Difference between revisions of "SOCR News Biophysics 2020 Spacekime"
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* '''Title''': ''Data Science, Time Complexity, and Spacekime Analytics'' | * '''Title''': ''Data Science, Time Complexity, and Spacekime Analytics'' | ||
* '''Presenter''': [http://umich.edu/~dinov Ivo Dinov], joint work with [http://socr.umich.edu/people/Milen_Velev.html Milen V. Velev] (Burgas University) | * '''Presenter''': [http://umich.edu/~dinov Ivo Dinov], joint work with [http://socr.umich.edu/people/Milen_Velev.html Milen V. Velev] (Burgas University) | ||
| − | [[Image:Spacekime_TCIU_NonTrivial_PolarCylinder_WqaveEqu_Phase_Anim.gif | + | [[Image:Spacekime_TCIU_NonTrivial_PolarCylinder_WqaveEqu_Phase_Anim.gif|right| [https://www.socr.umich.edu/TCIU/ Spacekime/TCIU Project] ]] |
* '''Abstract''': Digital information flows impact all human experiences. The proliferation of large, heterogeneous, and spatio-temporal data requires novel approaches for managing, modeling, analyzing, interpreting, and visualizing complex information. The scientific community is developing, validating, productizing, and supporting novel mathematical techniques, advanced statistical computing algorithms, transdisciplinary tools, and effective artificial intelligence apps. | * '''Abstract''': Digital information flows impact all human experiences. The proliferation of large, heterogeneous, and spatio-temporal data requires novel approaches for managing, modeling, analyzing, interpreting, and visualizing complex information. The scientific community is developing, validating, productizing, and supporting novel mathematical techniques, advanced statistical computing algorithms, transdisciplinary tools, and effective artificial intelligence apps. | ||
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: 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 reveals interesting statistical implications including inferential uncertainty and a Bayesian formulation of spacekime analytics. Complexifying time allows the lifting of all commonly observed processes (e.g., time-series) from the classical 4D Minkowski spacetime to a 5D spacekime manifold (e.g., kime-surfaces), where a number of mathematical problems remain to be solved. | : 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 reveals interesting statistical implications including inferential uncertainty and a Bayesian formulation of spacekime analytics. Complexifying time allows the lifting of all commonly observed processes (e.g., time-series) from the classical 4D Minkowski spacetime to a 5D spacekime manifold (e.g., kime-surfaces), where a number of mathematical problems remain to be solved. | ||
: We will present several direct data science applications of spacekime analytics using simulated data, clinical observations (e.g., UK Biobank), and environmental air quality data. | : We will present several direct data science applications of spacekime analytics using simulated data, clinical observations (e.g., UK Biobank), and environmental air quality data. | ||
| + | * '''Slidedeck''': [https://socr.umich.edu/docs/uploads/2020/Dinov_TCIU_SpaceKime_2020_Biophysics_UM.pdf PDF Slides] | ||
==SOCR Background== | ==SOCR Background== | ||
Latest revision as of 11:36, 22 August 2020
Contents
SOCR News & Events: UM Biophysics Seminar
Logistics
- UM Biophysics Seminar Series
- Date/Times: Friday, September 11, 2020, 12 Noon ET (GMT-4)
- Zoom: TBD
- Title: Data Science, Time Complexity, and Spacekime Analytics
- Presenter: Ivo Dinov, joint work with Milen V. Velev (Burgas University)
- Abstract: Digital information flows impact all human experiences. The proliferation of large, heterogeneous, and spatio-temporal data requires novel approaches for managing, modeling, analyzing, interpreting, and visualizing complex information. The scientific community is developing, validating, productizing, and supporting 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 reveals interesting statistical implications including inferential uncertainty and a Bayesian formulation of spacekime analytics. Complexifying time allows the lifting of all commonly observed processes (e.g., time-series) from the classical 4D Minkowski spacetime to a 5D spacekime manifold (e.g., kime-surfaces), where a number of mathematical problems remain to be solved.
- We will present several direct data science applications of spacekime analytics using simulated data, clinical observations (e.g., UK Biobank), and environmental air quality data.
- Slidedeck: PDF Slides
SOCR Background
- SOCR News & Events
- SOCR Global Users
- SOCR Navigators
- SOCR Datasets and Challenging Case-studies
- Electronic Textbooks:
SOCR Demos
- General SOCR Webapps
- SOCR BrainViewer
- Motion Charts webapp (try it with your own high-dimensional longitudinal data, e.g., Ozone Data)
- Hands-on interactive visualization of extremely high-dimensional data (learning module and webapp)
- Predicting Hospitalization-based Pressure Injuries (webapp)
- Virtual Hospital and Simulated Patient EHR Data (webapp)
References
- This work is supported 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
- www.Spacekime.org
- TCIU website.
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