Theory-guided Data Science – Karpatne & Kumar

Theory-guided Data Science: A new paradigm for scientific discovery

Theory-guided Data Science: A new paradigm for scientific discovery

Theory-guided Data Science

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Anuj Karpatne

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Vipin Kumar

Presentation Slides: Theory-guidedLearningSlides_Katarpne_Kumar_2017

Audio: (link in prep) |Video link

Learn more about this research at:  https://www-users.cs.umn.edu/~karpa009/

Presented at: IS-GEO Monthly Telecon | March 7, 2017

Abstract: Data science models, although successful in a number of commercial domains, have had limited applicability in scientific problems involving complex physical phenomena. Theory-guided data science (TGDS) is an emerging paradigm that aims to leverage the wealth of scientific knowledge for improving the effectiveness of data science models in enabling scientific discovery. The overarching vision of TGDS is to introduce scientific consistency as an essential component for learning generalizable models. Further, by producing scientifically interpretable models, TGDS aims to advance our scientific understanding by discovering novel domain insights. Indeed, the paradigm of TGDS has started to gain prominence in a number of scientific disciplines such as turbulence modeling, material discovery, quantum chemistry, bio-medical science, bio-marker discovery, climate science, and hydrology. In this presentation, we formally conceptualize the paradigm of TGDS and present a taxonomy of research themes in TGDS. We describe several approaches for integrating domain knowledge in different research themes using illustrative examples from different disciplines. We also highlight some of the promising avenues of novel research for realizing the full potential of theory-guided data science. (from Karpatne et al., 2017)

Link for more information:

A. Karpatne, G. Atluri, J. Faghmous, M. Steinbach, A. Banerjee, A. Ganguly, S. Shekhar, N. Samatova, and V. Kumar, Theory-guided Data Science: A New Paradigm for Scientific Discovery from Data, IEEE Transactions on Knowledge and Data Engineering (TKDE), 29(10), 2318–2331, 2017 [arXiv, DOI].