Tess Smidt
Assistant Professor of Electrical Engineering and Computer Science
Research Interests
Machine learning from first-principles for scientific data. Symmetry equivariant neural networks. Representation learning for 3D geometry. Computational science and ML surrogate models. Design and property prediction of atomic systems (e.g. molecules, materials, proteins, etc.).