SOCR News MIDAS Symposium 2020

SOCR News & Events: Computational Neuroscience, Time Complexity, and Spacekime Analytics

SOCR Resource

The 2020 UM Data Science Annual Symposium will take place virtually.

Logistics

• Program
• Dates/Times: Tuesday and Wednesday November 10-11, 2020
• Place: Zoom Event TBD ...
• Title: Computational Neuroscience, Time Complexity, and Spacekime Analytics
• Presenter: Ivo Dinov, joint work with Milen V. Velev (Burgas University)

Spacekime/TCIU Project

• Abstract: The proliferation of digital information in all human experiences presents difficult challenges and offers unique opportunities of managing, modeling, analyzing, interpreting, and visualizing heterogeneous data. 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, such as functional magnetic resonance imaging (fMRI). This approach relies on extending the notions of time, events, particles, and wavefunctions to complex-time (kime), complex-events (kevents), data and inference-functions, respectively. This talk 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 reveals 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.

Spacekime analytics transforms time-varying data, such as time-series observations, into higher-dimensional manifolds representing complex-valued and kime-indexed surfaces (kime-surfaces). This process uncovers some of the intricate structure in high-dimensional data that may be intractable in the classical space-time representation of the data. In addition, the spacekime representation facilitates the development of innovative data science analytical methods for model-based and model-free scientific inference, derived computed phenotyping, and statistical forecasting. Direct neuroscience science applications of spacekime analytics will be demonstrated using simulated data and clinical observations (e.g., UK Biobank).

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

• Slidedeck: Presentation Slides

Background

• SOCR News & Events
• SOCR Global Users
• SOCR Navigators
• SOCR Datasets and Challenging Case-studies
• Electronic Textbooks:
  • Probability and Statistics EBook
  • Scientific Methods for Health Sciences
  • Data Science and Predictive Analytics

• Spacekime website
• Time-Complexity and Inferential Uncertainty (TCIU)

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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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