# Applied Interdisciplinary Mathematics (AIM) Seminar

Data Science, Time Complexity, and Spacekime Analytics

The immersion of Big Data in all human experiences presents important challenges of managing, modeling, analyzing, interpreting, and visualizing 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 apps.

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 will 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 spacetime manifold, where a number of interesting mathematical problems arise.

Direct data science applications of spacekime analytics will be demonstrated using simulated data, clinical observations (e.g., UK Biobank), and environmental air quality data.

Joint work with Milen V. Velev (Burgas University, Bulgaria).

Slides, additional materials, and interactive demonstrations are available at: http://myumi.ch/2DP93. Speaker(s): Ivo Dinov (University of Michigan)

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 will 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 spacetime manifold, where a number of interesting mathematical problems arise.

Direct data science applications of spacekime analytics will be demonstrated using simulated data, clinical observations (e.g., UK Biobank), and environmental air quality data.

Joint work with Milen V. Velev (Burgas University, Bulgaria).

Slides, additional materials, and interactive demonstrations are available at: http://myumi.ch/2DP93. Speaker(s): Ivo Dinov (University of Michigan)

Building: | East Hall |
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Event Type: | Workshop / Seminar |

Tags: | Mathematics |

Source: | Happening @ Michigan from Department of Mathematics |