Matthias Aßenmacher

About

I am a postdoctoral researcher at the Chair of Statistical Learning and Data Science (Dept. of Statistics, LMU) and the NFDI Consortium for Business, Economic and Related Data (BERD@NFDI). I obtained my bachelor’s degree in Economics from LMU in 2014, afterwards I turned to Statistics (with a focus on social and economic studies) and obtained my Master’s degree in 2017 (also from LMU). In October 2021 I finished my PhD at the working group Methods for Missing Data, Model Selection and Model Averaging under the supervision of Prof. Dr. Christian Heumann with a focus on Natural Language Processing. Further I am one of the co-founders of the Open Science Initiative in Statistics (OSIS).

Contact

Institut für Statistik

Ludwig-Maximilians-Universität München

Ludwigstraße 33

D-80539 München

matthias [at] stat.uni-muenchen.de

Teaching

Thesis supervision

If you are interested in writing your thesis under my supervision, please send me your field of interest and your ideas, a CV and your current transcript of records. Please consider this information before writing me an e-mail.

A selection of theses I recently supervised (partly together with Christian Heumann) can be found here.

Research Interests

My main research interest lie in the field of Natural Language Processing:

Other topics that interest me:

You Can Find me on

References

  1. Koch P, Nuñez GV, Garces Arias E, Heumann C, Schöffel M, Häberlin A, Aßenmacher M (2023) A tailored Handwritten-Text-Recognition System for Medieval Latin Accepted at First Workshop on Ancient Language Processing (ALP 2023),
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  2. Aßenmacher M, Sauter N, Heumann C (2023) Classifying multilingual party manifestos: Domain transfer across country, time, and genre. arXiv preprint arXiv:2307.16511.
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  3. Aßenmacher M, Rauch L, Goschenhofer J, Stephan A, Bischl B, Roth B, Sick B (2023) Towards Enhancing Deep Active Learning with Weak Supervision and Constrained Clustering Proceedings of the 7th Workshop on Interactive Adaptive Learning, co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2023),
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  4. Urchs S, Thurner V, Aßenmacher M, Heumann C, Thiemichen S (2023) How Prevalent is Gender Bias in ChatGPT? - Exploring German and English ChatGPT Responses Accepted at 1st Workshop on Biased Data in Conversational Agents,
  5. Öztürk IT, Nedelchev R, Heumann C, Garces Arias E, Roger M, Bischl B, Aßenmacher M (2023) How Different Is Stereotypical Bias Across Languages? Accepted at 3rd Workshop on Bias and Fairness in AI,
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  6. Rauch L, Aßenmacher M, Huseljic D, Wirth M, Bischl B, Sick B (2023) ActiveGLAE: A Benchmark for Deep Active Learning with Transformers Accepted at ECML-PKDD 2023,
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  7. Goschenhofer J, Ragupathy P, Heumann C, Bischl B, Aßenmacher M (2022) CC-Top: Constrained Clustering for Dynamic Topic Discovery Proceedings of the The First Workshop on Ever Evolving NLP (EvoNLP), pp. 26–34. Association for Computational Linguistics, Abu Dhabi, United Arab Emirates (Hybrid).
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  8. Lebmeier E, Aßenmacher M, Heumann C (2022) On the current state of reproducibility and reporting of uncertainty for Aspect-based Sentiment Analysis Machine Learning and Knowledge Discovery in Databases (ECML-PKDD), Springer International Publishing, Grenoble, France.
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  9. Aßenmacher M, Dietrich M, Elmaklizi A, Hemauer EM, Wagenknecht N (2022) Whitepaper: New Tools for Old Problems.
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  10. Koch P, Aßenmacher M, Heumann C (2022) Pre-trained language models evaluating themselves - A comparative study Proceedings of the Third Workshop on Insights from Negative Results in NLP, pp. 180–187. Association for Computational Linguistics, Dublin, Ireland.
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  11. Aßenmacher M, Schulze P, Heumann C (2021) Benchmarking down-scaled (not so large) pre-trained language models Proceedings of the 17th Conference on Natural Language Processing (KONVENS 2021), pp. 14–27. KONVENS 2021 Organizers, Düsseldorf, Germany.
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  12. Aßenmacher M, Corvonato A, Heumann C (2021) Re-Evaluating GermEval17 Using German Pre-Trained Language Models Proceedings of the Swiss Text Analytics Conference 2021, CEUR Workshop Proceedings, Winterthur, Switzerland (Online).
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  13. Schulze P, Wiegrebe S, Thurner PW, Heumann C, Aßenmacher M, Wankmüller S (2021) Exploring Topic-Metadata Relationships with the STM: A Bayesian Approach. arXiv preprint arXiv:2104.02496.
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  14. Lebmeier E, Hou N, Spann K, Aßenmacher M (2021) Creating a Customer Centricity Graph from unstructured customer feedback. Applied Marketing Analytics 6, 221–229.
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  15. Meidinger M, Aßenmacher M (2021) A New Benchmark for NLP in Social Sciences: Evaluating the Usefulness of Pre-trained Language Models for Classifying Open-ended Survey Responses Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, pp. 866–873. SciTePress.
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  16. Schiergens TS, Drefs M, Dörsch M, Kühn F, Albertsmeier M, Niess H, Schoenberg MB, Assenmacher M, Küchenhoff H, Thasler WE, others (2021) Prognostic Impact of Pedicle Clamping during Liver Resection for Colorectal Metastases. Cancers 13, 72.
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  17. Guderlei M, Aßenmacher M (2020) Evaluating Unsupervised Representation Learning for Detecting Stances of Fake News Proceedings of the 28th International Conference on Computational Linguistics, pp. 6339–6349. International Committee on Computational Linguistics, Barcelona, Spain (Online).
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  18. Viellieber VD, Aßenmacher M (2020) Pre-trained language models as knowledge bases for Automotive Complaint Analysis. arXiv preprint arXiv:2012.02558.
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  19. Aßenmacher M, Heumann C (2020) On the comparability of pre-trained language models Proceedings of the 5th Swiss Text Analytics Conference and 16th Conference on Natural Language Processing, CEUR Workshop Proceedings, Zurich, Switzerland (Online).
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  20. Aßenmacher M, Kaiser JC, Zaballa I, Gasparrini A, Küchenhoff H (2019) Exposure-lag-response associations between lung cancer mortality and radon exposure in German uranium miners. Radiation and Environmental Biophysics.
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  21. Sint A, Lutz R, Assenmacher M, Küchenhoff H, Kühn F, Faist E, Bazhin AV, Rentsch M, Werner J, Schiergens TS (2019) Monocytic HLA-DR expression for prediction of anastomotic leak after colorectal surgery. Journal of the American College of Surgeons 229, 200–209.
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  22. Deffner V, Kreuzer M, Sobotzki C, Aßenmacher M, Güthlin D, Kaiser C, Küchenhoff H, Fenske N (2019) Uncertainties in radiation exposure assessment in the Wismut cohort: a preliminary evaluation BIO Web of Conferences, p. 03009. EDP Sciences.
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  23. Küchenhoff H, Deffner V, Aßenmacher M, Neppl H, Kaiser C, Güthlin D, others (2018) Ermittlung der Unsicherheiten der Strahlenexpositionsabschätzung in der Wismut-Kohorte-Teil I-Vorhaben 3616S12223.
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  24. Brandl C, Breinlich V, Stark KJ, Enzinger S, Aßenmacher M, Olden M, Grassmann F, Graw J, Heier M, Peters A, others (2016) Features of age-related macular degeneration in the general adults and their dependency on age, sex, and smoking: results from the German KORA study. PloS one 11, e0167181.
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