جست‌وجوی استاد و پژوهشگر

• Computational Linguistics
• Natural Language Processing
• Text Technology
• +۳ مورد دیگر

Yu Wang is a Lecturer at Bielefeld University's Faculty of Linguistics and Literary Studies, affiliated with the Computational Linguistics department. He is associated with the research project Monitoring the understanding of explanations under Prof. Dr. Hendrik Buschmeier, focusing on digital linguistics and text technology. While his institutional affiliation highlights computational linguistics, his Google Scholar publications reveal interdisciplinary work in optimization algorithms, graph neural networks, and machine learning applications. Research Interests: Yu Wang's work bridges computational linguistics with advanced algorithmic techniques. His publications explore Multi-objective optimization Graph neural network architectures Swarm intelligence applications Evolutionary computation methods Sparse learning systems Biologically-inspired algorithms Labs & Projects: Yu Wang contributes to the Digital Linguistics Group at Bielefeld University, specifically working on explainability monitoring in explanatory processes. His technical publications suggest collaborations with researchers in optimization and machine learning domains.

• Phonetics
• Speech Technology
• Machine Learning
• +۱ مورد دیگر

Jana Wiechmann is a Scientific Associate at the Department of Linguistics within the Faculty of Linguistics and Literary Studies at Bielefeld University . She is actively involved in phonetics and speech technology research as part of the TRR 318 Subproject C06 under the collaborative research framework at the university. Research Interests: Her work focuses on the intersection of phonetics , speech technology , and machine learning , particularly in modeling and disentangling acoustic voice characteristics. This aligns with Bielefeld University's strategic research areas in Transcending Boundaries and the Socio-Technical World . Recent Publications highlight her contributions to voice synthesis, hoarseness modeling, speaker representation interpretability, and explainability frameworks for voice analysis. Her research integrates technical and clinical perspectives through the Center for Cognitive Interaction Technology (CITEC) .

• Signal Processing
• Biomedical Signal Processing
• Image Reconstruction
• +۱۲ مورد دیگر

Rabab K. Ward is a Professor in the Department of Electrical and Computer Engineering at the University of British Columbia, Faculty of Applied Science, Vancouver, Canada. She is a leading figure in signal processing with extensive contributions to biomedical engineering, image reconstruction, and machine learning. She served as President of the IEEE Signal Processing Society (2016–2017) and continues to be actively involved in research and leadership. Her research focuses on signal processing , biomedical signal analysis , compressed sensing , medical image fusion and reconstruction , and deep learning for healthcare applications . She has pioneered methods in EEG and CT image processing, epileptic seizure detection, and 3D human pose estimation. The recent publications highlight her work in 3D face and human modeling , GAN-based forensics , brain-computer interfaces , and medical video analysis . Her research integrates advanced machine learning with practical engineering solutions for clinical and robotic systems. Her scientific awards include being an IEEE Fellow , Distinguished Lecturer of the IEEE Signal Processing Society , and serving as President of the IEEE Signal Processing Society . She has advised numerous students and researchers in areas such as compressed sensing , EEG signal processing , image reconstruction , and deep learning . Her collaborative work includes significant grants and projects in biomedical engineering , neurotechnology , and autonomous systems . Dr. Ward is associated with research labs and teams focused on signal processing for healthcare , medical imaging , and intelligent systems at UBC, fostering interdisciplinary collaboration in engineering and medicine.

• Artificial Intelligence
• Machine Learning
• Human-Computer Interaction
• +۲ مورد دیگر

Dr. Patrick Bieker is a researcher at the Faculty of Mathematics , Bielefeld University, affiliated with the Collaborative Research Center/Transregio 358 "Integral Structures in Geometry and Representation Theory" and Transregio 318 "Constructing Explainability" . His work focuses on interdisciplinary approaches to mathematical structures and AI explainability. Research areas include: Co-construction of explanations in AI systems Contextual factors in algorithmic transparency Mathematical foundations for interdisciplinary applications Contact: Office UHG V5-206, Phone +49 521 106-5032, Universität Bielefeld, Germany.

• Computational Linguistics
• Second Language Acquisition
• Artificial Intelligence in Education
• +۱۲ مورد دیگر

Prof. Dr. Walt Detmar Meurers is a leading academic in computational linguistics and artificial intelligence in education. He currently serves as Head of the Language and AI in Education lab at the Leibniz-Institut für Wissensmedien (IWM) and is a Full Professor in the Department of Linguistics at Eberhard Karls University of Tübingen . He has been a faculty member since 2008 and continues to lead major research initiatives bridging AI and educational practice. His research focuses on the intersection of computational linguistics and empirical educational science. Key areas include second and academic language acquisition, linguistic complexity analysis, intelligent tutoring systems, adaptive learning technologies, learner corpus research, and the development of AI-driven tools for real-world education. He emphasizes ecologically valid field studies and randomized controlled trials to ensure practical impact. His recent publications reveal a strong trend toward developing explainable, adaptive, and task-oriented AI systems for language education. These works span NLP applications in EFL, readability modeling, pedagogical agent design, and learning analytics dashboards for teachers. The research consistently integrates linguistic theory with machine learning to support real classroom needs. Member, Steering Board of LEAD Graduate School and Research Network Head, Language Intersection 4 in LEAD Consultant, EFCAMDAT project Scientific Advisory Board, CATALPA Scientific Advisory Board, SZHB (Language Centre of Universities in Bremen) English Language Teaching Expert Advisory Board, Oxford University Press Steering Committee, Heritage Language Consortium Meurers advises on multiple national and international projects and has co-organized key workshops such as AALL'09 and IICALL. He has led numerous funded projects including FeedBook , KANSAS , Interact4School , and the current AIM Project AITeach and WoLKE . His work demonstrates sustained leadership in both research and application. He leads research groups focused on intelligent CALL, linguistic modeling, and the integration of syntax and information structure within HPSG frameworks. His lab develops tools like Interact4School , DiDi , FLAIR , and TAGARELA , emphasizing real-life deployment and teacher collaboration.

• Medical Image Analysis
• Computer Vision
• Machine Learning
• +۴ مورد دیگر

Vajira Thambawita is a researcher at the University of Oslo's Department of Informatics, specializing in medical image analysis and AI-driven healthcare solutions. With over 90 publications since 2019, their work spans gastrointestinal endoscopy, reproductive medicine technology, and multimedia systems for clinical applications. Research focuses on medical image segmentation (polyp detection, sperm tracking), anomaly detection in time-series data, and multimodal analysis for clinical decision support. Key projects include the Medico Multimedia Task at MediaEval (sperm tracking), VISEM-Tracking dataset, and ImageCLEFmedical challenges for GI tract analysis. Their work bridges computer vision with clinical practice through collaborations with Oslo University Hospital and Simula Research Laboratory. Recent publications demonstrate strong trends in generative AI for medical data augmentation (SinGAN-Seg, PolypConnect), explainable AI for clinical validation , and multimodal integration (SoccerNet-Echoes). The research consistently targets real-world clinical problems with emphasis on transparency and robust evaluation. Thambawita actively contributes to major benchmark challenges including ImageCLEF, Medico, and Medical AI competitions, serving as organizer and participant in advancing evaluation standards for medical AI systems.

• Robotics
• Artificial Intelligence
• Human-Robot Interaction
• +۳ مورد دیگر

Jim Tørresen is a senior researcher at the Department of Informatics , University of Oslo , with a focus on Robotics , Artificial Intelligence , and Human-Robot Interaction . His work spans autonomous systems, machine learning applications, and ethical considerations in AI. Current Affiliation : University of Oslo, Norway Research Interests : Robotics for elderly healthcare and assistive technologies Machine learning in multimodal sensing and personalization Explainable AI and ethical user modeling Adaptive control systems and motion planning Embodied intelligence in creative domains like dance Recent Article Trends include AI-driven healthcare monitoring, social robotics for senior engagement, reinforcement learning in constrained environments, and computational creativity applications. His work emphasizes privacy preservation , real-time interaction , and human-centered design . Collaborations : Frequent collaborations with researchers like Kai Olav Ellefsen , Diana Saplacan Lindblom , and Charles Martin across EU projects and robotics conferences.

• Computer Vision
• Multimodal Learning
• Action Recognition
• +۳ مورد دیگر

Hilde Kuehne is a Full Professor at the University of Tuebingen's Tuebingen AI Center with significant affiliations at MIT-IBM Watson AI Lab, Goethe University Frankfurt, and University of Bonn. Her research leadership spans computer vision, multimodal learning, and artificial intelligence, with emphasis on video understanding and foundational model development. She actively collaborates with IBM Research and MIT across multiple high-impact projects. Her research program focuses on critical challenges in visual intelligence: Developing explainability methods for Vision Transformers to enhance model transparency Creating robust multimodal frameworks for audio-visual alignment and spatio-temporal grounding Addressing representation biases in video benchmarks through structured debiasing approaches Advancing zero-shot recognition capabilities using large language models Exploring associative memory mechanisms for next-generation foundation models Analysis of her 15 most recent publications (2024-2025) reveals three dominant research thrusts: (1) Multimodal foundation models showing strong emphasis on fine-grained audio-visual synchronization, (2) Explainable AI techniques targeting Vision Transformer interpretability, and (3) Systematic debiasing methodologies for video understanding benchmarks. Her work consistently bridges theoretical innovation with practical applications, particularly in instructional video analysis and training-free recognition systems. Key scientific recognition includes: NeurIPS 2024 Oral Presentation for "Convolutional Differentiable Logic Gate Networks" (top 2% acceptance rate) Professor Kuehne mentors a productive research group with notable PhD students including Walid Bousselham (ICCV 2025 first-author), Sivan Doveh (ICCV 2025 first-author), and Nina Shvetsova (CVPR 2025 first-author). Her research is supported through strategic partnerships with IBM Research and MIT, evidenced by consistent co-authorship on high-impact publications. She serves on the Scientific Advisory Board of the Carl-Zeiss-Foundation and contributed to Germany's 2024 Commission of Experts for Research and Innovation annual report. She leads research initiatives within the Tuebingen AI Center and MIT-IBM Watson AI Lab, while co-organizing influential workshops including the 3rd Workshop on What is Next in Multimodal Foundation Models (CVPR 2025) and New Frontiers in Associative Memories (ICLR 2025), demonstrating her leadership in shaping next-generation multimodal AI research directions.

Stefan Herzogمشاهده پروفایل

پژوهشگر

• Decision-Making
• Artificial Intelligence
• Collective Intelligence
• +۶ مورد دیگر

Stefan Herzog is a Senior Research Scientist and Head of the Research Area Boosting Decision-Making at the Max Planck Institute for Human Development in Berlin. He leads interdisciplinary projects integrating psychology, artificial intelligence, and computational social science to address global challenges like misinformation, climate change, and AI's societal impact. PhD in Psychology (2009, University of Basel) M.Sc. in Psychology (2005, University of Basel) B.Sc. in Psychology (2003, University of Basel) His research focuses on boosting human decision-making through: Hybrid human-AI collective intelligence Strengthening resilience against disinformation Behavioral interventions in digital environments Transparent, non-manipulative policy design Self-nudging strategies Methodological innovation in social science Recent publications emphasize AI ethics , digital sovereignty , and democratic resilience . He has secured significant funding from the EU Horizon program, DFG, and Volkswagen Stiftung. Scientific recognition includes the Hillel Einhorn New Investigator Award (2010).

Michèle Finckمشاهده پروفایل

استاد

• Law and Artificial Intelligence
• EU Data Law
• AI Regulation
• +۵ مورد دیگر

Michèle Finck is Professor of Law and Artificial Intelligence at the University of Tübingen and Director of the CZS Institute for Artificial Intelligence and Law. She previously held positions at the University of Oxford, the London School of Economics, and the Max Planck Institute for Innovation and Competition, and has served as a Visiting Professor at LUISS University and an Affiliated Fellow at University College London. Her research focuses on the intersection of law and artificial intelligence, with particular emphasis on EU data law. Her work explores critical areas including the use of AI by public administrations, explainability of AI systems, access to data for environmental purposes, regulation of data intermediaries under the Data Governance Act, and the concepts of data controller and personal data under the GDPR. Finck's publications reveal a strong trend toward analyzing regulatory frameworks for emerging technologies, with increasing focus on the EU AI Act implementation challenges and interdisciplinary approaches to technology governance. Her recent work demonstrates growing attention to cybersecurity aspects of AI systems, particularly in healthcare contexts, and the evolving relationship between environmental sustainability and data regulation. Honorable Mentions as part of the JEL Richard Macrory Prize for Best Article 2023 Member of the Council of Europe's ad hoc Committee on Artificial Intelligence Member of the European Commission's Blockchain Observatory and Forum Advisory roles for national institutions, the European Commission, and the European Parliament Professor Finck actively supervises a research team including postdocs and PhD candidates working on diverse aspects of AI and law. She is currently writing a book on the EU Artificial Intelligence Act for Oxford University Press (2025) and organizing an International PhD Summer School in Artificial Intelligence and EU Law (May 2025). Her research group collaborates with the CZS Institute for Artificial Intelligence and Law and the Cluster of Excellence 'Machine Learning in Science' at Tübingen.

Bernhard Reuterمشاهده پروفایل

پژوهشگر

• Statistical Machine Learning
• Bioinformatics
• Medical Informatics
• +۵ مورد دیگر

Bernhard Reuter is a researcher at the University of Tübingen in the Department of Computer Science , focusing on machine learning methods for biomedical data . He develops tools for drug-resistance prediction in tuberculosis and HIV, and created the Generalized Perron Cluster Cluster Analysis (G-PCCA) for modeling non-equilibrium systems. Education Dr. rer. nat. in Theoretical Physics (2018), University of Kassel Dipl.-Phys. in Physics (2013), University of Kassel Research Highlights : His work bridges machine learning with biomolecular dynamics , particularly in drug resistance prediction and non-equilibrium system modeling . Publications in Nature Methods and Journal of Chemical Physics demonstrate his technical contributions. Awards GDCH travel scholarships (2014, 2016) CINSAT poster award (2013) Doctoral scholarship (2013) Studienstiftung scholarship (2006) He has contributed to open-source software projects like pyGPCCA and COmic , and collaborated with institutions including the Max Planck Institute for Biological Cybernetics .

• Operations Research
• Supply Chain Management
• Artificial Intelligence
• +۶ مورد دیگر

Ajay Kumar is a prominent researcher and academic with extensive contributions across multiple domains including Operations Research, Artificial Intelligence, Blockchain, and Biometric Recognition. His work spans a wide range of topics, from digital transformation in supply chains to machine learning applications in healthcare and software engineering. He has collaborated with numerous scholars and published extensively in high-impact journals and conferences. Research Interests: Operations Research, AI in Healthcare, Blockchain Applications, Biometric Recognition, Software Reliability, Digital Transformation Publications: Kumar has authored numerous publications in journals like Annals of Operations Research , IEEE Transactions on Engineering Management , and SN Computer Science , as well as conferences such as CVPR and IC3I. Collaborations: Kumar has worked with leading experts like Kim Hua Tan, Shivam Gupta, Seema Bawa, and others, contributing to interdisciplinary research in supply chain, data science, and IoT. Key Contributions: His research includes innovative frameworks such as blockchain-enabled supply chains, hybrid AI models for diabetes prediction, and scalable tools for genomic data analysis. Emerging Trends: Kumar’s recent work focuses on explainable AI, fake news detection, and integrating IoT with blockchain for secure and efficient systems across health, logistics, and UAV communication.

• Explainable AI
• Artificial Intelligence
• Deep Learning
• +۵ مورد دیگر

Wojciech Samek is a Professor in the Department of Electrical Engineering and Computer Science at the Technical University of Berlin and jointly heads the Department of Artificial Intelligence at Fraunhofer Heinrich Hertz Institute (HHI) in Berlin, Germany. Dr. Samek received his Dr. rer. nat. degree with distinction (summa cum laude) from the Technical University of Berlin in 2014 after studying computer science at Humboldt University of Berlin, Heriot-Watt University, and University of Edinburgh. During his studies, he received scholarships from the German Academic Scholarship Foundation and the DFG Research Training Group GRK 1589/1, and was a visiting researcher at NASA Ames Research Center. Professor Samek's research focuses on Explainable AI, where he has made significant contributions to interpreting, explaining, and visualizing deep learning models. His work also extends to neural network compression and federated learning, addressing critical challenges in transparency, efficiency, and privacy in AI systems. His research spans both theoretical foundations and practical applications of artificial intelligence. Senior editor of IEEE TNNLS Editorial board member of PLoS ONE and Pattern Recognition Elected member of the IEEE MLSP Technical Committee Member of scientific advisory board of IDEAS NCBR 2020 Pattern Recognition Best Paper Award Multiple best paper awards Part of the expert group developing the ISO/IEC MPEG-17 NNR standard Recognized for "Highly Cited Papers" (top 1%) in Engineering Professor Samek has co-authored over 150 peer-reviewed publications and serves as associated faculty at BIFOLD – Berlin Institute for the Foundation of Learning and Data and the ELLIS Unit Berlin. He has edited influential books including "Explainable AI: Interpreting, Explaining and Visualizing Deep Learning" (2019) and "xxAI – Beyond explainable AI" (2022), and regularly organizes workshops and tutorials on cutting-edge AI topics. His dual appointments bridge academic research at Technical University of Berlin with applied research at Fraunhofer HHI, positioning him at the forefront of AI innovation in both theoretical and practical domains.

• Hardware Design
• FPGA Programming
• Parallel Computing
• +۵ مورد دیگر

Satnam Singh is a Professor at Newcastle University's School of Electrical and Electronic Engineering, UK. With a research career spanning over three decades from 1989 to present, Singh has established himself as a leading expert in hardware design, FPGA programming, and parallel computing systems. His research interests focus on hardware-software co-design , reconfigurable computing , and functional programming applications for hardware design. Singh has pioneered work in using functional languages like Haskell for hardware description and verification, particularly through his contributions to the Lava hardware description language. His work bridges theoretical computer science with practical hardware implementation challenges. Analysis of his publication history reveals a clear evolution from early work on formal verification and FPGA design in the 1990s, through substantial contributions to parallel programming models in the 2000s, to more recent applications of machine learning techniques in diverse domains including cheminformatics and sensory systems. His 2022-2025 publications demonstrate continued innovation in specialized processor programming, AI applications for olfactory systems, and health hazard classification using deep learning. Singh has maintained extensive collaborations throughout his career, notably with Krishna R. Pattipati (15 joint publications), Anuradha Kodali (8 publications), and David J. Greaves (5 publications), reflecting his ability to bridge theoretical computer science with practical engineering applications. His work spans multiple prestigious venues including FPGA, FCCM, ICFP, and IEEE Transactions on Systems, Man, and Cybernetics, demonstrating both theoretical depth and practical impact across computer architecture, programming languages, and applied machine learning domains.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۹ مورد دیگر

Han Chen is an active academic researcher in the fields of artificial intelligence, machine learning, computer vision, and biomedical informatics, with extensive publications in top-tier venues such as CVPR, ICLR, IEEE Transactions, and Bioinformatics. The research spans diverse applications including medical image analysis, deepfake detection, federated learning, and multimodal affective computing. The research interests of Han Chen include artificial intelligence, machine learning, computer vision, medical image analysis, graph neural networks, and cybersecurity. The work emphasizes deep learning architectures, multimodal fusion, and robustness in AI systems, particularly in healthcare and security applications. Key themes include disentangled representation learning, contrastive learning, and transformer-based models. The recent articles demonstrate a strong trend toward multimodal AI, with applications in medical diagnostics (e.g., mammography, nasopharyngeal imaging), trustworthy AI (e.g., deepfake detection, backdoor defense), and efficient learning paradigms (e.g., federated learning, weak supervision). There is a consistent focus on enhancing model interpretability, robustness, and real-world applicability across domains. Scientific Awards: No awards explicitly mentioned in the provided text. Advising and Grants: There is no explicit information about students or advising. However, the high volume of collaborative research suggests active involvement in research teams and likely supervision of graduate students. Funding sources are not mentioned, but the scope of work implies support from national or institutional research grants in AI and health informatics. Labs and Teams: While no specific lab or team is named, the collaborative nature of the work—especially with researchers in medical imaging and federated learning—suggests membership in a multidisciplinary AI research group, possibly affiliated with a medical school or engineering institute.