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Tess Smidt is an Associate Professor in the Department of Electrical Engineering and Computer Science at Massachusetts Institute of Technology (MIT). She specializes in machine learning with a focus on first-principles scientific data and symmetry equivariant neural networks. Her research explores representation learning for 3D geometry and the use of machine learning surrogates in computational science, particularly in the prediction of design properties of atomic systems, including molecules, materials, and proteins. Tess's work integrates advanced machine learning techniques with scientific computation to drive innovations in various scientific disciplines.