Prof. Dr. Ivan Habernal

I hold a W3 Professorship of Fairness and Transparency at Ruhr University Bochum, Germany, jointly affiliated with the Research Center Trustworthy Data Science and Security. My current research areas include privacy-preserving NLP, legal NLP, and explainable and trustworthy models. My research track spans argument mining and computational argumentation, crowdsourcing, large-scale corpora, serious games, sentiment and sarcasm on social media, and semantic web.

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Lena Held, MSc.

Lena is currently exploring the research area of computational argumentation in the legal domain.

Sebastian Ochs, MSc.

Sebastian' research areas include privacy-preserving NLP with a focus on text rewriting with provable guarantees.

Mahammad Namazov, MSc.

Mahammad explores interpretability in NLP applications focusing on the legal domain.

Dr. Erion Çano

Erion's research is on privacy-preserving NLP and robust anonymization.

Dr. Timour Igamberdiev

Timour's research areas included privacy-preserving NLP, differential privacy in graph neural networks, and privacy-preserving semantic representations of language.

Jesper Schlegel

Jesper's thesis aimed to detect prompt injection attacks on large language models using saliency methods.

Qiankun Zheng, MSc.

Qiankun's research areas included multimodal learning and fact-checking.

Martin Kerscher

Martin's thesis compared privacy-preserving inference methods, applying them to NLP tasks and developing software to connect PyTorch with techniques like homomorphic encryption and garbled circuits.

Marius Büttner

Marius investigated question answering in the German legal domain. His thesis explored how well existing models can support laymen to receive a first legal aid, based on a created dataset of questions in lay language to answers in legalese. → EACL'24 paper

Christopher Weiss

Chris's thesis focused on finding best practices on how to optimally adapt the concept of differential privacy in NLP environments while putting the needs of the end-users first and considering perceptional biases to make differential privacy more accessible. → LREC-COLING'24 paper

Lijie Hu

Lijie is a second-year PhD student in Computer Science at King Abdullah University of Science and Technology. Her research interests cover machine learning algorithm on Explainable AI (XAI), Differential Privacy, and Differential Private Natural Language Models. She is also interested in Machine Unlearning, and other security issues in data field. → EACL'24 paper

Sudarshan Sivakumar

Sudarshan is an undergraduate student in Computer Science from India. His primary research interest is in creating language processing tools that are socially and ethically responsible. He is working on a research project related to differentially private synthetic data generation.

Nina Mouhammad

Nina wrote her thesis on privacy-preserving techniques for crowdsourcing sensitive text data. → Linguistic Annotation Workshop (ACL'23) paper

Johanna Frenz

Johanna studied computer science at TU Darmstadt. In her bachelor thesis, she compiled an easily accessible legal benchmark dataset to enable evaluating models on a variety of legal NLP tasks.

Lars Wolf

Lars, student of information systems technologies, cooperated with political scientists to identify indoctrination in German history textbooks through entity emotion analysis.

Ying Yin

Ying explored privacy-preserving transformer models in the legal domain. Her thesis combined large-scale pre-training with differential privacy and evaluates the trade-off between privacy-preserving capability and downstream performance. → Legal NLP workshop (EMNLP'22) paper

Sarah Lettmann

Sarah explored ethical argumentation in scientific literature. Her thesis focused on controversial technologies and automatic mining of absent, shifting, and evolving ethical arguments.

Manuel Senge

Manuel was a bachelor's student at the TU Darmstadt focusing on machine learning. He wrote his thesis on the effectiveness and impact on accuracy using differential privacy in NLP. → EMNLP'22 paper

Lena Held

Lena studied computer science at TU Darmstadt. In her thesis she dealt with differentially private language representation learning.

Daniel Faber

Daniel explored legal argument mining in court decisions with focus on ECHR decisions and their art of argumentation in regard to their importance level. → AI & Law journal paper

Fabian Kaiser, M.Sc.

Fabian's research area included legal argument mining, expert annotations, and low-resource and few-shot transfer learning for annotation recommendations.