportunities of combining transdisciplinary methods to create meta ensemble techniques for holistic comprehensive understanding of complex systems.
-[[Image:APS_March_2024_Meeting_Session_Speakers.png|500px|thumbnail|center| [https://meetings.aps.org/Meeting/MAR24/Session/W56 2024 APS March GDS Invited Session] ]]
+[[Image:APS_March_2024_Meeting_Session_Speakers_r1.png|500px|thumbnail|center| [https://meetings.aps.org/Meeting/MAR24/Session/W56 2024 APS March GDS Invited Session] ]]
 == Invited Special Session Organizer & Chair==

@@ Line 40: / Line 40: @@
 | 3:36PM - 4:06PM || [https://meetings.aps.org/Meeting/MAR24/Session/W56.2 W56.00002: Energy Frontier Exploration using Particle Physics and AI] || [https://physics.illinois.edu/people/directory/profile/msn Mark S Neubauer]
 |-
-| 4:12PM - 4:42PM || [https://meetings.aps.org/Meeting/MAR24/Session/W56.3 W56.00003: Physics and Constrained Optimization Processes Data-driven medical image formation without a priori models] || [https://bioengineering.illinois.edu/people/mfi Michael Insana]
+| 4:12PM - 4:42PM || [https://meetings.aps.org/Meeting/MAR24/Session/W56.3 W56.00003: Data-driven medical image formation without a priori models] || [https://bioengineering.illinois.edu/people/mfi Michael Insana]
 |-
 | 4:48PM - 5:24PM || [https://meetings.aps.org/Meeting/MAR24/Session/W56.4 W56.00004: The Restricted Boltzmann Machine: from the statistical physics of disordered systems to a practical and interpretative generative machine learning] || [https://www.lri.fr/~adecelle/site/ Aurélien Decelle]
@@ Line 87: / Line 87: @@
 *Acknowledgements: We acknowledge support through the NSF cooperative agreement OAC-2117997 and the DOE Office of Science, Office of High Energy Physics, under Contract No. DE-SC0023365.
-===Abstract: W56.00003 : Physics and Constrained Optimization Processes Data-driven medical image formation without a priori models===
+===Abstract: W56.00003 : Data-driven medical image formation without a priori models===
 * Time: 4:12 PM–4:48 PM

← Older revision		Revision as of 14:14, 16 February 2024
Line 8:		Line 8:
	: There are significant recent advances linking model-based statistical inference, quantum physics, computable data science, and model-free artificial intelligence (AI). Interlacing research from these areas has significant potential to lead to novel techniques for understanding and interpreting the physical world. Statistical physics methods model the behavior and interactions of large systems of objects and are applicable far beyond elementary particles. Computational data science algorithms facilitate the pre- and post-processing and analysis of heterogeneous types of information, including structured and unstructured, static and longitudinal, high- and low-dimensional, and complete and missing datasets. Model-free machine learning and AI techniques provide complementary approaches for exploratory, hypotheses-generating, data-mining, clustering, and predictive analytics based on few a priori assumptions. This session will bring a diverse pool of experts to discuss the theoretical challenges, empirical evidence, and potential opportunities of combining transdisciplinary methods to create meta ensemble techniques for holistic comprehensive understanding of complex systems.		: There are significant recent advances linking model-based statistical inference, quantum physics, computable data science, and model-free artificial intelligence (AI). Interlacing research from these areas has significant potential to lead to novel techniques for understanding and interpreting the physical world. Statistical physics methods model the behavior and interactions of large systems of objects and are applicable far beyond elementary particles. Computational data science algorithms facilitate the pre- and post-processing and analysis of heterogeneous types of information, including structured and unstructured, static and longitudinal, high- and low-dimensional, and complete and missing datasets. Model-free machine learning and AI techniques provide complementary approaches for exploratory, hypotheses-generating, data-mining, clustering, and predictive analytics based on few a priori assumptions. This session will bring a diverse pool of experts to discuss the theoretical challenges, empirical evidence, and potential opportunities of combining transdisciplinary methods to create meta ensemble techniques for holistic comprehensive understanding of complex systems.

		+	[[Image:APS_March_2024_Meeting_Session_Speakers.png\|500px\|thumbnail\|center\| [https://meetings.aps.org/Meeting/MAR24/Session/W56 2024 APS March GDS Invited Session] ]]

	== Invited Special Session Organizer & Chair==		== Invited Special Session Organizer & Chair==

@@ Line 19: / Line 19: @@
 * '''Conference''': [https://march.aps.org/about March 2024 APS Annual Conference].
 * [https://march.aps.org/attendees-presenters/abstracts '''Abstract Submission''']
-* '''Session''':  Invited presentations - [https://meetings.aps.org/Meeting/MAR24/Session/W56 Session W56 Model-based Statistical Physics, Computable Data, and Model-Free Artificial Intelligence]
+* '''Session''':  Invited presentations - [https://meetings.aps.org/Meeting/MAR24/Session/W56 Session W56 Model-based Statistical Physics, Computable Data, and Model-Free Artificial Intelligence] and [https://march.aps.org/sessions?q=w56 W56 Session Summary]
 * [https://meetings.aps.org/Meeting/MAR24/ChairIndex APS March 2024 Invited Sessions]

← Older revision		Revision as of 20:53, 23 December 2023
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−	== Invited Special Session Organizer==	+	== Invited Special Session Organizer & Chair==
	* [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].

← Older revision		Revision as of 20:53, 23 December 2023
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	== [[SOCR_News \| SOCR News & Events]]: '''March 2024 APS Meeting''': ''Model-based Statistical Physics, Computable Data, and Model-Free Artificial Intelligence'' ==		== [[SOCR_News \| SOCR News & Events]]: '''March 2024 APS Meeting''': ''Model-based Statistical Physics, Computable Data, and Model-Free Artificial Intelligence'' ==

		+	==Annual APS March Meeting==
		+
		+	The [https://meetings.aps.org annual American Physical Society's March Meetings] are scientific research conferences convening over 13,000 physicists and students from around the globe to connect and collaborate across academia, industry, and major labs. The 2024 APS March meeting will celebrate the 125th anniversary of APS.

	==Overview==		==Overview==

@@ Line 43: / Line 43: @@
 </center>
-==Speakers, Titles, and Abstracts==
+==Abstracts==
-* ... coming up later ...
+===Abstract W56.00001: On the use of physics in machine learning for imaging and quantifying complex processes===
 ==Resources==

@@ Line 32: / Line 32: @@
 ! Time || Abstract || Invited Speaker
 |-
-| 3:00PM - 3:30PM || W56.00001: On the use of physics in machine learning for imaging and quantifying complex processes || George Barbastathis
+| 3:00PM - 3:30PM || [https://meetings.aps.org/Meeting/MAR24/Session/W56.1 W56.00001: On the use of physics in machine learning for imaging and quantifying complex processes] || [https://meche.mit.edu/people/faculty/gbarb@mit.edu George Barbastathis]
 |-
-| 3:36PM - 4:12PM || W56.00002: Energy Frontier Exploration using Particle Physics and AI || Mark S Neubauer
+| 3:36PM - 4:06PM || [https://meetings.aps.org/Meeting/MAR24/Session/W56.2 W56.00002: Energy Frontier Exploration using Particle Physics and AI] || [https://physics.illinois.edu/people/directory/profile/msn Mark S Neubauer]
 |-
-| 4:12PM - 4:48PM || W56.00003: Physics and Constrained Optimization Processes Data-driven medical image formation without a priori models || Michael Insana
+| 4:12PM - 4:42PM || [https://meetings.aps.org/Meeting/MAR24/Session/W56.3 W56.00003: Physics and Constrained Optimization Processes Data-driven medical image formation without a priori models] || [https://bioengineering.illinois.edu/people/mfi Michael Insana]
 |-
-| 4:48PM - 5:24PM || W56.00004: The Restricted Boltzmann Machine: from the statistical physics of disordered systems to a practical and interpretative generative machine learning || Aurélien Decelle
+| 4:48PM - 5:24PM || [https://meetings.aps.org/Meeting/MAR24/Session/W56.4 W56.00004: The Restricted Boltzmann Machine: from the statistical physics of disordered systems to a practical and interpretative generative machine learning] || [https://www.lri.fr/~adecelle/site/ Aurélien Decelle]
 |}

@@ Line 27: / Line 27: @@
 * Date/Time: Thursday, March 7, 2024; 3:00PM - 6:00PM
 <center>
 {| class="wikitable"