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Anuj Karpatne
Researcher at Virginia Tech
Publications - 101
Citations - 4044
Anuj Karpatne is an academic researcher from Virginia Tech. The author has contributed to research in topics: Computer science & Artificial neural network. The author has an hindex of 21, co-authored 79 publications receiving 2304 citations. Previous affiliations of Anuj Karpatne include University of Minnesota & Indian Institutes of Technology.
Papers
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Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data
Anuj Karpatne,Gowtham Atluri,James H. Faghmous,Michael Steinbach,Arindam Banerjee,Auroop R. Ganguly,Shashi Shekhar,Nagiza F. Samatova,Vipin Kumar +8 more
TL;DR: The paradigm of theory-guided data science is formally conceptualized and a taxonomy of research themes in TGDS is presented and several approaches for integrating domain knowledge in different research themes are described using illustrative examples from different disciplines.
Journal ArticleDOI
Theory-guided Data Science: A New Paradigm for Scientific Discovery from Data
Anuj Karpatne,Gowtham Atluri,James H. Faghmous,Michael Steinbach,Arindam Banerjee,Auroop R. Ganguly,Shashi Shekhar,Nagiza F. Samatova,Vipin Kumar +8 more
TL;DR: The theory-guided data science (TGDS) paradigm as mentioned in this paper 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.
Posted Content
Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modeling
TL;DR: A novel framework, termed as physics-guided neural network (PGNN), leverages the output of physics-based model simulations along with observational features to generate predictions using a neural network architecture to ensure better generalizability as well as physical consistency of results.
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Machine Learning for the Geosciences: Challenges and Opportunities
TL;DR: Some of the cross-cutting research themes in machine learning that are applicable across several geoscience problems, and the importance of a deep collaboration between machine learning and geosciences for synergistic advancements in both disciplines are discussed.
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Spatio-Temporal Data Mining: A Survey of Problems and Methods
TL;DR: A broad survey of this relatively young field of spatio-temporal data mining is presented, and literature is classified into six major categories: clustering, predictive learning, change detection, frequent pattern mining, anomaly detection, and relationship mining.