Kevin Bönisch is a researcher at the Text Technology Lab, Goethe University Frankfurt. With a background in software development (5 years as C# .NET full-stack developer) and concurrent Master's studies in Computer Science, he bridges practical software engineering with academic research through projects like the Unified Corpus Explorer and Viki LibraRy virtual reality systems. His work spans NLP, 3D visualization, and machine learning applications. DFG New Data Spaces program contributor Kaggle competition participant GitHub demonstrator Research focuses include annotation-based corpus exploration , causal inference in LLMs , and collaborative hypertext systems . His 2025 NAACL Best Demo Paper showcased UCE system innovations, while 2024 LIRAI workshop work demonstrated legal document retrieval via SVR ensembles. Notable awards include IAV-Coding competition first place , DESIGNRUSH website design recognition , and Goethe-University Innovation Prize finalist . Key projects: ROBERT dialogue system , BIOfid biodiversity service , and Bundestags-Mine legislative analysis .
Can Calisir is a Doctoral Researcher affiliated with the Machine Learning Group at the Technical University of Berlin. He holds a Master’s degree in Computational Engineering Science (2024) and a Bachelor’s degree in Mechanical Engineering (2021). Education M.Sc. Computational Engineering Science (2024), Technical University of Berlin B.Sc. Mechanical Engineering (2021), Karlsruhe Institute of Technology His research focuses on the application of AI in manufacturing, foundation models, anomaly detection, and multimodal machine learning. Specific interests include Large Language Models, Generative Models, and Time Series Analysis for code generation tasks.
Xiang Anthony Chen is affiliated with the University of California Los Angeles as a Researcher . His work spans Human-Computer Interaction , Artificial Intelligence , and Medical Imaging , with a focus on Human-AI Collaborative Systems and Accessible Technology . He has contributed to Generative UI Tools , Medical Diagnosis AI , and 3D Printing Applications . Research Interests include: Explainable AI for pathology and medical imaging Generative models in design and user experience Accessible technology for blind or low-vision users Human-AI collaboration in news and healthcare Recent Articles highlight trends in AI-Augmented Pathology , Conversational Agents , and 3D-Printable Mechanisms . His collaborations with medical professionals and technologists underscore practical applications of AI in diagnostics and design.
Wenjie Li is a Professor at the Department of Computing, Hong Kong Polytechnic University, and holds a PhD from the Chinese University of Hong Kong (1997). His research spans Natural Language Processing, Artificial Intelligence, and Machine Learning , focusing on Large Language Models (LLMs) Multimodal Systems Dialogue and Recommender Systems Speculative Decoding and Inference Optimization Chain-of-Thought Reasoning Recent work emphasizes generative retrieval , personalized web agents , and error-resilient LLM frameworks . Key contributions include the JobFormer for skill-aware recommendations, STeCa for trajectory calibration, and TokenSkip for controllable reasoning compression. Publications reveal a trend toward enhancing multimodal alignment and safety mechanisms in aligned LLMs. Li actively collaborates with institutions like Queen's University , Tsinghua University , and Nanjing University , working on projects such as text-image interleaved retrieval and speculative decoding surveys . His 2024-2025 output includes 15+ papers at venues like ACL, CVPR, ICLR , and journals like IEEE Transactions on Neural Networks .
Deng Cai is a Professor at Zhejiang University's College of Computer Science, working in the State Key Laboratory of CAD&CG in Hangzhou, China. He also maintains an affiliation with Tencent AI Lab, demonstrating his strong connection between academic research and industry applications in artificial intelligence. His academic background includes a PhD from the University of Illinois at Urbana-Champaign, Department of Computer Science (2009). Professor Cai's research spans multiple domains within artificial intelligence, with particular emphasis on computer vision, deep learning, and their applications. His work shows strong focus on 3D object detection, lane detection for autonomous vehicles, and the application of large language models to various vision tasks. He has made significant contributions to traffic forecasting, trajectory prediction, and CAD generation systems. His recent work increasingly integrates large language models with computer vision tasks, demonstrating the evolving nature of his research interests toward multimodal AI systems. The trajectory of Professor Cai's publications reveals a clear progression from foundational computer vision and machine learning research toward increasingly complex and applied systems. His work shows strong emphasis on practical applications in autonomous driving, with numerous papers on 3D object detection, lane detection, and trajectory prediction. More recently, his research has expanded to include generative models for CAD systems and video customization, often leveraging large language models in innovative ways. The consistent publication output across top-tier venues including CVPR, ICCV, AAAI, and NeurIPS demonstrates sustained research productivity and impact. Professor Cai has established significant research collaborations, particularly with Xiaofei He (161 joint publications), Haifeng Liu (50), Zhou Zhao (42), Wenxiao Wang (41), and Binbin Lin (39). His work appears across diverse publication venues including IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing, and proceedings of major AI conferences. The breadth of his publication venues reflects the interdisciplinary nature of his research spanning theoretical machine learning to applied computer vision systems. Professor Cai leads research activities within Zhejiang University's College of Computer Science, particularly focusing on the State Key Laboratory of CAD&CG. His work bridges academic research with practical industry applications through his affiliation with Tencent AI Lab. The laboratory environment supports research in computer vision, machine learning, and their applications to real-world problems in autonomous systems, content generation, and intelligent transportation.
Daniel Sonntag is a Research Professor at the German Research Center for Artificial Intelligence (DFKI) in Kaiserslautern, Germany, with extensive contributions to artificial intelligence, particularly in medical applications and human-AI interaction. His work spans multiple domains including explainable AI, medical imaging analysis, eye tracking technology, and clinical decision support systems. His research interests focus on developing interpretable AI systems for healthcare applications, with particular emphasis on ophthalmology and radiology. He investigates how to make AI systems more transparent and trustworthy through concept bottleneck models, multi-agent systems, and interactive learning approaches. His work bridges the gap between theoretical AI advancements and practical clinical applications, ensuring that AI tools are both effective and understandable for medical professionals. Sonntag's publication record demonstrates a consistent trend toward more interactive and interpretable AI systems, with recent work focusing on multi-agent RAG systems for radiology report generation, clinical decision support tools for ophthalmology, and biodiversity monitoring through soundscape analysis. His research shows a strong interdisciplinary approach, combining computer vision, natural language processing, and human-computer interaction to solve complex problems in healthcare and environmental monitoring. His work has appeared in leading venues including IUI, ICMI, KI, and various IEEE and ACM conferences, reflecting his contributions to both the theoretical foundations and practical applications of artificial intelligence.
Dietrich Klakow is a prominent researcher at Saarland University in Saarbrücken, Germany, with an extensive publication record spanning from 1997 to 2025. His work primarily focuses on natural language processing, speech recognition, and machine learning with significant contributions to multilingual models and African language processing. His research interests span a wide range of topics within computational linguistics and artificial intelligence. Klakow has made substantial contributions to Natural Language Processing , particularly in multilingual contexts and low-resource languages. His work on African language technologies has been particularly impactful, developing resources and models for languages that are often neglected in mainstream NLP research. He has also conducted significant research in speech recognition , transformer models , and computational linguistics , with a focus on practical applications and theoretical foundations. Klakow's recent publications demonstrate a strong focus on large language models, their capabilities, limitations, and applications across diverse linguistic contexts. His work spans both theoretical investigations of model architectures and practical applications addressing real-world challenges in language technology. His collaborative work spans numerous international partnerships, particularly with researchers working on African language technologies and multilingual NLP systems.
Yancheng He is an Assistant Professor at the School of Computer Science and Technology, University of Science and Technology of China. His research focuses on Natural Language Processing, Large Language Models, and Multimodal Learning, with emphasis on factuality evaluation, instruction tuning, and collaborative filtering. He has co-authored numerous high-impact publications in venues like ACL, NAACL, and EMNLP, including benchmarks like Chinese SimpleQA and Chinese SafetyQA. His recent work explores error detection in long chain-of-thought reasoning, 2D-DPO for preference optimization, and cross-domain recommendation systems. He collaborates extensively with researchers including Jiaheng Liu, Shilong Li, and Wenbo Su. No specific awards or student advising details are mentioned in the provided data.
Luca Cagliero is an Associate Professor in the Department of Control and Computer Engineering at Politecnico di Torino (Polytechnic University of Turin), Italy. His research spans multiple domains within computer science, with particular expertise in data mining, machine learning, natural language processing, and multimodal analysis. He has established a prolific research career with over 150 publications spanning from 2009 to the present, demonstrating consistent scholarly productivity. Dr. Cagliero's research interests focus on the intersection of artificial intelligence and practical applications. His work addresses fundamental challenges in data mining, information retrieval, and educational technology, with recent publications showing increasing emphasis on large language models, multimodal analysis, and explainable AI. He has made significant contributions to text summarization techniques, database systems, and applying machine learning to educational contexts. His recent publications (2023-2025) demonstrate a clear research trajectory toward multimodal AI systems, with particular attention to the integration of vision and language processing. His work spans theoretical contributions in machine learning methods as well as practical applications in educational technology, social media analysis, and document understanding. The breadth of his collaborations across different application domains indicates a versatile research profile that bridges theoretical and applied computer science. Dr. Cagliero has mentored numerous researchers who have become his frequent collaborators, including Lorenzo Vaiani, Moreno La Quatra, and Davide Napolitano. His work has appeared in top-tier venues including ACL, IEEE Transactions on Knowledge and Data Engineering, and Expert Systems with Applications, reflecting the high quality and impact of his research contributions.
Dr. Robert Haschke serves as a Professor and Responsible Investigator in the Cognitive Systems and Social Interaction Group at Bielefeld University's Faculty of Engineering. He is affiliated with the Center for Cognitive Interaction Technology (CITEC) and serves on the Examination Board for the Intelligent Interactive Systems Master's program. His office is located at CITEC 2-035, and he can be reached at rhaschke@techfak.uni-bielefeld.de or +49 521 106-12122. Professor Haschke's research spans multiple domains of robotics and artificial intelligence, with particular emphasis on tactile sensing systems, robotic manipulation, and human-robot interaction. His work explores advanced methods for enabling robots to perceive their environment through touch, with applications in assistive robotics and industrial automation. He investigates how machines can learn from human interactions and adapt their behavior through reinforcement learning and sensor fusion techniques. His research bridges theoretical advances with practical implementations, focusing on transferring knowledge from simulation to real-world robotic systems. Analysis of Professor Haschke's recent publications reveals a strong trajectory toward more sophisticated tactile perception systems and their integration with language and vision for natural human-robot collaboration. His work consistently addresses the simulation-to-reality gap, developing methods for transferring control policies from virtual environments to physical robots. The research shows increasing integration of multimodal sensing (tactile, visual, linguistic) to enable more capable and adaptable robotic manipulation in unstructured environments. As an educator, Professor Haschke teaches advanced courses including Robot Manipulators (39-Inf-RM), Advanced Artificial Intelligence (39-M-Inf-AI-adv_a), Advanced Artificial Intelligence (focus) (39-M-Inf-AI-adv-foc), and Basics of Artificial Intelligence (39-M-Inf-AI-bas). His teaching reflects his research expertise, providing students with both theoretical foundations and practical skills in robotics and AI. Professor Haschke is an integral member of Bielefeld University's Cognitive Systems and Social Interaction Group within CITEC. His work contributes significantly to the university's Socio-Technical World research area, particularly in developing capabilities that enable agents (humans, robots, and AI systems) to act, communicate, and learn in complex environments. His research group focuses on creating robotic systems that can interact naturally with humans through advanced perception and adaptive control mechanisms.
Markus Egg is a Professor at the Department of English and American Studies , Humboldt University of Berlin. He is a principal investigator in the Collaborative Research Center 1412 (CRC 1412) focused on "Register: Language Users’ Knowledge of Situational-Functional Variation". His work bridges theoretical linguistics with computational approaches, particularly in metaphor annotation, register analysis, and discourse structure. He has organized international workshops such as MeStaR (Metaphors and Stance Markers in Register Variation). Current affiliation: Humboldt University of Berlin Key projects: CRC 1412, GeRMaN (German Register Marking by Non-Literal Expressions), MeStaR workshop Research Focus: His research examines how metaphors and metonymies function as markers of register variation across different contexts, including social media (e.g., LGBTQ+ slang on Twitter), religious language, and computational linguistics. He integrates methodologies from corpus linguistics, cognitive science, and natural language processing to model register knowledge. Teaching: Regularly teaches courses on linguistics, computational semantics, and machine learning applications in language research. In 2023, he conducted a webinar on "Register marking by metaphor and metonymy" at Nakhchivan State University. Collaborations: Collaborates with institutions like Charles University (Prague), Ruhr-Universität Bochum, and Università della Calabria. Frequent collaborator: Valia Kordoni (metaphor corpus development).
Prof. Dr. Silvia Kutscher serves as Professor for the Theory and History of Multimodal Communication at the Institute of Archaeology, Humboldt University of Berlin since March 2016, a position established within the Cluster of Excellence Topoi. Her interdisciplinary work bridges linguistics, semiotics, and archaeology with particular focus on ancient Egyptian communication systems. She leads project B03 on Register variation and asymmetric communication in Ancient Egypt within the Collaborative Research Center 1412 "Register: Language Users' Knowledge of Situational-Functional Variation". Her research centers on semiotics with emphasis on multimodal communication in ancient contexts. She has pioneered analytical methods for examining how spatial relations function as semiotic resources in pharaonistic communication, developing the theoretical framework for analyzing text-image compositions in Ancient Egypt. Her work creates crucial interdisciplinary connections between semiotic-linguistic multimodality research and Egyptology, revealing how dimensional aspects and spatial organization contribute to meaning-making in ancient artifacts. Recent publications demonstrate her sustained focus on multimodal analysis of ancient Egyptian artifacts, particularly through the CaeMmCom Corpus project. Her scholarly trajectory shows consistent development from general linguistic theory to specialized application in ancient communication systems, increasingly incorporating digital humanities methodologies. This work represents significant advancement in understanding how ancient societies structured information through combined textual and visual means. Prof. Kutscher holds leadership roles in academic organizations: Since 2018: Board member and co-leader of the Archaeology section of the German Society for Semiotics 2010-2013: Second chair of the German Linguistic Society (DGfS) 2014-2016: Advisory board member of DGfS She directs the Working Group on Multimodal Communication in Ancient Egypt, which develops theoretically grounded analytical tools by combining semiotic-linguistic approaches with Egyptological expertise. This initiative also implements technological tools for annotating and querying multimodal artifact data across the text-object-space continuum.
Dr. Katja Maquate is a Researcher at the Institute for German Language and Linguistics within the Faculty of Arts and Humanities at Humboldt-Universität zu Berlin. She is actively involved in the Collaborative Research Centre (CRC 1412) with a specific focus on project C03: "Real-time register comprehension in adolescent heritage speakers' languages." Her research spans multiple aspects of language processing with particular emphasis on how social context influences linguistic comprehension. Maquate's research interests center around language register processing, examining how formality levels in language interact with syntactic and semantic processing during comprehension. Her work investigates the real-time processing of register congruence effects using eye-tracking methodologies in both written and spoken language comprehension. She explores how contextual formality information rapidly impacts sentence processing, interacts with morphosyntactic knowledge, and influences verb-argument relations. Her research spans multiple methodologies including eye-tracking reading studies, Visual World Paradigm experiments, and self-paced reading tasks to investigate the cognitive mechanisms underlying register processing. Analysis of Maquate's recent publications reveals a consistent focus on the intersection of social context and linguistic processing. Her work demonstrates sophisticated experimental designs that manipulate register congruence alongside traditional linguistic variables like morphosyntactic agreement and verb-argument relations. A notable pattern is her investigation of how context effects unfold over time during sentence processing, with particular interest in whether register effects emerge rapidly or require more processing time compared to traditional linguistic constraints. Her research often compares different presentation modalities (blocked vs. mixed) to understand how comprehenders adapt to shifting social contexts. Maquate actively collaborates with researchers across multiple institutions, frequently working with Pia Knoeferle and other members of the CRC 1412. Her grant-funded research through the Collaborative Research Centre demonstrates significant funding support for her investigations into language register phenomena. While specific grant amounts aren't detailed in the available information, her multiple publications from this research program indicate substantial research support. As part of the CRC 1412 team, Maquate contributes to a larger research infrastructure examining register phenomena across different time periods, languages, modalities, and cultures. Her work represents an important component of this comprehensive investigation into how social context shapes language use and comprehension.
Muriel Norde is a full Professor of Scandinavian Linguistics at the Department of Northern European Studies , Humboldt-Universität zu Berlin, since 2013. Her research focuses on historical linguistics , construction grammar , and degrammaticalization in Germanic languages, particularly Scandinavian varieties. She has extensive experience in corpus linguistics and diachronic morphological analysis . Current Role: Professor at Humboldt-Universität Berlin Past Affiliations: University of Groningen (1997-2013), University of Amsterdam (1998-2003) Research Highlights : Spearheads the BiNoKo Corpus project for comparative historical register analysis. Leads international collaborations on exaptation in morphology and evaluative morphology . Key publications in Diachronic Construction Grammar and libfixes in European languages. Academic Contributions : Authored 83+ publications with over 21,182 reads. Notable works include studies on Swedish genitive evolution , Dutch diminutives , and modal constructions in Old Swedish . Her 2023 Frontiers in Psychology paper co-analyzes register pervasiveness across cultures. Supervision : Serves as promotor or committee member for 9+ PhD theses, including projects on Old Norwegian syntax , Dutch productivity shifts , and Norwegian demonstrative reinforcement . Collaborates with universities in Edinburgh, Ghent, Leuven, and Oslo.
Dr. Antje Sauermann is a researcher at the Humboldt University of Berlin, affiliated with the Institute for German Language and Linguistics within the Faculty of Language and Literature. She is actively involved in the Collaborative Research Center 1412 (CRC 1412) 'Register: Language Users' Knowledge of Situational-Functional Variation', where she leads project C07 focusing on The impact of language ideologies on register distinctions in multilingual contexts . Her research spans multiple linguistic subfields with a particular emphasis on sociolinguistic variation across different language communities. Dr. Sauermann's research interests center on sociolinguistics, language register, and German linguistics, with special attention to multilingualism , language contact , and variationist sociolinguistics . Her work examines how language users navigate different registers across various social contexts, with particular focus on Namibian German ('Namdeutsch') and urban varieties like Kiezdeutsch. She investigates how linguistic features differentiate across registers and how social meaning is constructed through language variation. Her research employs both corpus linguistic and experimental approaches , including copy-editing tasks and perception studies. Analysis of Dr. Sauermann's recent publications reveals a consistent focus on register phenomena across different German varieties and contexts. Her work demonstrates how linguistic features vary systematically across formal and informal registers, particularly in multilingual settings. She has made significant contributions to understanding how language ideologies shape register distinctions, with particular attention to Namibian German's unique position as a variety used in both informal and formal contexts. Her research often intersects with broader questions about linguistic identity, social salience hierarchies, and the cognitive processing of register variation. As a member of the CRC 1412 research community, Dr. Sauermann collaborates with numerous scholars across disciplines to investigate register phenomena from multiple methodological perspectives. Her work contributes to the center's goal of developing comprehensive models of how language users acquire, process, and produce language according to situational and functional parameters. Dr. Sauermann's research methodology combines Corpus analysis of the DNam corpus (German in Namibia) Experimental perception studies Cross-linguistic comparison Copy-editing tasks to assess register awareness Analysis of linguistic features across social salience hierarchies Her work demonstrates how linguistic variables differ in their ability to convey social meaning across different communicative contexts.
