J.M. Suk MSc (Julian)

I am a PhD student from Munich, Germany, working on deep learning for 3D medical data in the form of finite-element meshes and point clouds. I employ graph-convolutional neural networks (GCN) and use symmetry (geometric deep learning) and physics-informed deep learning to boost their performance.

Additionally, I am interested in the mathematical foundations of deep learning through the application of functional analysis and identification of the interplay with dynamical systems and partial differential equations.

I am excited about (super)computers, especially Linux-based systems and clusters. Consequently, I wrote my master's thesis on Hessian-based optimisation of deep neural networks in the context of high performance computing (HPC).

Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), Mathematics of Imaging & AI (MIA)

Suk, J. , Brune, C. , & Wolterink, J. M. (2023). SE(3) symmetry lets graph neural networks learn arterial velocity estimation from small datasets. (pp. 1). ArXiv.org. https://doi.org/10.48550/arXiv.2302.08780

Suk, J., de Haan, P., Lippe, P. , Brune, C. , & Wolterink, J. M. (2022). Mesh Neural Networks for SE(3)-Equivariant Hemodynamics Estimation on the Artery Wall. (pp. 1). ArXiv.org. https://doi.org/10.48550/arXiv.2212.05023

Wiesner, D. , Suk, J. , Dummer, S., Svoboda, D. , & Wolterink, J. M. (2022). Implicit Neural Representations for Generative Modeling of Living Cell Shapes. In L. Wang, Q. Dou, P. T. Fletcher, S. Speidel, & S. Li (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2022: 25th International Conference, Singapore, September 18–22, 2022, Proceedings, Part IV (pp. 58-67). (Lecture Notes in Computer Science; Vol. 13434). Springer. https://doi.org/10.1007/978-3-031-16440-8_6

Suk, J. M., de Haan, P., Lippe, P. , Brune, C. , & Wolterink, J. M. (2022). Mesh convolutional neural networks for wall shear stress estimation in 3D artery models. In E. Puyol Antón, A. Young, A. Suinesiaputra, M. Pop, C. Martín-Isla, M. Sermesant, O. Camara, & K. Lekadir (Eds.), Statistical Atlases and Computational Models of the Heart. Multi-Disease, Multi-View, and Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge: 12th International Workshop, STACOM 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Revised Selected Papers (pp. 93-102) https://doi.org/10.1007/978-3-030-93722-5_11

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j.m.suk@utwente.nl

Visiting Address

University of Twente
Faculty of Electrical Engineering, Mathematics and Computer Science
Zilverling (building no. 11)
Hallenweg 19
7522NH Enschede
The Netherlands

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University of Twente
Faculty of Electrical Engineering, Mathematics and Computer Science
Zilverling (building no. 11), room 2110
Hallenweg 19
7522NH Enschede
The Netherlands

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Mailing Address

University of Twente
Faculty of Electrical Engineering, Mathematics and Computer Science
Zilverling 2110
P.O. Box 217
7500 AE Enschede
The Netherlands

Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), Mathematics of Imaging & AI (MIA)

University of Twente Drienerlolaan 5
7522 NB Enschede

+31 (0)53 489 9111
info@utwente.nl
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PhD Candidate

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Courses Academic Year 2021/2022

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