Dr. Pascal Fernsel

Pascal Fernsel

Dr. Pascal Fernsel

Research associate

Team Inverse Probleme und Magnetic Particle Imaging
Coordinator of the Research Training Group π³

Bibliothekstraße 5
28359 Bremen

Office: MZH 2290
Phone: +49 421 218-63814
E-Mail: p.fernsel[at]uni-bremen.de

Research Areas

  • Inverse Problems
  • Deep Learning
  • Matrix factorizations for Machine Learning
  • Mass Spectrometry Imaging

Projects

  • DELETO - Machine learning in correlative MR and high-throughput NanoCT
  • KIDOHE - AI-supported documentation for midwives

Theses

Bachelor Theses

  • Hauptkomponentenanalyse zur Untersuchung seismologischer Daten der Neumayer-Station III, Bachelorarbeit, Ribana Werner, 2022

Master Theses

  • Äquivalenz orthogonaler NMF und K-Means, Jan Hochmann, 2018
  • Quantisierung mit geringer Auflösung für Kanäle mit Gedächtnis, Lukas Henneke, 2019

 

Journal Articles

S. Arridge, P. Fernsel, A. Hauptmann.
Joint Reconstruction and Low-Rank Decomposition for Dynamic Inverse Problems.
Inverse Problems and Imaging, 16(3): 483-523, 2022.
DOI: 10.3934/ipi.2021059

P. Fernsel.
Spatially Coherent Clustering Based on Orthogonal Nonnegative Matrix Factorization.
MDPI Journal of Imaging, 7(10), 2021.
DOI: 10.3390/jimaging7100194

J. Leuschner, M. Schmidt, P. Fernsel, D. Lachmund, T. Boskamp, P. Maaß.
Supervised Non-negative Matrix Factorization Methods for MALDI Imaging Applications.
Bioinformatics, bty909 , 2018.
DOI: 10.1093/bioinformatics/bty909

P. Fernsel, P. Maaß.
A Survey on Surrogate Approaches to Non-negative Matrix Factorization.
Vietnam Journal of Mathematics, 46(4):987-1021, 2018.
DOI: 10.1007/s10013-018-0315-x

 

Proceedings

P. Fernsel, Željko Kereta, Alexander Denker.
Convergence Properties of Score-Based Models using Graduated Optimisation for Linear Inverse Problems.
Accepted at IEEE International Workshop on Machine Learning for Signal Processing (2024)
arXiv Link: https://arxiv.org/abs/2404.18699

 

Preprints

P. Fernsel, P. Maaß.
Regularized Orthogonal Nonnegative Matrix Factorization and K-means Clustering.
2021.
arXiv Link: https://arxiv.org/abs/2112.07641

J. Behrmann, S. Dittmer, P. Fernsel, P. Maaß.
Analysis of Invariance and Robustness via Invertibility of ReLU-Networks.
2018.
arXiv Link: https://arxiv.org/abs/1806.09730

Since 2022Postdoc at the Center for Industrial Mathematics, University of Bremen
2022Dr. rer. nat., Center for Industrial Mathematics, University of Bremen, "Nonnegative Matrix Factorization - Theory, Algorithms and Applications"
2017 - 2022PhD Student, First Cohort in the Research Training Group π³, Center for Industrial Mathematics, University of Bremen
2017Master of Science in Mathematics, University of Bremen, "Nichtnegative Matrixfaktorisierung mit MM-Algorithmen im MALDI-Imaging"

 

Lectures

SoSe 2023Nonlinear Inverse Problems

Exercise Classes

SoSe 2022Mathematical Foundations of Machine Learning
WiSe 2020/2021Nonlinear inverse Problems: Analysis, Applications und Algorithms
SoSe 2019Mathematics 1b for Engineers
WiSe 2017/2018Inverse Problems
Since October 2022Coordinator of the Research Training Group π³