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Author: abhilash-neog

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 layout: about
 title: about
 permalink: /
-subtitle: Knowledge-guided Machine Learning Lab
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+![Image](/assets/img/lab_logo.png)
 Welcome to our lab! We focus on advancing the field of machine learning (ML) to tackle complex scientific and socially relevant challenges. Led by Prof. Anuj Karpatne, our research emphasizes developing innovative methods in the emerging area of scientific Knowledge-guided Machine Learning (KGML). KGML represents a unique approach that combines both scientific knowledge and data in the design and learning of ML models, moving beyond traditional “data-only” or “science-only” methodologies.
 
 Our primary goal is to enhance the interpretability and generalization capabilities of ML models, particularly for out-of-sample distributions and in scenarios with limited high-quality data. KGML is also known in various research communities by other names, such as ‘theory-guided data science’ (TGDS), ‘physics-guided machine learning’ (PGML), ‘science-guided machine learning’ (SGML), and ‘physics-informed machine learning’ (PIML).