Difference between revisions of "SOCR News APS GDS 2024"
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! Time || Abstract || Invited Speaker | ! Time || Abstract || Invited Speaker | ||
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| − | | 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: | + | | 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] |
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| − | | 4:12PM - 4: | + | | 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] |
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Revision as of 14:56, 23 December 2023
Contents
SOCR News & Events: March 2024 APS Meeting: Model-based Statistical Physics, Computable Data, and Model-Free Artificial Intelligence
Overview
- 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.
Invited Special Session Organizer
Session Logistics
- Date/Time: Mar 7 2024 3:00PM CT
- Venue: Minneapolis Convention Center, Minneapolis, MN, USA, Room 205AB.
- Registration: Meeting Registration is required.
- Conference: March 2024 APS Annual Conference.
- Abstract Submission
- Session: Invited presentations - Session W56 Model-based Statistical Physics, Computable Data, and Model-Free Artificial Intelligence
- APS March 2024 Invited Sessions
Program
Logistics
- Sponsoring Unit: APS Group on Data Science (GDS)
- Chair: Ivo Dinov, University of Michigan
- Venue: Minneapolis Convention Center, Room: 205AB, 1301 Second Ave S, Minneapolis, MN 55403
- Date/Time: Thursday, March 7, 2024; 3:00PM - 6:00PM
Speakers, Titles, and Abstracts
- ... coming up later ...
Resources
- ... coming up later ...
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