Short Course: Complex-Time (Kime) Representation of Spatiotemporal Processes and Spacekime Analytics

ORAL · MAR-SH03 · ID: MAR-SH03

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.

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