Difference between revisions of "SOCR News APS GPS 2026"
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==Short Course (Tutorial) == | ==Short Course (Tutorial) == | ||
| − | * Event: Complex-Time (Kime) Representation of Spatiotemporal Processes and Spacekime Analytics | + | * Event: [https://summit.aps.org/schedule/unit-short-courses-tutorials/ Complex-Time (Kime) Representation of Spatiotemporal Processes and Spacekime Analytics] |
| − | * Date/Time: March 15, 2026, Time: 8:00am-12:00pm | + | * Date/Time: Sunday, March 15, 2026, Time: 8:00am-12:00pm |
* Place: [https://denverconvention.com/ Colorado Convention Center, Room 702] | * Place: [https://denverconvention.com/ Colorado Convention Center, Room 702] | ||
| + | * Price: | ||
| + | |||
| + | : Students: $10 | ||
| + | : Postdocs/fellows: $25 | ||
| + | : Academic faculty: $50 | ||
| + | : Federal Employees: $50 | ||
| + | : Industry: $75 | ||
| + | : International Participants: $30 | ||
| + | |||
| + | * ''Who should attend?'' This short course may be appropriate for undergraduate seniors, graduate students, postdocs and fellows, research scientists, and early career faculty interested in mathematical representation, computational modeling, statistical inference, and AI. While all content will be broadly accessible, a background in mathematical physics and a strong interest in advanced statistical learning methods and AI applications will be beneficial. Participants motivated to explore the intersection of statistical physics, computing, and AI will gain the most from learning spacekime analytics and completing this course. Additionally, some knowledge of statistical limit theory, functional analysis, and experience with R/Rmd programming may be helpful. | ||
| + | |||
| + | * ''Organizer'': [https://www.socr.umich.edu/people/dinov/ Ivo D. Dinov] | ||
| + | |||
| + | * ''Description'': Fundamentally new representations of complex systems are critical in advancing scientific discovery, transdisciplinary research, and blended data-driven human-machine intelligence. This PEP course will present the fundamentals of complex-time (kime) representation, which interfaces quantum mechanics, mathematical statistics, and AI applications. Specifically, the course will demonstrate computational inference, scientific visualization, rigorous statistical modeling of large datasets, understanding complex temporally dynamic processes, and spacekime 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. | ||
== Sessions === | == Sessions === | ||
Revision as of 16:36, 13 December 2025
Contents
SOCR News & Events: 2026 APS Global Physics Summit (GPS)
2026 APS Global Physics Summit
The annual American Physical Society's March Meetings are scientific research conferences convening over 14,000 physicists and students from around the globe to connect and collaborate across academia, industry, and major labs. The 2026 APS Global Physics Summit will take place at Colorado Convention Center, Denver Colorado. The APS 2026 Summit Program is here.
The SOCR group is organizing a Time-Complexity and Inferential Uncertainty (TCIU), Spacekime Analytics, short course, organize sessions, and present invited talks.
Short Course (Tutorial)
- Event: Complex-Time (Kime) Representation of Spatiotemporal Processes and Spacekime Analytics
- Date/Time: Sunday, March 15, 2026, Time: 8:00am-12:00pm
- Place: Colorado Convention Center, Room 702
- Price:
- Students: $10
- Postdocs/fellows: $25
- Academic faculty: $50
- Federal Employees: $50
- Industry: $75
- International Participants: $30
- Who should attend? This short course may be appropriate for undergraduate seniors, graduate students, postdocs and fellows, research scientists, and early career faculty interested in mathematical representation, computational modeling, statistical inference, and AI. While all content will be broadly accessible, a background in mathematical physics and a strong interest in advanced statistical learning methods and AI applications will be beneficial. Participants motivated to explore the intersection of statistical physics, computing, and AI will gain the most from learning spacekime analytics and completing this course. Additionally, some knowledge of statistical limit theory, functional analysis, and experience with R/Rmd programming may be helpful.
- Organizer: Ivo D. Dinov
- Description: Fundamentally new representations of complex systems are critical in advancing scientific discovery, transdisciplinary research, and blended data-driven human-machine intelligence. This PEP course will present the fundamentals of complex-time (kime) representation, which interfaces quantum mechanics, mathematical statistics, and AI applications. Specifically, the course will demonstrate computational inference, scientific visualization, rigorous statistical modeling of large datasets, understanding complex temporally dynamic processes, and spacekime 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.
Sessions =
...tbc...
Talks
...tbc...
Resources
- Dinov's Numerical and Analytical Complex-time Transformations of Longitudinal Processes and Spacekime Analytics slides
- Shen's Probabilistic Symmetry, Variable Exchangeability, and Deep Network Learning Invariance and Equivariance slides
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