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. I lead the Natural Language Processing focus group at SLDS and I am part of the Causal and Fair Machine Learning focus group. Further, I am one of the main maintainers of the course Deep Learning for NLP that is jointly developed at LMU Munich and the University of Vienna.

Contact

Department of Statistics, LMU Munich
Ludwigstraße 33, D-80539 München
matthias [at] stat [dot] uni [minus] muenchen [dot] de

Teaching

Research Interests

My main research interest lies in Natural Language Processing, for more details see the NLP focus group page.

Thesis supervision

I supervise theses on various topics related to NLP. Please read the following information before writing me an e-mail:

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

You Can Find me on

References

  1. Solderer A, Hicklin S, Aßenmacher M, Ender A, Schmidlin P (2024) Influence of an allogenic collagen scaffold on implant sites with thin supracrestal tissue height: a randomized clinical trial. Accepted to Clinical Oral Investigations.
  2. Mayer L, Heumann C, Aßenmacher M (2024) Can OpenSource beat ChatGPT? - A Comparative Study of Large Language Models for Text-to-Code Generation. Accepted at the Swiss Text Analytics Conference 2024.
  3. Aßenmacher M, Sauter N, Heumann C (2024) Classifying multilingual party manifestos: Domain transfer across country, time, and genre. Accepted at the Swiss Text Analytics Conference 2024.
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  4. Deiseroth B, Meuer M, Gritsch N, Eichenberg C, Schramowski P, Aßenmacher M, Kersting K (2024) Divergent Token Metrics: Measuring degradation to prune away LLM components – and optimize quantization. Accepted at the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.
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  5. Gruber C, Hechinger K, Aßenmacher M, Kauermann G, Plank B (2024) More Labels or Cases? Assessing Label Variation in Natural Language Inference Proceedings of the Third Workshop on Understanding Implicit and Underspecified Language, pp. 22–32. Association for Computational Linguistics, Malta.
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  6. Garces Arias E, Pai V, Schöffel M, Heumann C, Aßenmacher M (2023) Automatic Transcription of Handwritten Old Occitan Language Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 15416–15439. Association for Computational Linguistics, Singapore.
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  7. Schulze P, Wiegrebe S, Thurner PW, Heumann C, Aßenmacher M (2023) Exploring Topic-Metadata Relationships with the STM: A Bayesian Approach. Accepted at Advances in Statistical Analysis (AStA).
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  8. 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 First Workshop on Ancient Language Processing (ALP 2023),
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  9. 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 ECML-PKDD 2023),
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  10. Urchs S, Thurner V, Aßenmacher M, Heumann C, Thiemichen S (2023) How Prevalent is Gender Bias in ChatGPT? - Exploring German and English ChatGPT Responses 1st Workshop on Biased Data in Conversational Agents (co-located with ECML-PKDD 2023),
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  11. Öztürk IT, Nedelchev R, Heumann C, Garces Arias E, Roger M, Bischl B, Aßenmacher M (2023) How Different Is Stereotypical Bias Across Languages? 3rd Workshop on Bias and Fairness in AI (co-located with ECML-PKDD 2023),
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  12. Rauch L, Aßenmacher M, Huseljic D, Wirth M, Bischl B, Sick B (2023) ActiveGLAE: A Benchmark for Deep Active Learning with Transformers ECML-PKDD 2023,
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  13. Witte M, Schwenzow J, Heitmann M, Reisenbichler M, Aßenmacher M (2023) Potential for Decision Aids based on Natural Language Processing Proceedings of the European Marketing Academy, 52nd, (114322),
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  14. Vogel M, Aßenmacher M, Gubler A, Attin T, Schmidlin PR (2023) Cleaning potential of interdental brushes around orthodontic brackets-an in vitro investigation. Swiss Dental Journal 133.
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  15. Akkus C, Chu L, Djakovic V, Jauch-Walser S, Koch P, Loss G, Marquardt C, Moldovan M, Sauter N, Schneider M, Schulte R, Urbanczyk K, Goschenhofer J, Heumann C, Hvingelby R, Schalk D, Aßenmacher M (2023) Multimodal Deep Learning. arXiv preprint arXiv:2301.04856.
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  16. 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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  17. 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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  18. Aßenmacher M, Dietrich M, Elmaklizi A, Hemauer EM, Wagenknecht N (2022) Whitepaper: New Tools for Old Problems.
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  19. 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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  20. 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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  21. 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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  22. 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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  23. 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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  24. 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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  25. 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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  26. 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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  27. 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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  28. 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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  29. 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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  30. 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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  31. 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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  32. 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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  33. 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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