جست‌وجوی استاد و پژوهشگر

Torsten Zeschمشاهده پروفایل

استاد

• Computational Linguistics
• Natural Language Processing
• Educational Technology
• +۶ مورد دیگر

Prof. Dr.-Ing. Torsten Zesch serves as Deputy Scientific Director, Member of the Executive Board, and Head of the Research Professorship for Computational Linguistics at FernUniversität in Hagen since March 2022. He previously held W2 and W1 Professorships for Sprachtechnologie (Language Technology) at Universität Duisburg-Essen from 2014-2022. Zesch is also Spokesperson of the Advisory Board of the German Society for Computational Linguistics and Language Technology (GSCL) since 2024, having previously served as GSCL President from 2018-2024. Dr. Zesch completed his dissertation (Dr.-Ing.) in Computer Science at Technische Universität Darmstadt in 2009. His research spans robust language processing systems, analysis of non-standard language structures, and educational applications of language technology, with particular focus on automatic content scoring and spelling error correction for learner language. Zesch's recent publications reveal a strong focus on integrating language technology with educational applications. His work demonstrates expertise in developing practical NLP solutions for educational assessment, including transformer-based spelling error feedback systems, hierarchical automatic scoring methods, and leveraging LLMs for educational applications despite cold-start problems. He also maintains significant research in hate speech detection, particularly in multimodal contexts and visio-linguistic models. As leader of the Computational Linguistics research professorship within the CATALPA research center, Zesch directs a team investigating how language technology can support educational processes. His projects DAKODA and KISS-Pro focus on language acquisition and educational applications. He has contributed significantly to the field through numerous publications in top NLP and educational technology venues, with over 100 publications spanning from 2006 to present.

• Knowledge Representation
• Spatial and Temporal Cognition
• Multimodal Interaction
• +۳ مورد دیگر

Prof. Christopher Habel is a Professor in the Department of Computer Science at the University of Hamburg, leading the WSV work area. His research focuses on spatial and temporal cognition, multimodal interaction, and accessibility technologies. He holds the formal title of Head of Department WSV and is affiliated with the Faculty of Mathematics, Computer Science and Natural Sciences. Habel's work bridges cognitive science, artificial intelligence, and human-computer interaction with an emphasis on assistive technologies for visually impaired individuals. Key research projects include the CINACS International Research Training Group, exploring auditory-haptic access to spatial graphics, and the development of multimodal comprehension systems integrating text, graphics, and tactile feedback. His contributions span theoretical frameworks in cognitive linguistics and practical implementations of verbal assistance systems for tactile maps and haptic graph exploration. Over 30 years of academic activity is reflected in his extensive publication record (1984–2015), with recent work emphasizing cross-linguistic studies, event recognition in virtual environments, and the design of multimodal interfaces for cognitive robotics. His research integrates computational models with empirical studies on human perception and language processing.

Harald Koschمشاهده پروفایل

استاد

• Distributed Information Systems
• Big Data
• eHumanities
• +۳ مورد دیگر

Professor Harald Kosch serves as Vice President for Academic Infrastructure and IT at the University of Passau. Since 2006, he holds the Chair of Distributed Information Systems within the Faculty of Computer Science and Mathematics. He co-leads the tri-national IRIXYS research center (University of Passau, INSA Lyon, Università di Milano) and directs the German-French DFH/UFA Doctoral College. Current academic leadership roles Specialization in distributed systems and big data International research networks in digital innovation Recipient of French academic distinction His team develops tools for distributed information processing in multimedia and data-intensive applications, with applications in eHumanities and emergency logistics. Research emphasizes cross-border collaboration and technology transfer between academia and industry. Scientific distinctions include: Chevalier de l'Ordre des Palmes Académiques 2022 DFH Dissertation Prize (for Dr. Benjamin Planche) Active in EU digital strategy, Bavarian sustainability initiatives, and Franco-Bavarian AI competitions.

• Arabic Natural Language Processing
• Machine Translation
• Dialect Identification
• +۷ مورد دیگر

Ahmed Abdelali is a prominent researcher at Hamad Bin Khalifa University's Qatar Computing Research Institute (QCRI), where he works in the Arabic Language Technologies department. With 97 publications spanning from 2004 to 2024, he has established himself as a leading figure in Arabic natural language processing research. His work has been presented at major conferences including ACL, EMNLP, EACL, and specialized Arabic NLP venues. Dr. Abdelali's research focuses on several key areas within Arabic computational linguistics, with particular emphasis on Arabic language technologies. His work spans Arabic dialect identification, diacritization, machine translation, offensive language detection, and part-of-speech tagging. In recent years, he has been at the forefront of adapting large language models for Arabic language processing, contributing significantly to benchmarking frameworks like LAraBench and LLMeBench that evaluate Arabic language models' performance. His publication record shows consistent high-impact contributions, with notable collaborations with researchers including Hamdy Mubarak (45 co-authored papers), Kareem Darwish (38 co-authored papers), and Nadir Durrani (26 co-authored papers). Recent work has focused on large language models for Arabic, cross-lingual spaces, and benchmarking frameworks to advance Arabic AI capabilities. His research bridges theoretical advances with practical applications, particularly in processing the linguistic complexities of Arabic and its dialects. Through his leadership in developing resources like the Farasa segmenter and contributions to major Arabic NLP conferences, Dr. Abdelali has played a pivotal role in establishing the infrastructure and community for Arabic language technology research.

• Mass Spectrometry
• Metabolomics
• Lipidomics
• +۵ مورد دیگر

Zheng-Jiang Zhu is a Professor at the Shanghai Institute of Organic Chemistry (SIOC), Chinese Academy of Sciences, within the Interdisciplinary Research Center on Biology and Chemistry (IRCBC) . Since 2013, he has led a research program focused on advanced mass-spectrometry technologies and their application to metabolomics, lipidomics, and systems biology. Education and Training PhD in Chemistry, University of Massachusetts Amherst, 2006–2011 BS in Chemistry, Nanjing University, 2002–2006 Post-doctoral Research Associate, Scripps Research Institute, 2011–2013 Research Interests Professor Zhu’s research integrates cutting-edge mass spectrometry , ion mobility spectrometry , and bioinformatics to address fundamental questions in metabolism , aging , and neurodegeneration . His group develops novel analytical workflows, software tools, and databases that enable high-throughput, high-coverage profiling of metabolites and lipids in complex biological systems. Specific areas of focus include: Stable-isotope tracing metabolomics for mapping metabolic fluxes across tissues Lipid transport and remodeling at subcellular resolution Machine-learning-assisted metabolite annotation and structural elucidation Biomarker discovery for early cancer diagnosis and aging interventions Scientific Output & Impact Over the past decade, Zhu has authored more than 78 peer-reviewed publications in leading journals such as Nature Communications , Analytical Chemistry , Science Advances , and Nature Chemistry . His 2025 papers alone address tissue-specific fatty-acid biosynthesis, AI-driven metabolite annotation, organellar lipid trafficking, and the anti-aging role of citrulline in macrophages. These works collectively advance both technological frontiers and biological understanding, positioning his laboratory at the forefront of integrated multi-omics research. Software & Databases AllCCS2 – A curated atlas of collision cross-section values for small molecules Met4DX – A unified data-processing platform for multidimensional untargeted metabolomics LipidIMMS Analyzer – Integrative tools for ion-mobility-based lipidomics MetFlow – Interactive workflow for metabolomics data cleaning and differential analysis Collaborations & Funding Zhu’s research is supported by multiple national grants and extensive collaborations with clinicians and industry partners. His group routinely engages in peer review for over 20 high-impact journals, reflecting the community’s recognition of his expertise in analytical chemistry and systems biology.

• Logic and Philosophy of Logic
• Linguistic Communication
• 19th Century German Philosophy
• +۸ مورد دیگر

Jens Lemanski is an Associate Professor at the Philosophical Seminar of the University of Tübingen, with part-time roles as an Adjunct Professor at the University of Münster and a Researcher at Fernuniversität Hagen. His research spans Logic and Philosophy of Logic , Linguistic Communication , 19th Century German Philosophy , and Visualization in Mathematics . His work focuses on the historical and philosophical foundations of logic diagrams, including projects like Logic Diagrams in Kantianism (Fritz-Thyssen-Stiftung) and Gestures and Diagrams in Visual-Spatial Communications (DFG-priority programme). He has contributed extensively to understanding the evolution of logic from antiquity to modernity, emphasizing the role of diagrams in Euler-type systems , Byzantine logic , and Kantian thought . Lemanski co-edits Historia Logicae (College Publications) and serves as a Book Review Editor for History and Philosophy of Logic . His recent publications analyze the interplay between transcendental philosophy , formal logic , and AI-driven multimodal communication . He holds a PhD from Johannes Gutenberg University Mainz (2011) and contributes to conferences like Diagrammatic Representation and Inference (2024).

Tobias Hallmenمشاهده پروفایل

پژوهشگر

• Multimodal Conversation Analysis
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Tobias Hallmen is a Researcher at the University of Augsburg 's Chair of Human-Centered Artificial Intelligence within the Faculty of Applied Computer Science . His work focuses on multimodal conversation analysis using machine learning and artificial intelligence in psychotherapy and medical/educational training contexts. Research interests include: Automated evaluation of conversational quality through multimodal data (audio, video, text) AI-based assessment systems for therapy sessions and parent-teacher interviews Development of real-time feedback mechanisms for skill improvement Integration of behavioral signal processing and empathy modeling Recent publications demonstrate expertise in vocal burst analysis , emotional mimicry prediction , and multimodal foundation models for behavioral annotation. Key technical domains: deep learning architectures , cross-modal data correlation , and computer vision applications . The Chair team under Prof. Dr. Elisabeth André currently includes 23 members with 10 projects active, including TherapAI (psychotherapy analysis) and KodiLL (medical training systems). Tobias Hallmen's work particularly addresses speaker classification , reception signal analysis , and remote physiological measurement techniques like video-based heart rate detection .

Daniel Sonntagمشاهده پروفایل

استاد

• Intelligent User Interfaces
• Natural Language Processing
• Information Retrieval
• +۷ مورد دیگر

Prof. Dr.-Ing. Daniel Sonntag serves as Scientific Director of Interactive Machine Learning at the German Research Center for Artificial Intelligence (DFKI) and holds an Endowed Chair of Applied Artificial Intelligence at University of Oldenburg. He leads multiple significant research initiatives including No-IDLE (Interactive Deep Learning Enterprise), MASTER (Mixed reality ecosystem for teaching robotics), Ophthalmo-AI (intelligent eye care diagnostics), pAItient (AI applications in medicine), and XAINES (explainable AI with narratives). His research spans intelligent user interfaces, natural language processing, information retrieval, dialogue systems, common-sense modeling, and explainable machine learning methods for cognitive computing. His work particularly focuses on multimodal-multisensor interfaces for medical and health systems and Industry 4.0 applications. His research has had industrial impact, with SmartWeb's multimodal interaction design forming the technical infrastructure for Apple's Siri. His recent publications demonstrate strong trends in medical AI applications, particularly in eye tracking, radiology report generation with enhanced interpretability, and interactive federated learning frameworks. His work consistently bridges theoretical AI advances with practical medical and industrial applications. German High Tech Champion Award (2011) As a principal investigator, Sonntag leads national and European projects funded by the Federal Ministry of Education and Research, Federal Ministry for Economic Affairs and Energy, Federal Ministry of Health, and Horizon Europe. He has supervised numerous researchers including Michael Barz, Jan Zacharias, and Alexander Prange across multiple projects. His research group has successfully transitioned technologies to industry, with former researchers joining companies like Apple, Wacom, and Semvox. Sonntag's laboratory work centers on the Interactive Machine Learning department at DFKI, with strong connections to medical institutions and industry partners. His team develops cognitive assistance systems, mixed reality applications for smart factories, and AI-based diagnostic support tools, particularly in ophthalmology and radiology.

• AI Ethics
• Fairness in AI
• Text-to-Image Generation
• +۵ مورد دیگر

Felix Friedrich is a researcher at the Technical University of Darmstadt, Department of Computer Science, focusing on AI ethics, fairness, and safety in generative models. He collaborates extensively with Professor Kristian Kersting and other researchers in the AI field, contributing significantly to understanding and mitigating biases in text-to-image generation systems and multilingual AI models. His research interests center on ethical AI development, with particular emphasis on identifying and addressing gender stereotypes, cultural biases, and safety concerns in generative models. Friedrich's work bridges technical AI development with societal implications, examining how language and cultural contexts influence model behavior across different populations. His approach combines rigorous empirical evaluation with practical mitigation strategies to create more responsible AI systems. Friedrich's publication record shows a strong trend toward addressing critical challenges in AI safety and fairness, particularly in multimodal and multilingual contexts. His recent work develops benchmarks and evaluation frameworks to systematically assess issues like cross-linguistic safety gaps, cultural bias magnification, and emotional recognition accuracy in synthetic data. These contributions provide valuable tools for the research community to build more equitable and reliable AI systems. His work has contributed to several collaborative research initiatives focused on AI safety and ethics, working with international teams to develop comprehensive evaluation frameworks and mitigation strategies for emerging AI technologies. Friedrich's research has practical implications for AI developers seeking to create more responsible systems that account for diverse cultural contexts and user needs.

Stefan Rothمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Semantic Scene Understanding
• +۳ مورد دیگر

Stefan Roth is a Professor of Computer Science at Technische Universität Darmstadt, where he leads the Visual Inference Lab. His research focuses on statistical models for visual inference problems, with expertise in computer vision, machine learning, and deep learning applications. Specific areas include semantic scene understanding, image motion estimation, and probabilistic models for low-level vision tasks. Key research areas: Semantic scene understanding and segmentation Optical flow and scene flow estimation Deep learning architectures for vision Probabilistic modeling in computer vision Video analysis and understanding Professor Roth's recent publications explore transformer-based scene graph generation, unsupervised segmentation techniques, and foundation models for depth estimation. His work demonstrates strong interdisciplinary connections between computer vision, machine learning, and autonomous systems, with applications in urban scene understanding and intelligent transportation systems.

• Neural Machine Translation
• Computational Linguistics
• Natural Language Processing
• +۳ مورد دیگر

Yang Liu is a researcher at the Institute of Computing Technology (ICT), Tsinghua University , and affiliated with the Beijing Academy of Artificial Intelligence . His work spans computational linguistics, machine translation, and dialogue systems, with additional collaborations in medical NLP and cross-lingual knowledge representation. Doctorate from Purdue University Collaborations with Microsoft, Amazon, and Facebook researchers Research focuses on Neural Machine Translation (NMT) advancements through: Self-supervised alignment and regularization Document-level context integration Robust translation frameworks for low-resource scenarios Adapter-based pluggable models Recent works extend to conversational AI, including: Subjective knowledge integration in task-oriented dialogue Context representation analysis for open-domain dialogue Synthetic conversation dataset generation Key contributions include: THUMT: Open-source NMT toolkit Earth Mover's Distance for lexicon induction Context Gates for translation fluency Publications demonstrate continuous engagement with top-tier conferences including ACL, EMNLP, and COLING, collaborating with prominent figures like Maosong Sun and Qun Liu.

Alexander Asterothمشاهده پروفایل

استاد

• Machine Learning
• Stochastic and Probabilistic Learning Methods
• Surrogate Modelling
• +۵ مورد دیگر

Prof. Dr. Alexander Asteroth is a Professor of Computer Science at the Department of Computer Science , Hochschule Bonn-Rhein-Sieg (H-BRS). His work bridges Machine Learning , Surrogate Modeling , and Aerodynamic Analysis through interdisciplinary collaborations with the Institute of Technology, Resource Conservation, and Energy Efficiency (TREE) . Project leadership roles in GARRULUS (drone-based reforestation), eTa (sustainable mobility), and ELaBoR (EV charging infrastructure). Research themes: Quality Diversity Algorithms for design exploration, Bayesian Optimization , and AI in Sports Science . His publications (2017–2025) demonstrate expertise in evolutionary computation , generative models , and human-AI co-creativity . Collaborations with industry partners like GKN Driveline and academic peers (Houben, Sebastian; Hagg, Alexander) underscore his applied research focus on energy-efficient systems and sports performance modeling.

• Information Retrieval
• Large Language Models
• Parameter Efficient Fine-Tuning
• +۵ مورد دیگر

Marco Braga is a researcher focused on information retrieval, large language models, and parameter-efficient fine-tuning techniques. His work spans personalized community question answering systems, synthetic data generation, and adapter-based model optimization. Recent research interests include: Zero-shot learning and task arithmetic for LLMs Context-aware personalization in retrieval systems Hybrid neural-symbolic approaches for structured reasoning Telecommunications domain modeling Key article trends show expertise in: Adapter modules and low-rank parameter optimization Community question answering personalization Synthetic dataset creation for training LLMs Model efficiency in resource-constrained settings Collaborations include Gabriella Pasi, Pranav Kasela, and Alessandro Raganato. His work appears in SIGIR, COLING, WI-IAT, and IIR conferences, with notable Zenodo dataset contributions.

Tim Wilhelm Nattkemperمشاهده پروفایل

استاد

• Bioinformatics
• Bioimage Informatics
• Computer Vision
• +۶ مورد دیگر

Prof. Dr.-Ing. Tim Wilhelm Nattkemper leads the Biodata Mining Group at the Faculty of Engineering , Universität Bielefeld , while holding affiliations with the Center for Biotechnology (CeBiTec) and the Institute for Bioinformatics Infrastructure . His work bridges bioinformatics with marine environmental monitoring , focusing on machine learning and computer vision applications. The group specializes in multivariate bioimage analysis , developing platforms like BioIMAX for web-based high-dimensional data exploration. Research spans from MALDI imaging to deep-sea megafauna classification , integrating information visualization and web technologies . Recent projects address seafloor macrolitter monitoring , coral stress response analysis , and self-supervised learning for diatom classification. Their 15 most recent publications (2023-2025) highlight advancements in marine imaging , automated annotation systems , and AI-driven biodiversity assessment , particularly in polymetallic nodule fields. The group also tackles technical challenges like data imbalance in marine image classification and FAIR data principles implementation. As module responsible for courses like Information Visualization and Introduction to Bioinformatics , Nattkemper contributes to academic training in bioinformatics and data science . His interdisciplinary collaborations span physics , chemistry , and ecology within Bielefeld's Material World strategic research area.

Franziska Klemsteinمشاهده پروفایل

پژوهشگر

• Monument Preservation
• Digital Humanities
• GDR Architectural History
• +۳ مورد دیگر

Dr. Franziska Klemstein is an active researcher specializing in monument preservation and digital humanities, with significant contributions to understanding heritage practices in the German Democratic Republic (GDR) era. Her work critically bridges historical scholarship and computational methods, particularly through the development of specialized datasets for document analysis and image retrieval in cultural heritage contexts. Her research focuses on: Monument preservation systems under socialist regimes Ethical implementation of AI in heritage documentation Diversity and inclusion challenges in conservation practices Classification methodologies for historical inventories Digital reconstruction of contested heritage sites Gender dynamics in preservation professions Analysis of Klemstein's recent publications reveals a methodological evolution from historical analysis toward technical innovation, with increasing emphasis on dataset creation (DHMTIC, DHREAAL, TexBiG) and evaluation frameworks for heritage computing. Her work consistently addresses the tension between digital efficiency and critical heritage values, particularly regarding sustainable AI implementation and inclusive documentation practices. While specific awards remain undocumented in the provided materials, her editorial leadership in significant volumes like "On the Way to a Place of Remembrance" (2024) demonstrates recognized expertise. Klemstein's collaborative projects with researchers like Tschirschwitz and Schmidgen indicate active participation in interdisciplinary teams focused on digital heritage solutions, though institutional affiliations and grant details aren't specified in the current text.