Update about.md

Author: anujkarpatne

_pages/about.md

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 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/).
 
-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.
+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 foundations 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.
 
 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.