SOCR News MICDE Seminar 2021
Contents
SOCR News & Events: Data Science, Time Complexity, and Spacekime Analytics
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Logistics
- MICDE Seminar-Series
- Dates/Times: Thursday, February 4, 2021 at 11:00am (EST)
- Place: Zoom Event
- Title: Data Science, Time Complexity, and Spacekime Analytics
- Presenter: Ivo Dinov, joint work with Milen V. Velev (Burgas University)
- Abstract:
- Many observable processes demand managing, harmonizing, modeling, analyzing, interpreting, and visualizing of large and complex information. There is a substantial need to develop, validate, productize, and support novel mathematical techniques, advanced statistical computing algorithms, transdisciplinary tools, and effective artificial intelligence applications. Spacekime analytics is a new technique for modeling high-dimensional longitudinal data. This approach relies on extending the notions of time, events, particles, and wavefunctions to complex-time (kime), complex-events (kevents), data, and inference-functions. We will illustrate how the kime-magnitude (longitudinal time order) and kime-direction (phase) affect the subsequent predictive analytics and the induced scientific inference.
- The mathematical foundation of spacekime calculus reveal various statistical implications including inferential uncertainty and a Bayesian formulation of spacekime analytics. Complexifying time allows the lifting of all commonly observed processes from the classical 4D Minkowski spacetime to a 5D spacekime manifold, where a number of interesting mathematical problems arise. Direct data science applications of spacekime analytics will be demonstrated using simulated data and clinical observations (e.g., structural and functional MRI).
- Slidedeck: Presentation Slides.
- Video: Recorded Video.
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References
- This work is sponsored in part by NIH Grants P30 DK089503, P20 NR015331, R01CA233487, and R01MH121079, as well as, NSF Grants 1916425, 1734853 and 1636840.
- Dinov, ID and Velev, MV (2021) Data Science: Time Complexity, Inferential Uncertainty, and Spacekime Analytics, De Gruyter (STEM Series), Berlin/Boston, ISBN 9783110697803 / 3110697807.
- SOCR Home page: https://www.socr.umich.edu
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