About

I am a final-year PhD student at the working group for Statistical Learning and Data Science at LMU Munich in cooperation with the Fraunhofer ADA Lovelace Center where I am involved in application research across different domains. I am researching on methods for Learning with Limited Labeled Data (L3D) including but not limited to Semi-supervised-/ Self-supervised- /Weakly-supervised Learning, currently focusing on Constrained Clustering and Positive Unlabeled Learning. I am generally interested in all things low supervision including weak/ noisy/ inconsistent data annotations, model-based labeling error detection and the intelligent design of data annotation pipelines.

I hold a Master's Degree in Statistics from LMU and a Bachelor's Degree in quantitative Economics from Heidelberg University. Find more information about me on my personal website.

Contact

Institut für Statistik

Ludwig-Maximilians-Universität München

Ludwigstraße 33

D-80539 München

Room 139

Phone: +49 911 58061 9595

jann.goschenhofer [at] stat.uni-muenchen.de

Research Interests

• Semi-supervised learning
• Weakly-supervised Learning
• Positive Unlabeled Learning
• Learning from Constraints
• Time Series Problems
• Manifold Learning

You Can Find me on

• Personal homepage
• GitHub
• ResearchGate
• Google Scholar
• ADA Lovelace Center

References

1. Goschenhofer J, Ragupathy P, Heumann C, Bischl B, Aßenmacher M (2022) CC-Top: Constrained Clustering for Dynamic Topic Discovery. Workshop on Ever Evolving NLP (EvoNLP).
   link|pdf.
2. Dexl J, Benz M, Kuritcyn P, Wittenberg T, Bruns V, Geppert C, Hartmann A, Bischl B, Goschenhofer J (2022) Robust Colon Tissue Cartography with Semi-Supervision. Current Directions in Biomedical Engineering 8, 344–347.
   link|pdf.
3. Rueger S, Goschenhofer J, Nath A, Firsching M, Ennen A, Bischl B (2022) Deep-Learning-based Aluminum Sorting on Dual Energy X-Ray Transmission Data. Sensor-based Sorting and Control.
   link|pdf.
4. Kaminwar SR, Goschenhofer J, Thomas J, Thon I, Bischl B (2021) Structured Verification of Machine Learning Models in Industrial Settings. Big Data.
   link|pdf.
5. Goschenhofer J, Hvingelby R, Rügamer D, Thomas J, Wagner M, Bischl B (2021) Deep Semi-Supervised Learning for Time Series Classification 20th IEEE International Conference on Machine Learning and Applications (ICMLA),
   link|pdf.
6. Dorigatti E, Goschenhofer J, Schubert B, Rezaei M, Bischl B (2021) Positive-Unlabeled Learning with Uncertainty-aware Pseudo-label Selection. arXiv preprint arXiv:2109.05232.
   link|pdf.
7. Pfister FMJ, Um TT, Pichler DC, Goschenhofer J, Abedinpour K, Lang M, Endo S, Ceballos-Baumann AO, Hirche S, Bischl B, others (2020) High-Resolution Motor State Detection in Parkinson’s Disease Using convolutional neural networks. Scientific reports 10, 1–11.
   link.
8. Goschenhofer J, Pfister FMJ, Yuksel KA, Bischl B, Fietzek U, Thomas J (2019) Wearable-based Parkinson’s Disease Severity Monitoring using Deep Learning. Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 400–415.
   link.