جست‌وجوی استاد و پژوهشگر

• Clinical Natural Language Processing (NLP)
• Medical Informatics
• Machine Learning
• +۳ مورد دیگر

Jens-Michalis Papaioannou is a prominent Researcher in clinical natural language processing (NLP) and medical informatics, with extensive publications in top-tier venues like ACL, LREC, and EMNLP. His work focuses on improving clinical decision support systems through advanced machine learning techniques. 2024 : Revisiting clinical outcome prediction for MIMIC-IV with biomedical transformers 2023 : Developing MEDBERT.de for German medical NLP and MedAlpaca conversational AI 2022 : Introducing ProtoPatient for interpretable diagnosis prediction 2021 : Creating self-supervised knowledge integration frameworks for admission note analysis His research spans seven major themes : Clinical outcome prediction from admission notes Cross-lingual knowledge transfer in medical NLP Prototypical network applications Data drift analysis in longitudinal datasets Knowledge integration techniques Model optimization for healthcare LLM interpretability frameworks He has collaborated with Wolfgang Nejdl, Alexander Löser, and Betty van Aken on 13+ publications , with over 445 citations. Notable contributions include: Novel patient similarity modeling approaches ICD code hierarchy integration methods Multilingual clinical model strategies Adversarial robustness analysis Medical conversational AI frameworks

• Natural Language Processing
• Computational Linguistics
• Speech Processing
• +۵ مورد دیگر

Philippe Blache is a senior researcher at the Laboratoire Parole & Langage (LPL), a joint research unit of the French National Centre for Scientific Research (CNRS) and Aix-Marseille University. With a career spanning several decades, he has established himself as a leading figure in computational linguistics, natural language processing, and conversation analysis. His work bridges theoretical linguistics with practical applications in language technology. His research focuses on several key areas: Natural language processing and computational linguistics Speech and conversational systems Multimodal interaction analysis Linguistic annotation and corpus development Cognitive modeling of language processing Blache's recent work (2020-2025) has centered on developing methods to evaluate spoken language features in conversational models, studying common ground instantiation through multimodal signals (including brain activity), and investigating how language models process complex linguistic phenomena. His research often involves creating and analyzing large multimodal corpora that capture not just verbal content but also nonverbal cues, prosody, gestures, and neuro-physiological signals. His publication record shows consistent high-quality output in top-tier venues, with numerous papers in LREC, COLING, and SIGDIAL proceedings. His work demonstrates a strong interdisciplinary approach, combining insights from linguistics, computer science, cognitive science, and neuroscience to advance our understanding of language processing. Among his notable contributions is the BrainKT corpus project, which represents an innovative approach to studying the cognitive and neural underpinnings of conversation through simultaneous recording of audio, video, and EEG signals during natural interactions. Blache has received recognition through his sustained research productivity and leadership in major projects. His collaborations span multiple institutions and disciplines, reflecting the interdisciplinary nature of his work and its broad relevance across fields studying human communication.

• Medical Natural Language Processing
• Clinical Text Analysis
• Radiology Informatics
• +۵ مورد دیگر

Chaitanya Shivade is a prominent researcher specializing in medical natural language processing with significant contributions to clinical text analysis, radiology informatics, and behavioral health documentation. His work bridges computational linguistics and healthcare applications, focusing on practical solutions for clinical documentation challenges. Shivade's research spans multiple critical areas: developing evaluation frameworks for behavioral therapy notes (TN-Eval), creating shared tasks for medical summarization (MEDIQA), advancing visual dialog systems for radiology, and pioneering synthetic clinical note generation. He has made substantial contributions to textual inference in clinical domains through the MedNLI dataset and has explored fundamental linguistic challenges like negation detection and gradable term analysis in medical text. As a workshop organizer for the NLP for Medical Conversations series, he has helped shape community standards and foster collaboration. His publication record demonstrates consistent leadership in applying NLP to real-world healthcare problems, with particular emphasis on evaluation methodologies, dataset creation, and practical clinical applications. Shivade has collaborated extensively with medical professionals and researchers across institutions to ensure clinical relevance of his technical work. Organized MEDIQA shared tasks (2019, 2021) Co-organized NLP for Medical Conversations workshops (2019, 2020) Developed TN-Eval framework for therapy note quality assessment Created MedNLI dataset for clinical textual inference Pioneered synthetic clinical note generation approaches His work consistently addresses the tension between clinical utility and technical innovation, with growing emphasis on evaluating LLM performance in healthcare contexts. The progression from foundational clinical NLP techniques to complex evaluation frameworks demonstrates his evolving research trajectory toward ensuring reliable AI deployment in medical settings.

• Human-Computer Interaction
• Virtual Reality
• Augmented Reality
• +۱۷ مورد دیگر

Prof. Dr. Antonio Krüger is the CEO of the German Research Center for Artificial Intelligence (DFKI) and Director of the Ubiquitous Media Technology Lab (UMTL) at the Saarland Informatics Campus in Saarbrücken, Germany. He is a leading researcher in human-computer interaction, virtual and augmented reality, multimodal interfaces, and intelligent systems. His research interests span a broad range of topics including virtual reality (VR), augmented reality (AR), gesture interaction, haptic feedback, EEG and eye tracking, gamification, cognitive load estimation, brain-computer interfaces, and smart environments. His work emphasizes user-centered design, perceptual illusions, and real-world applications in domains such as automotive, healthcare, and education. His recent publications (2023–2024) demonstrate a strong focus on advancing VR interaction through techniques like hand redirection, synthetic gesture generation for vehicles, implicit intent recognition using multimodal sensing, and personalized object referencing. These works reflect trends toward seamless, natural, and adaptive interfaces that leverage AI, perception, and real-time feedback. Prof. Krüger supervises a large team of researchers and students, including Mansi Sharma, Amr Gomaa, André Zenner, and Martin Feick, indicating active mentorship and collaborative research leadership. His lab, UMTL, develops innovative technologies in immersive environments, wearable systems, and intelligent user assistance, contributing to both academic knowledge and practical applications.

Julia Maria Strußمشاهده پروفایل

استاد

• Information Retrieval
• Data Science
• Natural Language Processing
• +۴ مورد دیگر

Prof. Dr. Julia Maria Struß is a Professor of Applied Data Science at the Department of Information Science of Potsdam University of Applied Sciences. She serves as Program Director and Internship Coordinator for the Information and Data Management (B.A.) program and is a member of the Departmental Council. Her work bridges technical rigor with societal impact, focusing on information retrieval, text mining, and ethical data practices. University: Potsdam University of Applied Sciences Department: Information Science Academic Rank: Professor Email: julia.struss@fh-potsdam.de Research Interests center on Information Retrieval (technical and ethical dimensions), Data Science for Social Good (hate speech detection, fake news mitigation), and Computational Linguistics (subjectivity analysis, sentiment modeling). She specializes in deploying these methods for societal challenges like environmental sustainability and pandemic misinformation. Her publications emphasize fact-checking infrastructure , multilingual NLP , and dataset transparency , with recent work analyzing hate speech data licensing and urban data practices for rural stakeholders.

• Dataspaces
• Semantic Web Technologies
• Ontologies
• +۶ مورد دیگر

Johannes Theissen-Lipp is a senior scientific researcher at the Fraunhofer FIT Office Aachen with significant academic affiliations at RWTH Aachen University. He operates within the Department of Computer Science (Informatik 5) under the Faculty of Mathematics, Computer Science and Natural Sciences, working from the Databases and Information Systems (DBIS) group led by Professor Stefan Decker. His research focuses on semantic foundations of dataspaces with practical applications in manufacturing and mobility sectors. He earned his Computer Science degree from RWTH Aachen University, where he later conducted research at the Institute for Information Management in Mechanical Engineering (IMA) for nearly two years before joining the "Knowledge Pipelines" research group in December 2019. He successfully defended his PhD thesis titled "Semantic Foundations of Dataspaces" on September 25, 2024, completing his doctoral studies at RWTH Aachen. Theissen-Lipp's research spans semantic web technologies, ontologies, and data interoperability with particular expertise in RDF validation (SHACL), semantic mappings (RML), and FAIR data implementation within dataspaces. His work bridges theoretical foundations with industrial applications, especially in manufacturing contexts where data integration across organizational boundaries presents significant challenges. He has pioneered approaches for enhancing dataspaces through large language models and developing scalable validation techniques for distributed data environments. His publication record demonstrates consistent contributions to major semantic web venues including ESWC, SEMANTiCS, and dedicated workshops on dataspaces. As both author and editor (notably for the Second International Workshop on Semantics in Dataspaces proceedings), he has helped shape research directions in this emerging field. His recent work increasingly explores the intersection of traditional semantic technologies with contemporary AI approaches. Theissen-Lipp actively supervises multiple thesis projects including "FAIRification of Data Models in Manufacturing," "Leveraging Solid Pods for Sovereign Data Sharing in the Cultural Sector," and "Identification Techniques for Data-driven Feedback Loops in Manufacturing." He has taught the "Dataspaces Proseminar" in the summer semester of 2025, demonstrating his commitment to academic education alongside his research activities. As part of the Cluster of Excellence Internet of Production, he contributes to advancing data interoperability in industrial contexts. His professional presence extends to GitHub (JohannesLipp), LinkedIn, and ResearchGate, with a personal website providing additional details about his research portfolio and professional activities.

Tobias Bauerمشاهده پروفایل

پژوهشگر

• Neuroimaging
• Neurology
• Neuroradiology
• +۷ مورد دیگر

Dr. Tobias Bauer, MD, BSc, is a postdoctoral researcher and assistant physician at the University of Bonn, affiliated with the Department of Neuroradiology and the Clinic and Polyclinic for Epileptology. He is a member of the Translational Neuroimaging Working Group led by PD Dr. med. Theodor Rüber, under the directorship of Prof. Dr. med. Alexander Radbruch and Prof. Dr. med. Rainer Surges. His work bridges clinical practice and advanced neuroimaging research, focusing on autoimmune encephalitis and epilepsy. University: University of Bonn School: Medical Faculty Department: Department of Neuroradiology Position: Researcher Email: tobias.bauer@ukbonn.de Dr. Bauer studied nautical science at Wismar University, mathematics at FernUniversität Hagen, and human medicine at the University of Bonn, where he earned his doctorate on structural connectivity in limbic encephalitis. His research focuses on image-guided phenotyping of autoimmune encephalitis using 7T MRI and data-driven modeling. He aims to develop clinically relevant biomarkers for improved diagnosis and treatment planning in both adults and children. The recent publications highlight a strong trend in computational neuroimaging, machine learning for lesion detection, and multimodal MRI analysis in epilepsy. His work spans from fundamental brain connectivity studies to translational applications, including portable MRI and AI benchmarks. This reflects a cohesive research program integrating clinical neurology, advanced imaging, and data science. Scientific awards include: Merit Award, Organization for Human Brain Mapping (2022) Medical Excellence Award, Manfred-Lautenschläger-Foundation (2021) BONFOR SciMed Grant (2019) Cusanuswerk Scholarship (2019) BonnNI Structured MD Program (2018) Dr. Bauer has been supported by multiple grants and is actively involved in national and international collaborations. He supervises no students explicitly mentioned, but contributes to a vibrant research environment in the Translational Neuroimaging Group. His lab focuses on developing imaging biomarkers and disease models in autoimmune encephalitis, leveraging ultrahigh-field MRI and computational tools.

• Geospatial Digital Twins
• Virtual Geographic Environments
• Remote Sensing
• +۷ مورد دیگر

Jun Zhu is a professor at the Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, China. His research is centered on geospatial digital twins, virtual geographic environments, and intelligent visualization for disaster risk management, with strong interdisciplinary work in AI, remote sensing, and VR-based simulation. His research interests include: Geospatial Digital Twins Virtual Geographic Environments AI for Remote Sensing 3D and VR-based Disaster Visualization Knowledge Graphs in GIS Public Risk Communication The recent articles (2023–2025) demonstrate a strong trend in integrating large language models, knowledge graphs, and deep learning with geospatial data to build intelligent, interactive, and immersive systems for infrastructure monitoring, disaster simulation, and public engagement. His work emphasizes data-knowledge fusion, human-centered visualization, and real-world applicability in urban and environmental contexts. Scientific Awards: No awards listed in the provided text. Advising and Grants: While Jun Zhu has extensive collaboration with researchers such as Weilian Li, Qing Zhu, Yakun Xie, and Jianbo Lai, and appears to lead research projects, there is no explicit mention of student advising, grant funding, or project titles in the provided data. Labs and Teams: Jun Zhu is likely part of a research group focused on digital twins and geospatial AI at Southwest Jiaotong University, collaborating closely with colleagues in geoinformatics and remote sensing, though specific lab names are not mentioned.

Jakob Nikolas Katherمشاهده پروفایل

استاد

• Artificial Intelligence in Medicine
• Precision Oncology
• Computational Pathology
• +۳ مورد دیگر

Prof. Jakob Nikolas Kather is a Full Professor in Clinical Artificial Intelligence at Technical University Dresden, Germany, with dual appointments in Medicine (Medical Faculty) and Computer Science. He is a senior physician in medical oncology at University Hospital Dresden and holds affiliations with the National Center for Tumor Diseases in Heidelberg. His research focuses on applying AI to precision oncology, developing deep learning methods for analyzing histopathology, radiology, and genomic data. Education: Medical license (2016), Master of Science in Medical Physics (2016, Heidelberg University). Career highlights include postdoctoral research at German Cancer Research Center (2016–2018) and a Junior Professorship at RWTH Aachen (2021–2022). Research interests: AI-driven biomarker discovery, multi-modal data integration, federated/swarm learning for clinical AI, and bridging medicine and data science. His lab is funded by over €6.5M from grants including ERC, DFG, and EU Horizon. Key achievements: Over 240 publications, including high-impact work in Nature Medicine , Nature Cancer , and Gastroenterology . Awards include the Heinz Maier-Leibnitz Award (2021) and Thannhauser Award (2022). Serves on editorial boards of npj Precision Oncology and Nature Reviews Gastroenterology . Grants and collaborations: Projects like DEEP LIVER, PEARL, and EDELWEISS. Leads teams in decentralized AI, radiology core, and computational pathology. Lab members include ~30 researchers and clinicians. Labs/Teams: Clinical AI Group at Else Kroener Fresenius Center, interdisciplinary team of biologists, clinicians, and computer scientists. Active in consortia like TANGERINE and ODELIA.

Marc Erich Latoschikمشاهده پروفایل

استاد

• Virtual Reality (VR)
• Augmented Reality (AR)
• Human-Computer Interaction
• +۵ مورد دیگر

Prof. Marc Erich Latoschik is the head of the Human-Computer Interaction (HCI) Chair at the University of Würzburg. His research focuses on immersive technologies, VR/AR applications, and their psychological impacts. He holds a formal academic appointment as a Professor and has led major research initiatives in Germany, including the Embodiment Lab (Bavarian competence center for virtual embodiment) and projects like ViTras (VR therapy for obesity) and ILAST (VR rehabilitation for knee injuries). Education : Bachelor's/Master's in Mathematics and Computer Science (University of Paderborn) Studies at the New York Institute of Technology and Bielefeld University Research Interests : Latoschik’s work bridges computer science and cognitive sciences, emphasizing embodiment theory, time perception in VR, and social VR dynamics. His interdisciplinary approach combines AI, real-time systems, and 3D graphics to develop impactful technologies for therapy, training, and entertainment. Publications & Impact : He has authored over 160 peer-reviewed articles (e.g., in TVCG , IEEE VR ) and won awards for innovations like multimodal VR interfaces. His research is funded by DFG, BMBF, and EU agencies. Teaching & Service : Latoschik teaches courses on real-time systems, HCI principles, and scientific writing. He actively contributes to academic communities as a GI VR/AR SIG co-founder and reviewer for top conferences. Labs & Initiatives : His Embodiment Lab explores avatar embodiment, while projects like SIRIS and CaveUDK advance VR middleware and multimodal interfaces.

Gullal Singh Cheemaمشاهده پروفایل

پژوهشگر

• Multimodal Learning
• Natural Language Processing
• Computer Vision
• +۳ مورد دیگر

Gullal Singh Cheema is a Researcher at the Leibniz Information Centre for Science and Technology (TIB) within the Visual Analytics research group (Program Area C - Research and Development). His work bridges computer vision and natural language processing to advance multimodal analysis in digital media contexts. His research spans multimodal learning, natural language processing, and computer vision with emphasis on fact-checking systems, claim detection, and social media analytics. Key contributions include the MM-Claims dataset for multimodal verification and leadership in the CLEF CheckThat! lab series addressing political bias and news source authority. His work integrates visual and textual evidence for applications in news analysis, event-centric knowledge graphs, and wildlife conservation. Recent publications (2021-2023) reveal a concentrated focus on multimodal news verification, with significant output in CLEF and SemEval evaluation campaigns. His research demonstrates consistent innovation in fusing transformer architectures with syntactic features while expanding into multilingual sentiment analysis and misogynistic meme detection. Cheema actively shapes the research community through workshop organization (MUWS'22) and dataset development. His Visual Analytics group at TIB drives methodological advances in multimodal understanding with direct applications for social media monitoring and digital humanities.

• Natural Language Processing
• Machine Learning
• Social Media Analysis
• +۴ مورد دیگر

Vivek Kulkarni is a Professor at Stanford University's Department of Computer Science, with extensive contributions to natural language processing, machine learning, and intelligent IoT environments. His work bridges computational methods with social science applications. Key Affiliations: Stanford University (2024), Nara Institute of Science and Technology (2022), Stony Brook University (2014) Research Interests span NLP, social media analysis, energy-efficient computing, and IoT systems. He develops instruction-tuned models for social tasks, multilingual text editing frameworks, and socially sensitive pretraining methods. Publication Trends (2024–2014): 46% focus on NLP, 31% on IoT, 15% on social factors, and 8% on machine learning. Specialized subfields include instruction tuning, multilingual modeling, social language dynamics, and energy-efficient document classification. Collaborations with William Yang Wang, Steven Skiena, Bryan Perozzi, and H. Andrew Schwartz demonstrate interdisciplinary engagement. 15 recent articles highlight advancements in textual representations, social media mining, and contextual modeling.

• Neuroscience
• Brain Mapping
• Medical Imaging
• +۳ مورد دیگر

Katrin Amunts is a University Professor at the Cécile and Oskar Vogt Institute for Brain Research of Heinrich-Heine-University Düsseldorf and heads the Institute for Neuroscience and Medicine (INM-1) at Forschungszentrum Jülich. Her research focuses on creating multi-level brain atlases through cytoarchitectonic, molecular, and fiber architecture analysis. Director of the Cécile and Oskar Vogt Institute for Brain Research Head of INM-1 at Forschungszentrum Jülich Pioneered the BigBrain ultra-high-resolution 3D human brain model Research Interests: Combining image analysis, high-performance computing, and big data analytics to map brain structure-function relationships, intersubject variability, and interspecies differences. Her work underpins the Human Brain Project. Key Publications: Her research output includes probabilistic cytoarchitectonic atlases, comparative studies of avian and human brain circuits, and ultra-high-resolution models. These focus on cortical organization, receptor mapping, and neuroethical frameworks.

• Computer Science
• Distributed Systems
• Blockchain Technology
• +۳ مورد دیگر

Hans-Arno Jacobsen is a Professor at the Faculty of Computer Science (Technische Universität München, TU Munich) and affiliated with the Department of Electrical and Computer Engineering at the University of Toronto. His work spans Computer Science , Distributed Systems , and Artificial Intelligence . Research interests include Blockchain Technology , Consensus Algorithms , Graph Neural Networks , and Quantum Computing . Recent projects focus on decentralized consensus , energy-efficient databases , and federated learning in edge environments. His 15 most recent articles (2024–2025) explore topics such as dynamic resource orchestration , CRDT-based blockchains , and multimodal depression recognition . Collaborates with researchers like Ruben Mayer , Gengrui Zhang , and Shiqiang Wang on systems for federated computing , blockchain benchmarking , and distributed GNN training .

• Computer Science
• Computational Linguistics
• Environmental Science
• +۳ مورد دیگر

Weihong Zhong is an active researcher with dual expertise in Computer Science (particularly large language models and controllable text generation) and Environmental Science (focusing on biodegradation of industrial pollutants like phthalate esters and plastic waste). Their work includes methodological innovations in ACL and EMNLP venues, as well as biochemical studies in Applied and Environmental Microbiology and Synthetic and Systems Biotechnology . Key collaborators include Xiaocheng Feng , Lei Huang , and Bing Qin , with whom they co-author multiple papers across NLP and environmental research. Weihong Zhong’s computational research addresses LLM hallucinations (via frameworks like RHIO, Seal, and FRONT), context window extension (through positional encoding optimization), and controllable generation (using latent space probability density estimation). In environmental science, their work focuses on degrading phthalate esters (PAEs) and plastic pollutants using bacterial consortia and enzymatic engineering. Papers like the 2024 GroundBench study and 2023 MMHalSnowball framework highlight their contributions to multimodal hallucination mitigation. Scientific awards or honors are not explicitly documented in the provided materials. Their interdisciplinary approach bridges technical innovation in AI/ML with applied biological solutions for environmental remediation, as evidenced by publications in both top-tier computational conferences and environmental journals .