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J.M. Suk MSc (Julian)

PhD Candidate

About Me

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).

Open master thesis project: I am currently looking for a student to work on the interface of neural ODEs, optical flow and physics-informed neural networks (PINN) for blood flow in arteries. If you like programming (Python, JAX), deep learning and PDEs feel free to reach out to me!

Organisations

Publications

Recent
Suk, J. , Brune, C. , & Wolterink, J. M. (2023). SE(3) Symmetry Lets Graph Neural Networks Learn Arterial Velocity Estimation from Small Datasets. In O. Bernard, P. Clarysse, N. Duchateau, J. Ohayon, & M. Viallon (Eds.), Functional Imaging and Modeling of the Heart: 12th International Conference, FIMH 2023, Lyon, France, June 19–22, 2023, Proceedings (pp. 445-454) https://doi.org/10.1007/978-3-031-35302-4_46
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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Courses Academic Year  2023/2024

Courses in the current academic year are added at the moment they are finalised in the Osiris system. Therefore it is possible that the list is not yet complete for the whole academic year.
 

Courses Academic Year  2022/2023

Contact Details

 
 

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

Organisations

University of Twente Drienerlolaan 5
7522 NB Enschede
+31 (0)53 489 9111
info@utwente.nl
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