
Research on generative AI, LLMs, federated learning, RAG, and AI-powered automation.
Artificial intelligence and language processing are key factors for automation, efficiency gains, and innovation in businesses and society. Despite major advances in recent years, significant challenges remain in terms of scalability, domain adaptation, explainability, and the responsible use of AI systems. Our research area develops advanced methods in machine intelligence and natural language processing to support companies in analysis, automation, and decision-making.
The focus is on the development and application of large language models, retrieval-augmented generation, and domain-specific AI solutions that can handle complex language and knowledge processing tasks and be flexibly adapted to different application areas. We support companies in integrating AI-based assistance systems, implementing federated learning, and developing methods for unlearning to ensure data protection, adaptability, and regulatory compliance. Close collaboration with industry partners results in innovative solutions that increase efficiency, enable new business models, and strengthen competitiveness in the long term. Our research places particular emphasis on the explainability and transparency of AI systems in order to gain user trust and increase acceptance in practice.
We develop practical tools and frameworks that help companies use AI responsibly and profitably. Empirical studies and pilot projects demonstrate a significant improvement in precision, adaptability, and efficiency in real-world applications. Our work helps to fully exploit the potential of AI and language processing and prepare companies for the challenges and opportunities of the digital future.
Jahr Year | Titel/Autor:in Title/Author | Publikationstyp Publication Type |
---|---|---|
2025 | Supporting Software Engineers in IT Security and Privacy through Automated Knowledge Discovery Ehl, Marco; Ahmadian, Amir Shayan; Großer, Katharina; Elsofi, Duaa Adel Ali; Herrmann, Marc; Specht, Alexander; Schneider, Kurt; Jürjens, Jan |
Konferenzbeitrag Conference Paper |
2025 | Innamark: A Whitespace Replacement Information-Hiding Method Hellmeier, Malte; Norkowski, Hendrik; Schrewe, Ernst-Christoph; Qarawlus, Haydar Khalid Haydar; Howar, Falk |
Zeitschriftenaufsatz Journal Article |
2025 | Evaluation of a large language model to simplify discharge summaries and provide cardiological lifestyle recommendations Rust, Paul; Frings, Julian; Meister, Sven; Fehring, Leonard |
Zeitschriftenaufsatz Journal Article |
2025 | With Great Power Comes Great Responsibility: Responsible Management of Artificial Intelligence in Supporting Design Research Activities Schoormann, Thorsten; Gupta, Samrat; Möller, Frederik; Chandra-Kruse, Leona |
Konferenzbeitrag Conference Paper |
2024 | Safe AI in Autonomous Vehicles. Track at AISoLA 2023 Howar, Falk; Hungar, Hardi |
Konferenzbeitrag Conference Paper |
2024 | Challenges and Opportunities for Enabling the Next Generation of Cross-Domain Dataspaces Deshmukh, Rohit; Collarana Vargas, Diego; Gelhaar, Joshua; Theissen-Lipp, Johannes; Lange-Bever, Christoph; Arnold, Benedikt Tobias; Curry, Edward; Decker, Stefan |
Konferenzbeitrag Conference Paper |