Update about.md

Author: anujkarpatne

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-**Welcome to our lab!**
+Welcome to the **Knowledge-guided Machine Learning (KGML)** Lab led by [Prof. Anuj Karpatne](https://people.cs.vt.edu/karpatne/) at [Virginia Tech](https://www.vt.edu/) in the [Department of Computer Science](https://cs.vt.edu/) and the [Sanghani Center for AI and Data Analytics](https://sanghani.cs.vt.edu/).
 
-We focus on advancing the field of machine learning (ML) to tackle complex scientific and socially relevant challenges. Led by [Prof. Anuj Karpatne](https://people.cs.vt.edu/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.
+A central focus of our lab is to advance the emerging field of KGML where scientific knowledge is deeply integrated in the design and training of ML models to produce `scientifically grounded`, `explainable`, and `generalizable` results, going beyond *black-box (data-only)* applications of AI/ML in science. Through our inter-disciplinary collaborations with researchers from diverse institutions and disciplinary backgrounds, we aspire to contribute on two fundamental fronts: (1) `Advance the frontiers of AI/ML` by incorporating diverse forms of scientific knowledge in AI/ML frameworks including *partial differential equations (PDEs), symbolic rules, ontologies, and mechanistic models*, and (2) `Deliver real-world impacts' to scientific applications of high societal relevance including *aquatic sciences, organismal biology, virology, mechanobiology, fluid dynamics, geophysics, quantum mechanics, and electromagnetism*. We are grateful to NSF for their generous support for our research projects. Check out our [Projects](/projects), [Publications](/publications), and [Team](/people) pages to learn more about us and our work.
 
-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).
+To learn more about the field of KGML, see the [KGML book](https://sites.google.com/vt.edu/kgml-book/) and a recent [perspective article](https://arxiv.org/abs/2403.15989) summarizing current trends and future prospects in this emerging field.
 
-We have a strong focus on interdisciplinary collaboration and have successfully partnered with experts across numerous scientific fields, including lake modeling, remote sensing, geophysics, fluid dynamics, quantum mechanics, optics, radar physics, mechanobiology, and trait-based biology. If you're interested in exploring collaborative opportunities, please reach out to us!
+# 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).
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+# We have a strong focus on interdisciplinary collaboration and have successfully partnered with experts across numerous scientific fields, including lake modeling, remote sensing, geophysics, fluid dynamics, quantum mechanics, optics, radar physics, mechanobiology, and trait-based biology. If you're interested in exploring collaborative opportunities, please reach out to us!
 
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