The Enigmatic Kime: Time Complexity in Data Science

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Friday, September 21, 2018 - 4:00pm


Weiser Hall, Low Rise, Room 182, 500 Church Street 


UMSN's Dr. Ivo Dinov will be the guest presenter for the Michigan Institute for Data Science's (MIDAS) seminar series.  

Abstract from MIDAS: We will provide a constructive definition of “Big Biomedical/Health Data” and provide examples of the challenges, algorithms, processes, and tools necessary to manage, aggregate, harmonize, process, and interpret such data. In data science, time complexity frequently manifests as sampling incongruency, heterogeneous scales, and intricate dependencies. We will present the concept of 2D complex-time (kime) and illustrate how the kime-order (time) and kime-direction (phase) affect advanced predictive analytics and scientific inference based on Big Biomedical Data. Kime-representation solves the unidirectional arrows of time problems, e.g., psychological arrow of time reflects the irrevocable past to future flow and thermodynamic arrow of time reflecting the relentless growth of entropy. Albeit kime-phase angles may not always be directly observable, we will illustrate how they can be estimated and used to improve the resulting space-kime modeling, trend forecasting, and predictive data analytics. Simulated data, clinical observations (e.g., neurodegenerative disorders), and multisource census-like datasets (e.g., UK Biobank) will be used to demonstrate time-complexity and inferential-uncertainty.