| School of Computation, Information and Technology Department of Computer Science, I19 Software Engineering for Business Information Systems (sebis) Technical University of Munich stephen.meisenbacher [at] tum.de Room FMI 01.12.040 Office hours: by appointment
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| Although I do not currently offer any new thesis or guided research topics, I invite motivated and qualified students interested in privacy and/or NLP to get into contact with me. Please include your specific interest in a topic, as well as your motivation in this particular field. |
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Stephen Meisenbacher has been a research associate at the chair for Software Engineering of Business Information Systems at the Technical University of Munich since March 2022. Stephen earned his Master's degree in Informatics at TUM under a DAAD Graduate Scholarship, having concentrated his studies in Machine Learning and Data Analytics. His Master Thesis involved identifying the practical challenges in the implementation of technical measures for data privacy compliance. Before coming to Munich, Stephen earned his Bachelor's of Computer Science at the University of Notre Dame in Indiana, USA. There, he also majored in German Language and Literature, and his honors senior thesis aimed to investigate the strengths and limitations of NLP techniques in the identification of irony in German literature. His Bachelor studies also included three summers in Berlin, as well as a semester at Universität Heidelberg. More recently, Stephen has gained valuable experience in his work as a data science consultant to Loyola University Chicago in Chicago, Illinois. |
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| A cool event we organize every year! hackaTUM is the official hackathon of the Department of Computer Science at TUM. The event features exciting challenges, prominent industry partners, and awesome fun! Learn more here: https://hack.tum.de. |
Introducing the Legal AI Use Case Radar: an online resource surveying the landscape of Legal AI. Also be sure to read the companion report! Check out it at https://legal-ai-radar.de |
| 2024 | |
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| [Me24h] | Meisenbacher, S.; Schopf, T.; Yan, W.; Holl, P.; Matthes, F.: 2024. An Improved Method for Class-specific Keyword Extraction: A Case Study in the German Business Registry. In Proceedings of the 20th Conference on Natural Language Processing (KONVENS 2024), pages 159–165, Vienna, Austria. Association for Computational Linguistics. |
| [Me24g] | Meisenbacher, S.; Chevli, M.; Matthes, F.: 2024. A Collocation-based Method for Addressing Challenges in Word-level Metric Differential Privacy. In Proceedings of the Fifth Workshop on Privacy in Natural Language Processing, pages 39–51, Bangkok, Thailand. Association for Computational Linguistics. |
| [Me24f] | Meisenbacher, S.; Chevli, M.; Vladika, J.; Matthes, F.: 2024. DP-MLM: Differentially Private Text Rewriting Using Masked Language Models. In Findings of the Association for Computational Linguistics ACL 2024, pages 9314–9328, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics. |
| [Me24e] | Meisenbacher, S.; Klymenko, A.; Kelley, P.G.; Peddinti, S.T.; Thomas, K.; Matthes, F.: Privacy Risks of General-Purpose AI Systems: A Foundation for Investigating Practitioner Perspectives. In SUPA@SOUPS'24 (2024). |
| [Me24d] | Meisenbacher, S.; Matthes, F.: 2024. Just Rewrite It Again: A Post-Processing Method for Enhanced Semantic Similarity and Privacy Preservation of Differentially Private Rewritten Text. In Proceedings of the 19th International Conference on Availability, Reliability and Security (ARES '24). Association for Computing Machinery, New York, NY, USA, Article 133, 1–11. https://doi.org/10.1145/3664476.3669926 |
| [Me24c] | Meisenbacher, S.; Machner, N.; Vladika, J.; Matthes, F.: Legal AI Use Case Radar 2024 Report. Technical University of Munich, July 2024. https://mediatum.ub.tum.de/1748412. |
| [Vl24e] | Vladika, J.; Meisenbacher, S.; Preis, M.; Klymenko, A.; and Matthes, F.: Towards A Structured Overview of Use Cases for Natural Language Processing in the Legal Domain: A German Perspective (2024). AMCIS 2024 Proceedings. 1. |
| [Kl24c] |
Klymenko, A.; Meisenbacher, S.; Polat, A. A.; and Matthes, F.: Breaking Down Privacy by Design: A Threefold Perspective (2024). AMCIS 2024 Proceedings. 30.
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| [Me24b] |
Meisenbacher S.; Chevli M.; Matthes F.: 1-Diffractor: Efficient and Utility-Preserving Text Obfuscation Leveraging Word-Level Metric Differential Privacy. In Proceedings of the 10th ACM International Workshop on Security and Privacy Analytics, pp. 23-33. 2024.
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| [Me24a] |
Meisenbacher, S.; Nandakumar, N.; Klymenko, A.; Matthes, F.: A Comparative Analysis of Word-Level Metric Differential Privacy: Benchmarking the Privacy-Utility Trade-off. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), Torino, Italy, 2024. |
| [Kl24b] |
Klymenko, A.; Meisenbacher, S.; Lilova, I., Matthes, F.: Investigating the Motivational Factors Influencing Managerial Decisions to Adopt Privacy-Enhancing Technologies, In Proceedings of the 32nd European Conference on Information Systems (ECIS 2024), Paphos, Cyprus, 2024. |
| [Kl24a] |
Klymenko, A.; Meisenbacher, S.; Favaro, L. and Matthes, F. (2024). On the Integration of Privacy-Enhancing Technologies in the Process of Software Engineering. In Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 2, ISBN 978-989-758-692-7, ISSN 2184-4992, pages 41-52. DOI: 10.5220/0012632500003690 |
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2023 | |
| [Kl23c] |
Klymenko, A.; Meisenbacher, S.; Matthes, F.: The Structure of Data Privacy Compliance. Proceedings of the 3rd International Workshop on Current Information Security and Compliance Issues in Information Systems Research (CIISR 2023), Paderborn, Germany, September 18, 2023. |
| [Kl23b] |
Klymenko, A.; Meisenbacher, S.; Messmer, F.; Matthes, F.: Privacy-Enhancing Technologies in the Process of Data Privacy Compliance: An Educational Perspective. Proceedings of the 3rd International Workshop on Current Information Security and Compliance Issues in Information Systems Research (CIISR 2023), Paderborn, Germany, September 18, 2023. |
| [Kl23a] |
Klymenko, O.; Meisenbacher, S.; Matthes, F.: Identifying Practical Challenges in the Implementation of Technical Measures for Data Privacy Compliance, In Proceedings of the 29th Americas Conference on Information Systems (AMCIS 2023), Panama City, Panama, 2023. |
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2022 | |
| [Vl22a] |
Vladika, J.; Meisenbacher, S.; Matthes, F. 2022. TUM sebis at GermEval 2022: A Hybrid Model Leveraging Gaussian Processes and Fine-Tuned XLM-RoBERTa for German Text Complexity Analysis. In Proceedings of the GermEval 2022 Workshop on Text Complexity Assessment of German Text, pages 51–56, Potsdam, Germany. Association for Computational Linguistics. |
| [Kl22b] |
Klymenko, O.; Kosenkov, O.; Meisenbacher, S.; Elahidoost, P.; Mendez, D.; Matthes, F. 2022. Understanding the Implementation of Technical Measures in the Process of Data Privacy Compliance: A Qualitative Study. In Proceedings of the 16th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM '22). Association for Computing Machinery, New York, NY, USA, 261–271. https://doi.org/10.1145/3544902.3546234 |
| [Kl22a] |
Klymenko, O.; Meisenbacher, S.; Matthes, F.: Differential Privacy in Natural Language Processing: The Story So Far. In Proceedings of the Fourth Workshop on Privacy in Natural Language Processing, pages 1–11, Seattle, United States. Association for Computational Linguistics. 2022. |
| [Me22] |
Meisenbacher, S.: Identifying Practical Challenges in the Implementation of Technical Measures for Data Privacy Compliance - Master's Thesis: Technische Universität München, Munich, Germany, 2022. |
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