Dr. Sarah J. Hamilton

Dr. Sarah J. HamiltonMarquette University

Cudahy Hall, 355

MilwaukeeWI53201United States of America(414) 288-6343sarah.hamilton@marquette.eduPersonal Website

Associate Professor, Graduate Chair

Department of Mathematical and Statistical Sciences

Courses Taught

• Differential Equations
• Partial Differential Equations
• Applied Mathematical Analysis

Research Interests

• Inverse problems and nonlinear solution methods
• Applying deep learning methods to computational imaging
• Algorithm development for medical imaging, in particular Electrical Impedance Tomography

Publications

• S.J. Hamilton, P. Muller, V. Kolehmainen, and J. Toivanen. Fast 3D Partial Boundary Data EIT Reconstruction using Direct Inversion CGO-based Methods. IEEE Transactions on Biomedical Engineering, early access, online November 2025. DOI: 10.1109/TBME.2025.3633560.
• J. Toivanen, V. Kolehmainen, A. Paldanius, A. Hänninen, A. Hauptmann, S.J. Hamilton. Graph convolutional networks enable fast hemorrhagic stroke monitoring with electrical impedance tomography. IEEE Transactions on Biomedical Engineering, early access, online July 2025. DOI: 10.1109/TBME.2025.3594249.
• W. Herzberg, A. Hauptmann, and S.J. Hamilton. Domain independent post-processing with graph U-nets: applications to electrical impedance tomographic imaging. Physiological Measurement, Nov. 2023. DOI: 10.1088/1361-6579/ad0b3d.
• S.J. Hamilton, P. Muller, D. Isaacson, V. Kolehmainen, O. Rajabi Shishvan, G. Saulnier, and J. Toivanen. Fast absolute 3D CGO-based electrical impedance tomography on experimental tank data. Physiological Measurement, 43(12400), 2022. DOI: 10.1088/1361-6579/aca26b.
• W. Herzberg, A. Hauptmann, D. Rowe, and S.J. Hamilton. Graph Convolutional Networks for Model-Based Learning in Nonlinear Inverse Problems. IEEE Transactions on Computational Imaging, vol. 7, pp. 1341-1353, 2021. DOI: 10.1109/TCI.2021.3132190.