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title: "Predicting the Mechanics of Living Cells"
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description:
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img: assets/img/memtrack.png
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importance: 1
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category: work
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This is a collaborative project with researchers from Mechanical Engineering at VT to develop physics-guided ML methods for tracking, characterizing, and predicting the movement of cells and bacteria in fibrous environments using traction-force microscopy images collected in the field of mechanobiology. The physics knowledge that we are integrating in our ML methods includes phenomenological models of cell and bacteria migration and knowledge of the mechanical forces governing interactions between cells and fiber backgrounds. This work is supported by a $1M NSF Medium grant where I am the lead investigator.
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### Papers:
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<img src="/assets/img/memtrack.png" alt="Thumbnail" style="max-width: 150px; height: auto;">
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<h2 style="margin: 0; font-size: 16px;">Motion Enhanced Multi-Level Tracker (MEMTrack): A Deep Learning-Based Approach to Microrobot Tracking in Dense and Low-Contrast Environments</h2>
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<p style="margin: 0; font-size: 14px; color: #666;">Medha Sawhney, Bhas Karmarkar, Eric J. Leaman, Arka Daw, Anuj Karpatne, Bahareh Behkam</p>
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<p style="margin: 0; font-size: 12px; color: #666;">Advanced Intelligent Systems, 2024</p>
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<a href="https://onlinelibrary.wiley.com/doi/10.1002/aisy.202300590">Paper</a> |
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<a href="https://github.com/sawhney-medha/MEMTrack" style="text-decoration: none;">Github</a>
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