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Christiane Attig, Luisa Winzer, Tim Schrills, Mourad Zoubir, Maged Mortaga, Patricia Wollstadt, Christiane Wiebel, Thomas Franke , "Understanding successful human-AI teaming: The role of goal alignment and AI autonomy for social perception of LLM-based chatbots", Computers in Human Behavior: Artificial Humans, vol. 7, 2026.
AbstractLLM-based chatbots such as ChatGPT support collaborative, complex tasks by leveraging natural language processing to provide skills, knowledge, or resources beyond the user's immediate capabilities. Joint activity theory suggests that effective human-AI collaboration, however, requires more than responding to verbatim prompts—it depends on aligning with the user’s underlying goal. Since prompts may not always explicitly state the goal, an effecti...
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Nergiz Yuca, Christian Internó, Nikolay Matyunin, Markus Olhofer, Barbara Hammer, Stefan Katzenbeisser , "PPFLex: Securing Non-IID Optimization in Federated Learning via MPC", FLCA@AAAI'26 - Workshop on Federated Learning for Critical Applications, 2026.
AbstractFederated Learning enables collaborative model training across multiple clients without sharing raw data, yet remains vulnerable to inference attacks. Optimizing FL for high performance and ensuring strong privacy guarantees are separate challenges, each requiring different approaches and trade-offs between efficiency, privacy and accuracy. Existing approaches often prioritize either model performance or privacy, failing to address both effective...
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Nergiz Yuca, Nikolay Matyunin, Ektor Arzoglou, Nikolaos Athanasios Anagnostopoulos, Stefan Katzenbeisser , "A Survey on Privacy-Preserving Computing in the Automotive Domain", ACM Computing Surveys, vol. 58, 2026.
AbstractAs vehicles become increasingly connected and autonomous, they accumulate and manage various personal data, thereby presenting a key challenge in preserving privacy during data sharing and processing. This survey reviews applications of Secure Multi-Party Computation (MPC) and Homomorphic Encryption (HE) that address these privacy concerns in the automotive domain. First, we identify the scope of relevant use cases for these technologies, by su...
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Simon Kohaut, Daniel Ochs, Shun Zhang, Benedict Flade, Julian Eggert, Kristian Kersting, Devendra Dhami , "CycliST: A Video Language Model Benchmark for Reasoning on Cyclical State Transitions", Data-centric Machine Learning Research, 2026.
AbstractWe present CycliST, a novel benchmark dataset designed to evaluate Video Language Models (VLM) on their ability for textual reasoning over cyclical state transitions. CycliST captures fundamental aspects of real-world processes by generating synthetic, richly structured video sequences featuring periodic patterns in object motion and visual attributes. CycliST employs a tiered evaluation system that progressively increases difficulty through vari...
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Hifza Javed, Ella Maule, Thomas H Weisswange, Bilge Mutlu , "Robots for Communities", 21st ACM/IEEE International Conference on Human-Robot Interaction (HRI 2026), 2026.
AbstractThis workshop on Robots for Communities explores how robots can serve as shared social resources that support the collective well-being of communities. While robots have traditionally been created to serve corporations or individuals, leading human–robot interaction research to focus largely on individuals or small groups, communities remain a crucial yet underexplored context for robotics. Understanding robots in community settings requires...
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Tingkai Li, Yifan Zhang, Benjamin Nowacki, Sina Navidi, Thomas Schmitt, Shan Hu, Chao Hu , "Benchmarking half-cell model fitting approaches for lithium-ion battery degradation diagnostics", eTransportation, vol. 29, 2026.
AbstractAccurate degradation diagnostics for lithium-ion batteries—specifically quantifying loss of active material on the positive and negative electrodes and loss of lithium inventory—enables component-level insights to improve design, control, and maintenance strategies. Among the few non-intrusive techniques, empirical half-cell model fitting to full-cell cycling data offers a promising path but often suffers from inconsistent fitting performance. To...
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Melvin Wong, Yueming Lyu, Thiago de Jesus de Araujo Rios, Stefan Menzel, Yew Soon Ong , "LLM-to-Phy3D: Physically Conform Online 3D Object Generation with LLMs", IEEE Congress on Evolutionary Computation, 2026.
AbstractThe emergence of generative artificial intelligence (GenAI) and large language models (LLMs) has revolutionized the landscape of digital content creation in different modalities. However, its potential use in Physical AI for engineering design, where the production of physically viable artifacts is paramount, remains vastly underexplored. The absence of physical knowledge in existing LLM-to-3D models often results in outputs detached from real-wo...
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Maria Bresich, Guenther Raidl, Caspian Coleman, Pascal Welke, Steffen Limmer , "Search Space Reduction Through Machine Learning for the Electric Autonomous Dial-A-Ride Problem", International Conference on Machine Learning, Optimization, and Data Science (LOD) 2025, 2026.
AbstractThe complexity of combinatorial optimization problems often leads to a steep performance decrease of exact as well as heuristic solving approaches with increasing problem size. This paper explores the usage of machine learning to reduce the practical complexity of such problem instances by predicting and removing unpromising parts of the search space in a preprocessing step in order to accelerate the subsequent solving process. This approach is i...
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Patricia Wollstadt , "GRADE CompuMath CareerTAlk", Goethe University, online, 2026.
AbstractCareer talk on own biography with a focus on the switch from academic to industry research for members of the Goethe Research Academy for Early Career Researchers (GRADE). The talk will feature a short introduction of HRI, my own background and biography, and will close with an overview of my work at HRI. The talk closes with a Q&A session....
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Ehsan Aliyan, Jose Almeida, Steffen Limmer, Sergio Ramos, Joao Soares , "Adaptive Large Neighborhood Search for Optimal Clustering of Prosumers into Energy Communities", International Conference on Machine Learning, Optimization, and Data Science (LOD) 2025, 2026.
AbstractThe growing adoption of renewable energy sources (RES) is transforming sustainable energy systems and economies, driven by their environmental benefits and steadily decreasing costs. The increasing integration of renewable energy sources in local energy systems necessitates efficient clustering of prosumers to enhance economic benefits and grid stability. This paper presents a method for clustering prosumers into energy communities based on their...
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