-Hello everyone! I am an Associate Professor in the Department at Computer Science at Virginia Tech (VT). My research strives to push the capabilities of current standards of machine learning (ML) in solving scientific and societally relevant problems by developing novel methodologies in the emerging field of scientific Knowledge-guided Machine Learning (KGML). KGML seeks a distinct departure from “data-only” and “science-only” methods by using both scientific knowledge and data in the design and learning of ML models. The key motivation behind KGML is to improve the interpretability and generalization power of ML models, especially on out-of-sample distributions and even in the paucity of gold-standard data. KGML is also referred to by various names in different research communities, including ‘theory-guided data science’ (TGDS), ‘physics-guided machine learning’ (PGML), ‘science-guided machine learning’ (SGML), and ‘physics-informed machine learning’ (PIML). I enjoy working on inter-disciplinary problems and have been fortunate to work with amazing collaborators from a diverse range of scientific disciplines including lake modeling, remote sensing, geophysics, fluid dynamics, quantum mechanics, optics, radar physics, mechanobiology, and trait-based biology. If you are interested in collaborating, please feel free to contact me!
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