جست‌وجوی استاد و پژوهشگر

• Medical Informatics
• Artificial Intelligence in Healthcare
• Healthcare Data Integration
• +۷ مورد دیگر

Prof. Martin Boeker is a Professor of Medical Informatics at the Technical University of Munich (TUM), affiliated with the TUM School of Medicine and Health. His work focuses on advancing healthcare through AI-driven solutions, interoperability frameworks, and precision medicine initiatives. Key projects include the German Medical Text Corpus (GeMTeX) and the MIRACUM DIFUTURE Alignment Hub. Expertise: Medical Informatics, AI in Healthcare, Federated Learning, Health Data Integration Key Contributions: FHIR-based systems, clinical decision support, patient-centered outcomes research Leadership: Director of the Institute for AI and Informatics in Medicine at TUM Hospital Right of the Isar Research emphasizes bridging clinical practice and data science through projects like modular health crawlers, automated guideline adherence monitoring, and cross-institutional medical NLP solutions. His work spans oncology informatics, rare disease management, and pandemic response data ecosystems. Recent articles highlight innovations in digital twins for precision oncology, federated analysis in oncology, and German-language medical NLP challenges. He collaborates internationally on EHR standardization and healthcare interoperability, contributing to the Medical Informatics Initiative (MII) and pandemic evidence ecosystems. Grants and collaborations involve the German Federal Ministry of Education and Research, European initiatives, and industry partnerships. Educational efforts focus on training future medical informatics professionals through MII competency programs.

• Mining Engineering
• Raw Materials Processing
• Underground Mining
• +۷ مورد دیگر

Prof. Dr.-Ing. Elisabeth Clausen is a Professor and Director of the Chair and Institute for Advanced Mining Technologies at RWTH Aachen University. She holds key roles in the Specialist Group for Raw Materials and Disposal Technology, serves as a rectorate representative, and leads the Commission for EU Research Funding. Her research spans Underground mining automation Acoustic emission diagnostics Sustainable mining systems Space resource extraction Advanced sensor technologies Her recent publications focus on autonomous mining machinery, underground communication systems, and acoustic emission analysis across 15+ studies from 2013–2025, with particular emphasis on Ultra-wideband positioning Thermographic detection Crack monitoring in planetary gearboxes Explosive atmosphere safety Mineral processing diagnostics Digitalization trends Prof. Clausen contributes to mining education reform through initiatives like CDIO™ and has developed innovative learning spaces in underground mines. She coordinates international educational labs and integrates sustainability into mining engineering curricula, with publications on Adaptive ventilation systems Mining education frameworks Future-proof mineral extraction Entrepreneurial mindset in engineering

• Machine Learning
• Data Analytics
• Biomechanics
• +۴ مورد دیگر

Björn Eskofier is a Principal Investigator for the Translational Digital Health Group at AI for Health and leads the research group at Helmholtz Zentrum Munich. He is Professor at Friedrich-Alexander-University Erlangen-Nuremberg (FAU), where he founded the Machine Learning and Data Analytics (MaD) Lab in 2013 and established the Department of Artificial Intelligence in Biomedical Engineering (AIBE). Education: PhD in Biomechanics (University of Calgary, 2006-2013), MSc in Electrical Engineering (FAU, 2006). His research focuses on building a Digital Health Ecosystem through multidisciplinary collaboration, emphasizing Machine Learning, Data Analytics, Biomechanics , and AI-driven clinical translation. Recent publications highlight his work in federated health data systems, predictive disease modeling, and digital symptom monitoring. He is an active academic leader , serving as Area Editor for IEEE journals, General Chair of BHI 2023, and co-director of the EmpkinS initiative. His awards include the Unipreneurs award (2023), multiple best paper prizes, and recognition as a Heisenberg Professor (DFG, 2017-2022).

Felix Balzerمشاهده پروفایل

استاد

• Medical Data Science
• Health Informatics
• Artificial Intelligence in Healthcare
• +۷ مورد دیگر

Prof. Felix Balzer is a Professor for Medical Data Science and Chief Medical Information Officer (CMIO) at Charité - University Medicine Berlin . He serves as Director of the Institute of Medical Informatics, leading digitalization efforts for patient care and overseeing implementation of the hospital's electronic medical record (EMR) systems. Medical Data Science professorship (2021) Director of Institute of Medical Informatics Acting Chief Information Officer (2024-2025) Deputy Chief Medical Officer for Clinical Digitalization (2025) His research focuses on: Digital healthcare transformation Machine learning in critical care Alarm fatigue mitigation Interoperability standards (FHIR, OMOP) Electronic health records (EHR) optimization Patient monitoring systems The 2025-2026 publications reveal expertise in ICU data analysis, predictive modeling for postoperative delirium, and pandemic response technology. His work bridges clinical practice with technical implementation through: Interdisciplinary teams Multi-center trials Real-time clinical data architectures Human factors in healthcare AI

• Semantic Web
• Ontology Engineering
• Knowledge Graphs
• +۴ مورد دیگر

Birgitta König-Ries is a Professor at the Department of Computer Science, University of Jena, Germany. She is a leading researcher in semantic technologies, ontology engineering, and knowledge graph management for biodiversity and life sciences. Her work focuses on reproducibility, provenance tracking, and data integration using semantic approaches. Research Interests: Semantic Web, Ontology Engineering, Knowledge Graphs, Biodiversity Informatics, Reproducibility of Scientific Experiments Key Collaborations: Sheeba Samuel, Nora Abdelmageed, Samira Babalou, Alsayed Algergawy, Felicitas Löffler, Vamsi Krishna Kommineni Her recent publications emphasize automated knowledge graph construction, domain-specific language models (e.g., BiodivBERT), benchmarking semantic table interpretation (KG2Tables, BiodivTab), and tools for provenance management (MLProvLab, MLProvCodeGen). She contributes to FAIR data principles and interdisciplinary research, particularly in biodiversity and public administration transparency. Her work bridges theoretical advances with practical implementations, including open-access benchmarks (tFood, tBiodiv, tBiomed) and collaborative platforms like BiodivPortal and fusion-jena. Notable Tools & Benchmarks: BiodivBERT, KG2Tables, BiodivTab, MLProvLab, tBiodiv, tBiomed

Volker J Schmidمشاهده پروفایل

استاد

• Bayesian Statistics
• Spatial Statistics
• Medical Imaging
• +۷ مورد دیگر

Volker J Schmid is a Professor of Bayesian Imaging and Spatial Statistics at the Department of Statistics, Ludwig Maximilian University of Munich. He leads the Bayesian Imaging and Spatial Statistics group and contributes to interdisciplinary initiatives like the Munich Center of Machine Learning. His work bridges statistical theory with applications in medical imaging and biology. PhD in Statistics (2004), LMU Munich Diploma in Statistics (2000), LMU Munich Abitur, Joseph-von-Fraunhofer-Gymnasium Cham (1993) His research focuses on Bayesian computational methods for high-dimensional data, particularly in medical imaging (MRI, DCE-MRI) and biological microscopy (e.g., 3D nuclear architecture analysis via super-resolution microscopy). Key applications include disease mapping , image segmentation , and spatio-temporal modeling . His software tools (e.g., nucim , bioimagetools , BAMP ) enable quantitative analysis in nuclear imaging and age-period-cohort modeling. His 15 most recent publications span Bayesian modeling for medical imaging , spatio-temporal epidemiology , and computational biology . Topics include co-localization metrics in fluorescence microscopy, nuclear architecture analysis, and dynamic Bayesian frameworks for MRI data. Collaborations extend to neuroimaging, oncology, and nuclear biology.

Oya Beyanمشاهده پروفایل

استاد

• Biomedical Informatics
• Semantic Web Technologies
• Clinical Decision Support
• +۵ مورد دیگر

Prof. Dr. Oya Beyan is a Professor at the University of Cologne's Institute for Biomedical Informatics and a Core Scientist at the Center for Data and Simulation Science. Her research focuses on enabling FAIR (Findable, Accessible, Interoperable, Reusable) data management, distributed analytics on sensitive medical data, and data-driven innovations in healthcare. She leads projects like the PADME platform for federated machine learning and privacy-preserving analytics. Key areas include biomedical informatics, semantic web technologies, clinical decision support systems, and ethical challenges in data science. Research Interests: FAIR Data Principles & Infrastructure Privacy-Preserving Distributed Learning Explainable AI in Healthcare Semantic Interoperability Medical Data Integration Ethical & Social Implications of Data Use Notable Contributions: Development of the Personal Health Train framework for decentralized medical data analysis Leadership in EU-funded initiatives like NFDI4Health and Medical Informatics Collaborations Pioneering work on federated learning applications in oncology and rare disease research Lab & Affiliations: Prof. Beyan's work is anchored in the Institute for Biomedical Informatics and the Center for Data and Simulation Science, fostering interdisciplinary collaboration between computational science and medical research.

• Telecommunication Networks
• Distributed Machine Learning
• Edge Computing
• +۵ مورد دیگر

Falko Dressler is a Full Professor and Chair for Telecommunication Networks at the School of Electrical Engineering and Computer Science, Technische Universität Berlin. He holds a Ph.D. and M.Sc. in Computer Science from Friedrich-Alexander University of Erlangen-Nuremberg (1998-2003). His research focuses on next-generation wireless systems , distributed machine learning , edge computing , and applications in Internet of Things (IoT) , cyber-physical systems , and internet of bio-nano-things . Editorial roles: IEEE Trans. on Mobile Computing, Elsevier Computer Communications, IEEE/ACM Trans. on Networking Conference leadership: IEEE INFOCOM, ACM MobiSys, IEEE VNC Textbooks: Self-Organization in Sensor and Actor Networks (Wiley), Vehicular Networking (Cambridge) Recent publications highlight trends in Edge Computing Resilience and 6G Network Architecture , with a strong emphasis on Molecular Communication , Terahertz Band Synchronization , and Federated Learning in vehicular environments. Scientific contributions include multiple IEEE Fellow , ACM Fellow , and VDE ITG Prize 2023 recognitions. Advisory and professional activities include membership in the German National Academy of Science and Engineering (acatech), IEEE COMSOC Conference Council, and ACM SIGMOBILE Executive Committee. His work spans cooperative driving, ultra-low power sensor networks, and security in nano-communication systems.

Jakob Beetzمشاهده پروفایل

استاد

• Building Information Modeling (BIM)
• Digital Twins
• Artificial Intelligence in Construction
• +۳ مورد دیگر

Prof. Dr. Jakob Beetz serves as a University Professor at RWTH Aachen University's Faculty of Architecture, leading the Design Computation (DC) research group. His work addresses critical challenges in sustainable built environments through digital innovation, focusing on integrating knowledge, information, and data across disciplines to reduce the sector's energy and material consumption—which accounts for over one-third of global totals—while advancing climate goals under the European Green Deal. His research spans Building Information Modeling (BIM), digital twins, and artificial intelligence, with emphasis on graph-based data federation, semantic web technologies, and large language models in construction. Key interests include evidence-based planning, parametric design optimization, building physics simulation, and networked knowledge modeling. Recent projects explore federated digital twin ecosystems for infrastructure management, intelligent damage assessment systems, and AI-driven solutions for wood structure preservation, directly contributing to sustainable development targets. Analysis of his 2024-2025 publications reveals a cohesive trajectory toward decentralized data environments and AI integration in Architecture, Engineering, and Construction (AEC). His work bridges theoretical foundations in knowledge representation with practical applications in bridge maintenance, road infrastructure, and timber construction, demonstrating consistent innovation in spatial data querying, federated issue management, and ontology-based process modeling. Prof. Beetz actively supervises PhD candidates, as evidenced by DC.Promotions 2024, and drives international collaboration through events like the Forum Construction Informatics 2025 and CIB W78 conferences. His research group engages with industry standards including Industry Foundation Classes (IFC) and Common Data Environments (CDEs), emphasizing open data principles and interoperability to transform construction workflows.

Miguel Mahechaمشاهده پروفایل

استاد

• Ecosystem Responses to Climate Extremes
• Biogeography and Ecosystem Functioning
• Earth System Data Science
• +۴ مورد دیگر

Miguel Mahecha is Professor of Environmental Data Science and Remote Sensing at the University of Leipzig, where he serves as Institute Head of the Institute for Earth System Science and Remote Sensing. He is also affiliated with the Remote Sensing Centre for Earth System Research, a collaboration between Leipzig University and the Helmholtz Centre for Environmental Research (UFZ). Mahecha is a member of the German Centre for Integrative Biodiversity Research (iDiv) and serves as Principal Investigator in the Centre for Scalable Data Analytics and Artificial Intelligence. Additionally, he is a Fellow of the European Laboratory for Learning and Intelligent Systems and co-spokesperson for the National Research Data Infrastructure for Earth System Sciences (NFDI4Earth). Full Professor for Modelling Approaches in Remote Sensing, University of Leipzig (since 03/2020) Research Group Leader: Empirical Inference in the Earth System, Max Planck Institute for Biogeochemistry, Jena (12/2012 - 03/2020) PostDoc, Max Planck Institute for Biogeochemistry, Jena (10/2009 - 11/2012) PhD in Environmental Sciences, ETH Zürich (06/2006 - 09/2009) Diploma in Geoecology, Bayreuth University (10/2000 - 04/2006) Mahecha's research focuses on understanding ecosystem responses to climate extremes and human-environment relationships during these events. He investigates macro-ecological dynamics and ecosystem functioning using data-driven methods and high-dimensional Earth observations. A key contribution is his co-development of the Earth System Data Cube concept, which integrates empirical methods with theoretical understanding to analyze complex Earth system interactions. His work spans biogeography, ecosystem functioning, and advanced data science methodologies for environmental monitoring. His recent publications demonstrate a strong emphasis on analyzing compound climate extremes, particularly heatwaves and droughts, and their impacts on ecosystems. Mahecha has pioneered methods using Earth System Data Cubes to integrate diverse environmental datasets, enabling novel insights into biosphere-atmosphere interactions. His research increasingly incorporates artificial intelligence and machine learning approaches to understand spatiotemporal patterns in ecological systems, with applications in real-time forest monitoring and biodiversity assessment. Fellow of the European Laboratory for Learning and Intelligent Systems Co-spokesperson for NFDI4Earth (National Research Data Infrastructure for Earth System Sciences) Mahecha leads multiple significant research projects including Digital Forest (real-time forest monitoring), NFDI4BioDiversity, and XAIDA (extreme events: AI for Detection and Attribution). His work receives funding from diverse sources including EU, DFG, and Stiftungen Inland. He collaborates extensively with the German Centre for Integrative Biodiversity Research (iDiv) and the Centre for Scalable Data Analytics and Artificial Intelligence. His research group, Earth System Data Science (ESDS), focuses on developing methods to extract valuable information from long-term environmental observations to understand coupled Earth system dynamics. At the Remote Sensing Centre for Earth System Research, Mahecha's ESDS group investigates how ecosystem functions respond to climate extremes, societal vulnerability to environmental hazards, and nonlinear interactions in coupled Earth systems. The group leverages citizen science data, remote sensing observations, and advanced computational methods to address pressing environmental questions.

Jan-David Liebeمشاهده پروفایل

استاد

• Implementation Science
• Digital Health
• Human-Centered Design
• +۳ مورد دیگر

Prof. Dr. Jan-David Liebe is a Professor of Digital Society at Osnabrück University of Applied Sciences, leading the Digital Health Management field. He holds a PhD in Business Informatics from the University of Osnabrück (2013–2018). His research focuses on implementation science in healthcare, human-centered design, and AI-driven healthcare innovations. He is a co-founder of innnow GmbH and an associated researcher at UMIT’s Institute for Medical Informatics. Key roles include heading the GMDS AG mwmKIS and contributing to national IT reports in healthcare. Research Interests: His work spans digital transformation in healthcare, evaluating healthcare IT maturity models, and fostering innovation through design thinking. Notable projects include benchmarking health IT systems and developing frameworks for AI-supported diagnostics. Professional Roles: Prof. Liebe serves as the Study Director for the Health Management MA program and Entrepreneurship Officer at the WiSo faculty. He actively contributes to research networks like the German Society for Medical Informatics and advises on healthcare IT strategies. Grants & Projects: Leads projects on cloud readiness of hospitals, VR-based medical training, and AI in echocardiography. His work is published in over 50 peer-reviewed articles, emphasizing practical applications of digital tools in healthcare workflows and policy. Affiliations: Member of the Research Commission and Study Committee at Osnabrück University of Applied Sciences. Collaborates with institutions like the Medical School Hamburg and Novia University of Applied Sciences.

• Transportation Systems
• Smart Cities
• Traffic Simulation
• +۳ مورد دیگر

Maria Paz Linares Herreros is a Lecturer at the Universitat Politècnica de Catalunya (UPC), affiliated with the School of Mathematics and Statistics (FME) and the Department of Statistics and Operations Research. She is a member of the IMP (Information Modeling and Processing) research group and collaborates with inLab FIB on intelligent transportation systems. Research interests: Transportation systems, smart cities, traffic simulation, data-driven modeling, environmental impact assessment Specializes in applying machine learning and simulation to urban mobility challenges Her recent publications focus on: Parking availability prediction using deep learning Traffic emission modeling linked to urban policies Dynamic ride-sharing system optimization Integration of IoT data in transportation planning Scientific recognition: Recipient of the IV International Award on Transport Infrastructure Management Research (2018) Active contributor to projects like CitScale and Virtual Mobility Lab Collaborator in European initiatives like KIC Urban Mobility

• Information Systems
• Databases
• Knowledge Graphs
• +۷ مورد دیگر

Stefan Decker is a full University Professor (Universitätsprofessor) at RWTH Aachen University, Germany, where he heads the Chair of Information Systems and Databases (Informatik 5) within the Faculty of Mathematics, Computer Science and Natural Sciences. He is actively involved in teaching, research, and the supervision of numerous ongoing and completed doctoral, master’s, and bachelor theses. Education & Academic Background Doctorate (Dr. rer. pol.) – field of Information Systems or related (exact institution/year not stated in text). Appointed as University Professor and Chair of Information Systems and Databases at RWTH Aachen University. Research Interests Prof. Decker’s work lies at the intersection of databases, knowledge graphs, semantic web technologies, data science, and cybersecurity . He investigates architectures and algorithms for large-scale, privacy-preserving, decentralized data analytics , develops ontology-driven information systems , and explores the use of large language models (LLMs) for educational technology, anomaly detection, and incident-response playbooks. Additional focal areas include smart energy systems, mixed-reality learning environments, FAIR data principles, and federated machine learning . Scientific Contributions & Trends His recent publications (2022-2025) demonstrate a clear trend toward explainable AI, LLM-enhanced systems, secure data spaces, and semantic interoperability . Key contributions include novel anomaly-detection frameworks for encrypted power-grid communications, knowledge-graph-driven chatbots for higher-education support, and methodological advances in decentralized analytics and FAIR data sharing. These works are disseminated in top-tier venues such as AAAI, IEEE ISGT Europe, ESWC, IDEAL, and various Springer LNCS and IEEE Transactions. Supervision & Grants Doctoral Theses Advised: A. T. Neumann – “Chatbots as professional companions in large-scale community information systems” (2024) S. M. Welten – “Methods for practical data sharing and decentralized analytics” (2025) Master’s Theses Co-Advised: A. R. Küsters – “Object-centric process constraints using variable bindings” (2025) Additionally supervising more than 30 ongoing bachelor, master, and doctoral projects covering topics such as LLM-driven cybersecurity playbooks, knowledge-graph construction for German law, privacy-preserving analytics in smart grids, and mixed-reality learning agents. Principal investigator or senior researcher in large collaborative projects including NFDI4DS, WestAI, champI4.0ns and several EU/national initiatives on sovereign data spaces and AI services. Labs & Teams Prof. Decker leads the Information Systems & Databases (DBIS) Research Group . The group operates well-equipped laboratories for knowledge-graph engineering, mixed-reality applications, privacy-enhancing technologies, and secure distributed analytics . Current team size exceeds 30 researchers including PhD candidates, postdocs, and scientific programmers, supported by national and EU funding streams.

Catia Pesquitaمشاهده پروفایل

دانشیار

• Semantic Web
• Biomedical Ontologies
• Knowledge Graphs
• +۷ مورد دیگر

Catia Pesquita is an Associate Professor in Computer Science at the Faculty of Sciences of the University of Lisbon , where she is also a Senior Researcher at LASIGE and leads the Health and Biomedical Informatics Research Line . With a multidisciplinary background in Biology and Computer Science, she focuses on Artificial Intelligence and Data Science applications in life and health sciences . Her research spans Semantic Web , Biomedical Ontologies , Knowledge Graphs , and Explainable AI , with significant contributions to ontology matching and semantic similarity . Education: PhD in Computer Science - Bioinformatics (2012) MSc in Bioinformatics (2008) Degree in Cell Biology and Biotechnology (2005) Current Projects: KATY (2021-2024): AI-Empowered Personalized Medicine for cancer treatments. BRAINTEASER (2021-2024): AI for ALS and MS disease progression models. Research Outputs: Developed tools like AgreementMakerLight (AML) , KGsim-benchmark , and the Epidemiology Ontology . Over 133 publications with significant citations (32,909 reads, 3,889 citations). Teaching: Lectures advanced topics in Databases , Data Integration , Bioinformatics , and Big Data . Advocacy: Vice-president of Biodata.pt , promoting biological data valorization in Portugal. Actively involved in initiatives to promote computer science careers to young women .

• Programming Languages
• Software Engineering
• Formal Methods
• +۳ مورد دیگر

L. Thomas van Binsbergen is a Professor in the Department of Computer Science & Computer Engineering at the University of Wisconsin-La Crosse, affiliated with the College of Science & Health. His work focuses on language design, formal methods, and policy-based systems. His research explores Executable formal specifications of programming languages Modular meta-language frameworks (e.g., iCoLa+, eFLINT) Data-dependent grammars for network protocols Purpose-based access control derived from GDPR Functional parsing algorithms (GLL, Happy-GLL) Policy enforcement in distributed systems Recent publications (2020-2025) demonstrate trends in language parametric design, security policy formalization, and exploratory programming environments. Notable collaborations include Damian Frölich, Tim Müller, and Tom M. van Engers. Van Binsbergen earned his PhD from Royal Holloway, University of London (2019) and contributes to conferences like GPCE, SLE, and workshops on programming language theory and data security.