Data Science, Time Complexity and Inferential Uncertainty (TCIU)
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The SOCR Data Science Fundamentals project explores new theoretical representation and analytical strategies to understand large and complex data, time complexity and inferential uncertainty. It utilizes information measures, entropy KL divergence, PDEs, Dirac’s bra-ket operators (〈 , 〉). This fundamentals of data science research project will explore time-complexity and inferential uncertainty in modeling, analysis and interpretation of large, heterogeneous, multi-source, multi-scale, incomplete, incongruent, and longitudinal data.
This work is supported in part by NIH grants P20 NR015331, P50 NS091856, P30 DK089503, P30AG053760, UL1TR002240, and NSF grants 1916425, 1734853, 1636840, 1416953, 0716055 and 1023115. Students, trainees, scholars, and researchers from SOCR, BDDS, MNORC, MIDAS, MADC, MICHR, and the broad R-statistical computing community have contributed ideas, code, and support.