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Department of Applied Mathematics and Theoretical Physics

Career

  • 2024-present: Research Associate, DAMTP, Univeristy of Cambridge
  • 2018-2023: PhD student, DAMTP, University of Cambridge
  • 2015-2018: M.Sc. in Mathematics, University of Münster, Germany
  • 2012-2015: B.Sc. in Mathematics, University of Münster, Germany

Research

Tamara is a member of the Department of Applied Mathematics and Theoretical Physics as part of the Cambridge Image Analysis research group and the Cantab Capital Institute for the Mathematics of Information. Her current research interests include inverse problems, image processing, non-linear spectral decomposition and deep learning. She is supervised by Prof. Carola-Bibiane Schönlieb.

She has published work and wrote her PhD thesis on deep learning approaches for PDE-based image analysis (e.g. spectral TV decomposition and TV flow) and applied her work to cultural heritage applications. She additionally published a paper on the comparison of the finite element method and physics-informed neural networks for the solution approximation of PDEs.

Find the video on her paper "Deeply Learned Spectral Total Variation Decomposition" here.

Additionally, Tamara is co-founder and co-organiser of Her Maths Story, a platform that promotes women in mathematics and beyond by regularly sharing the stories of women mathematicians.

Publications

Deep Learning Approaches for PDE-based Image Analysis and Beyond: From the Total Variation Flow to Medieval Paper Analysis
T Großmann
(2023)
Extracting chain lines and laid lines from digital images of medieval paper using spectral total variation decomposition
T Großmann, C-B Schönlieb, O Da Rold
– Heritage science
(2023)
11,
180
Hidden Knowledge: Mathematical Methods for the Extraction of the Fingerprint of Medieval Paper from Digital Images
TG Grossmann, C-B Schönlieb, OD Rold
(2023)
Can Physics-Informed Neural Networks beat the Finite Element Method?
TG Grossmann, UJ Komorowska, J Latz, C-B Schönlieb
(2023)
Unsupervised Learning of the Total Variation Flow
TG Grossmann, S Dittmer, Y Korolev, C-B Schönlieb
(2022)
Deeply learned spectral total variation decomposition
TG Grossmann, Y Korolev, G Gilboa, CB Schönlieb
– Advances in Neural Information Processing Systems
(2020)
2020-December,

Research Group

Cambridge Image Analysis

Room

FL.04

Telephone

01223 763139