Deploying to gh-pages from @ ae38b9e 🚀

Author: sajeedmehrab

404.html

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+<div style="background-image: url('/assets/img/lab_logo.png'); background-size: 100% 100%; background-position: center; background-repeat: no-repeat; padding: 20px;">
+---
+layout: about
+title: About
+permalink: /
+# subtitle: 
+
+# profile: 
+#  align: right
+#  image: AK_VT_pic2.png
+#  image_circular: false
+#  more_info: >
+#    <b>Anuj Karpatne</b>
+#    <p>Associate Professor</p>
+#    <p>Dept. of Computer Science, Virginia Tech</p>
+#    <p>Email: karpatne-AT-vt.edu</p>
+#    <p>Office: D&DS 438</p>
+    
+news: true # includes a list of news items
+# selected_papers: true # includes a list of papers marked as "selected={true}"
+social: true # includes social icons at the bottom of the page
+---
+
+<p align="center">
+  <img src="/assets/img/lab_logo.png" alt="Lab Logo" style="width: 100%;">
+</p>
+
+**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](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.
+
+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).
+
+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!
+
+</div>

_pages/about.md

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