Tamara Großmann

Career

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.

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

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,