جست‌وجوی استاد و پژوهشگر

• Bioinformatics
• Scientific Workflows
• Biomedical Natural Language Processing
• +۳ مورد دیگر

Prof. Dr. Ulf Leser serves as Professor and Deputy Director at the Institute of Computer Science within Humboldt University of Berlin's Faculty of Mathematics and Natural Sciences. His work bridges computer science and life sciences through bioinformatics and knowledge management systems, with significant contributions to scientific workflow optimization and biomedical text mining. His research spans multiple high-impact areas: Bioinformatics and precision oncology tool development (e.g., OncoTagger for cancer gene curation) Scientific workflow systems with focus on carbon-aware execution and energy efficiency Biomedical natural language processing for relation extraction and knowledge base curation Advanced time series analysis methods (e.g., ClaSP for segmentation) Environmental monitoring through satellite data analysis He actively develops infrastructure for reproducible scientific computing while addressing sustainability challenges in HPC environments. Recent publications (2023-2026) reveal three dominant trends: (1) Integration of explainable AI in healthcare assessment systems, (2) Sustainable computing approaches for scientific workflows including carbon-aware scheduling, and (3) Advancement of biomedical text mining through knowledge-augmented language models. His work consistently targets real-world applications in precision medicine and environmental science. Prof. Leser currently supervises students including Michael Piechotta (Diplom in Bioinformatics, defense scheduled September 2025). As Deputy Director and Faculty Council member, he shapes institutional research strategy while leading projects at the intersection of computer science and life sciences. His group maintains active collaborations with biomedical research institutions and contributes to community standards in scientific workflow systems.

Rudolf Schneiderمشاهده پروفایل

پژوهشگر

• Deep Learning
• Information Extraction
• Information Retrieval
• +۳ مورد دیگر

Rudolf Schneider is a researcher in Data Science and Artificial Intelligence at Beuth University of Applied Sciences Berlin. He holds a PhD in Computer Science (2023) with focus on medical text mining and neural information retrieval. His academic roles included Research Assistant at DATEXIS and doctoral studies under Prof. Alexander Löser's supervision. Key research interests span Deep Learning applications in healthcare, information extraction from clinical notes, and neural retrieval systems. Current work involves BMWi-funded MACSS project for medical service solutions. Notable contributions include SECTOR (topic segmentation) and Smart-MD (medical paragraph retrieval) models. Publications highlight advancements in Open Information Extraction (OIE) benchmarking, relational database interactions, and NLP system evaluations. Active in conferences like ISWC and WWW. Organizational involvement includes heavy metal festival coordination and Chaos Computer Club participation.

• Causal Machine Learning
• Explainable AI
• High-Dimensional Data Analysis
• +۵ مورد دیگر

Prof. Stefan Bauer is an Associate Professor at TU Munich and a senior PI at Helmholtz AI. He holds a Ph.D. in Computer Science from ETH Zurich (2018), where he received the ETH Medal for his thesis on causal learning. His research focuses on developing algorithms that uncover causal relationships in high-dimensional data, enabling explainable AI and robust, transformative technologies. Bauer previously worked as an Assistant Professor at KTH Stockholm and a Group Leader at the Max Planck Institute for Intelligent Systems. Educational Background Ph.D. in Computer Science, ETH Zurich (2018) M.Sc. Mathematics, ETH Zurich B.Sc. Economics and Finance, University of London Research Interests Bauer’s work bridges causal inference, machine learning, and robotics. Key themes include causal representation learning for AI transparency, scalable algorithms for complex systems, and applications in robotics, materials science, and biomedical research. His team emphasizes real-world validation, such as deploying dexterous manipulation robots and discovering novel high-entropy alloys via machine learning. Awards and Contributions CIFAR Azrieli Global Scholar (2020) Best Paper Award at ICML (2019) ETH Medal for Outstanding Dissertation (2018) Advancing AI and Robotics Bauer leads projects like the Real Robot Challenge , a cloud-based platform for reproducible robotics research. He also collaborates on biomedical applications, such as bias detection in medical imaging and robustness testing for foundation models in healthcare. Labs and Collaborations His lab at TU Munich integrates interdisciplinary teams working on causal AI, robotics, and materials discovery. Bauer collaborates with Helmholtz AI, KTH, and institutions globally to advance AI’s societal impact.

René Andraeمشاهده پروفایل

پژوهشگر

• Stellar Astrophysics
• Galaxy Morphology
• Quasar Variability
• +۲ مورد دیگر

René Andrae is a Researcher at the Max Planck Institute for Astronomy (MPIA) in Heidelberg, Germany. He works in the Gaia group under Coryn Bailer-Jones and contributes to the Gaia Data Analysis and Processing Consortium (DPAC). Education: Undergraduate studies in physics at the Free University of Berlin (2003) ERASMUS exchange at the University of Manchester (2005-2006) Diploma in physics from the University of Heidelberg (2009) PhD at MPIA with Knud Jahnke (2011, magna cum laude), funded by the Klaus-Tschira Foundation. Research Interests: Stellar Parameter Calibration: Developing statistical methods to correct mismatches between synthetic and real astrophysical spectra, focusing on metallicity and reddening estimates. Galaxy Morphology: Investigating mathematical parametrization of galaxy shapes and unsupervised classification methods like bipartite-graph models. Quasar Variability: Bayesian model comparison of stochastic vs deterministic models using SDSS lightcurves. Exoplanet Detection: Addressing methodological pitfalls in radial-velocity data analysis. Statistical Methodology: Specializing in classification algorithms, regression, and model assessment (e.g., Bayes factors, cross-validation). Programming Expertise: Advanced Java/C++ skills, good Python/Mathematica knowledge, and basic HTML. Scientific Contributions: 15+ peer-reviewed publications (first-author and co-author) on Gaia data analysis, galaxy dynamics, and methodological advancements in astronomy. Key Awards: Klaus-Tschira Foundation PhD scholarship and a magna cum laude PhD distinction.

• Geospatial Artificial Intelligence
• Explainable Artificial Intelligence
• Causal Inference
• +۴ مورد دیگر

Chuan Chen is a researcher at the Technical University of Munich , affiliated with the Chair of Cartography and Visual Analytics . His work focuses on the intersection of Geospatial Artificial Intelligence (GeoAI) , Explainable AI (XAI) , Causal Inference , and Visual Analytics , aiming to improve the interpretability and policy relevance of spatial modeling. Research Interests include responsible AI applications in urban contexts, population distribution modeling, and frameworks for transparent decision-making using geospatial analysis. His studies emphasize ethical AI, causal mechanisms, and spatial representation. Recent publications highlight his contributions to geospatial modeling, AI ethics narratives, and urban sustainability. He applies explainable AI to frozen ground analysis, population distribution predictability, and AI risk decoding from news data. Teaching includes courses like Image Analysis for Mapping (M.Sc., since 2022) and Mapping for a Sustainable World (M.Sc., 2023 Summer). He supervises master theses and seminars on AI ethics and global-scale visual analysis.

• Artificial Intelligence
• Natural Language Processing
• Information Retrieval
• +۵ مورد دیگر

Xin Zhao is a prominent professor at Renmin University of China's School of Information, Department of Computer Science, with an extensive publication record spanning from 1997 to 2026. His research demonstrates significant contributions across multiple disciplines including artificial intelligence, natural language processing, computer vision, and interdisciplinary applications in ecology, medicine, and business. Dr. Zhao's research interests are remarkably diverse, focusing primarily on artificial intelligence and its applications. His work spans natural language processing, large language models, information retrieval, machine learning, and computer vision. Recent publications reveal a strong emphasis on practical AI applications in healthcare (dental implant failure prediction), environmental science (kelp bed dynamics), finance (green finance impact), and industrial systems (composite curing process monitoring). His research often combines theoretical advancements with real-world problem solving, demonstrating both academic rigor and practical relevance. Analysis of his recent publications (2024-2026) shows a clear trend toward interdisciplinary AI applications, with increasing focus on multimodal learning, robustness in dynamic environments, and practical implementations across various sectors. His work bridges theoretical computer science with domain-specific challenges in medicine, ecology, finance, and industrial engineering. The publications demonstrate sophisticated methodological approaches including deep learning architectures, mathematical modeling, and novel algorithmic solutions to complex problems. Dr. Zhao has made significant contributions to the academic community through numerous publications in high-impact journals and conferences including IEEE Transactions, ACL, AAAI, and CVPR. His collaborative work spans international boundaries, with co-authors from institutions worldwide, indicating strong research networks and interdisciplinary collaborations. His research group appears to be actively engaged in cutting-edge AI research, with particular strengths in large language models, multimodal learning, and practical AI applications. The group's work on projects like C-3PO (Compact Plug-and-Play Proxy Optimization) and RMoA (Optimizing Mixture-of-Agents) demonstrates leadership in emerging AI methodologies. Current research directions include enhancing LLM capabilities, improving multimodal understanding, and developing robust AI systems for real-world applications across diverse domains.

Volker Hofmannمشاهده پروفایل

پژوهشگر

• Scientific Metadata
• Ontology Development
• Knowledge Graphs
• +۵ مورد دیگر

Dr. Volker Hofmann serves as Group Lead of the Metadata & Information Systems (MIS) group within the Materials Data Science and Informatics (IAS-9) department at the Institute for Advanced Simulation (IAS) , Forschungszentrum Jülich . His research focuses on advancing scientific metadata standards, ontology development, and knowledge graph systems to drive cultural change in scientific data management. Specifically, he emphasizes FAIR principles ( Findable, Accessible, Interoperable, Reusable ), semantic harmonization, and the creation of data-aware information systems. His work integrates interdisciplinary approaches to enhance the findability and reusability of research data. Notable research interests include data annotation strategies, the role of metadata in reproducibility, and the application of neuroscientific insights to inform metadata practices. Hofmann’s contributions bridge computational methodologies with practical implementation, addressing challenges in institutional data governance and scientific workflows. Publications span neuroscience and data science, exploring topics like electrosensory processing in fish and the development of metadata frameworks for large-scale research institutions. Collaborations emphasize translating theoretical advancements into actionable tools for the scientific community. He holds no listed academic awards but has contributed to projects fostering FAIR data compliance within the Helmholtz Association and beyond. His team at IAS-9 collaborates across disciplines to address challenges in data integration and semantic interoperability, aiming to standardize metadata practices globally.

Varish Mulwadمشاهده پروفایل

پژوهشگر

• Information Extraction
• Knowledge Graph Population
• Semantic Web
• +۴ مورد دیگر

Varish Mulwad is a Senior Scientist at GE Research with 14 years of experience in algorithm development and knowledge graph construction from structured/unstructured data. He holds a Ph.D. and M.S. in Computer Science from the University of Maryland, Baltimore County (UMBC), where he worked under Prof. Tim Finin and collaborated with Prof. Anupam Joshi, and a B.E. in Computer Engineering from University of Mumbai. Ph.D. & M.S. in Computer Science (UMBC) B.E. in Computer Engineering (University of Mumbai) His research focuses on semantic interpretation of tabular data through linked data frameworks, information extraction from unstructured text, and knowledge graph population using probabilistic reasoning and graphical models. He has pioneered domain-independent systems for table interpretation and developed novel methods for cloud SLA automation and cybersecurity threat detection via social media analysis. Recent work trends include: Context-aware web table annotation Relational table representation learning Linked data generation from spreadsheets Pre-training/fine-tuning paradigms for web tables Application of semantic web standards to cybersecurity He has led 3-4 member project teams in developing production-ready solutions, contributed to 19 peer-reviewed publications, and secured 7 patents with 900+ citations. During his academic tenure at UMBC's Ebiquity Research Lab, he co-developed the TABEL framework for table semantics inference and produced the first interactive system for meta-analysis report generation from linked data.

Tomáš Kliegrمشاهده پروفایل

استاد

• Explainable machine learning
• Cognitive biases in machine learning
• Natural language processing
• +۴ مورد دیگر

Tomáš Kliegr is a Professor at the Faculty of Informatics and Statistics of Prague University of Economics and Business, leading the Data Science & Explainable AI (DSXAI) research team. He specializes in explainable machine learning, cognitive biases in AI, natural language processing for entity recognition and knowledge graphs, and preference learning. 2025: Professor of Applied Informatics at Prague University of Economics and Business 2019: Associate Professor (docent) in Applied Informatics at Prague University of Economics and Business 2017: PhD in Research in Electronic Engineering from Queen Mary University London, recognized in the Czech Republic as Ph.D. in Artificial Intelligence and Biocybernetics 2012: PhD in Applied Informatics at Prague University of Economics and Business His research focuses on explainable and interpretable machine learning, particularly rule-based models, cognitive biases in AI, natural language processing, and knowledge graphs. He has contributed to software tools like arc, action-rules, and pyIDS, and leads the EasyMiner.eu project. His articles between 2018 and 2025 span topics like counterfactual explanations, text clustering visualization, SARS-CoV-2 diagnostic modeling, and rule-based ML optimization. He co-organizes the RuleML Webinars and the XI-ML workshop, and was a program committee member for RuleML+RR 2020. Best paper award at RuleML Challenge 2019 He has advised PhD students including Lukas Sykora and Stanislav Vojíř, and collaborates with institutions like the Czech Academy of Sciences and Lawrence Berkeley National Laboratory. His work addresses the intersection of machine learning interpretability and human cognitive factors, with applications in public health and semantic web technologies.

• Computational Biology
• Bioinformatics
• Phylogenetics
• +۵ مورد دیگر

Maria Anisimova is Professor and Director of the Bioinformatics Center at the Institute of Computational Life Sciences (ICLS) within ZHAW Zurich University of Applied Sciences, Wädenswil, Switzerland. Her research spans evolutionary biology, computational genomics, and bioinformatics tool development, focusing on tandem repeats, codon usage, and indel evolution. Her publications address: Computational challenges in codon optimization (2025) Indel-aware phylogenetic methods (2024-2025) Tandem repeat dynamics in cancer and evolution (2022-2025) Ontology-driven biomedical data querying (2022) She has received multiple research grants from: Swiss National Science Foundation (grants 193832, 182330, 176316, 157064, 127325) European Commission (grant 823886) European Cooperation in Science and Technology (grant C16.0072) Maria leads collaborative projects in: Trans-omic cancer research Repeat protein classification Indel evolutionary modeling Biomedical data integration Her lab develops bioinformatics tools like TRAL, ProPIP, and ARPIP for tandem repeat analysis and sequence alignment.

Tom Hanikaمشاهده پروفایل

مدرس

• Knowledge Discovery
• Machine Learning
• High-Dimensional Data Analysis
• +۷ مورد دیگر

Tom Hanika is a Lecturer at the Institute of Library and Information Science, Humboldt University, within the Faculty of Philosophy. His research focuses on Knowledge Discovery, Machine Learning, and Formal Concept Analysis, with applications to high-dimensional data, databases, and social network analysis. He actively contributes to Open Science initiatives and scientometric studies. He is a senior developer of the BibSonomy publication sharing system and maintains the FCA-tool conexp-clj. Hanika has held roles as Program Chair for ICCS and ICFCA conferences, and has served on program committees for ISWC, ECML/PKDD, and others. His work bridges theoretical foundations (e.g., lattice theory, ordinal data science) with practical applications in biology, geography, and digital humanities. Recent research includes studying reproducibility in graph neural networks, ordinal motifs in lattices, and intrinsic dimensionality analysis. His contributions span over 50 peer-reviewed publications across venues like Information Sciences , Scientometrics , and Tohoku Mathematical Journal . Professional Memberships: German Computer Science Society, German Mathematical Society, Free Software Foundation Labs/Teams: Knowledge & Data Engineering Group (KDE), Intelligent Information Systems group

Andreas Martin Lisewskiمشاهده پروفایل

مدرس ارشد

• Computational Life Sciences
• Synthetic Biology
• Biodigital Convergence
• +۷ مورد دیگر

Dr. Andreas Martin Lisewski is a Senior Lecturer of Science at the School of Science, Constructor University. His research focuses on computational life sciences and synthetic biology, particularly analyzing genomic information in infectious agents and exploring the biodigital convergence between molecular biology and digital communication theory. He investigates synthetic biology's societal impacts, including ethical implications and risks associated with bioengineering advancements. Dr. Lisewski holds an MSc from the University of Hamburg and a PhD from the Technical University of Munich. He has received prestigious awards such as the Sigma Xi (2025) and DAAD Rückgewinnung Stipendium (2015-2016). His teaching portfolio includes courses on synthetic biology, global health, and ethics in science and technology. His research outputs include groundbreaking studies on SARS-CoV-2 genomic analysis, vaccine effectiveness, and computational methods for protein function prediction. He serves as a Guest Editor for International Journal of Molecular Sciences and Academic Editor for Microbial Cell . Key research themes include: Decoding genomic information using digital signal processing principles Development of computational tools to distinguish synthetic from natural genomes Thermostability design in proteins for bioengineering applications Evaluation of synthetic biology's societal and environmental risks

Cedric du Mouzaمشاهده پروفایل

استاد

• Database Systems
• Data Mining
• Digital Humanities
• +۶ مورد دیگر

Cédric du Mouza is a full Professor within the CEDRIC Laboratory at the Conservatoire National des Arts et Métiers (CNAM) in Paris, France. His research activities lie at the intersection of database systems, data mining, and digital humanities, with a strong emphasis on social network analysis, knowledge base population, and spatial/temporal data management. Education details are not explicitly provided in the source material. His research interests can be summarized as follows: Database Technologies: scalable indexing, distributed repositories, spatial and temporal data structures. Data Mining & Machine Learning: community detection, influence maximization, recommender systems. Digital Humanities: prosopographic databases, historical social networks, credibility assessment of historical sources. Social Media Analytics: Twitter user profiling, early detection of popular accounts, reducing filter bubbles. Across more than two decades, Prof. du Mouza has produced a rich scholarly record that blends theoretical advances with practical applications. Recent publications (2020-2024) highlight a methodological shift toward end-to-end evaluation frameworks for knowledge base population, uncertainty modeling in historical corpora, and real-time influence maximization in advertising ecosystems. These works are published in premier venues such as SIGIR , VLDB Journal , WISE , CIKM , and leading French conferences (EGC, BDA). No specific scientific awards, funded projects, or doctoral student names are mentioned in the supplied text. Prof. du Mouza collaborates extensively within interdisciplinary teams involving historians, computer scientists, and statisticians, reflecting CEDRIC’s integrative research culture. While no dedicated laboratory or team name is provided, his continuous affiliation with CEDRIC/CNAM confirms an active and ongoing role in both research and graduate-level education.

• Formal Concept Analysis
• Machine Learning
• Semantic Web
• +۳ مورد دیگر

Professor Dr. Tom Hanika is affiliated with the University of Hildesheim , working in the Intelligent Information Systems (IIS) division within the Institute of Computer Science. His research bridges formal concept analysis , machine learning , and knowledge representation , focusing on geometric interpretations of data and explainable AI systems. Research Themes: Intrinsic dimensionality, lattice structures, and hybrid human-AI collaboration Teaching: Offers courses in databases, C++ programming, and semantic technologies Contact: Office (SC.C. 2.03), Phone +49 5121 883-40312, Email via contact form Recent publications highlight his work on geometric data analysis and formal context manipulation , including applications in graph neural networks, ordinal pattern recognition, and conceptual lattice visualization. His Collaborative Hybrid Human AI Learning framework demonstrates practical implementations of these theories. Current projects explore dimensionality resilience in machine learning models and topic flow visualization in academic networks, reflecting his dual focus on theoretical foundations and applied knowledge systems.

Klim Zaporojetsمشاهده پروفایل

پژوهشگر ارشد

• Natural Language Processing
• Information Extraction
• Entity Linking
• +۳ مورد دیگر

Klim Zaporojets is a Marie Skłodowska-Curie Postdoctoral Fellow in the Department of Computer Science at Aarhus University, where he conducts research within the Data-Intensive Systems Group. His work bridges theoretical advancements and practical applications in natural language understanding. His research focuses on information extraction systems that connect textual content with structured knowledge bases. His methodology emphasizes leveraging external knowledge sources to enhance information extraction performance, particularly in document-level contexts where entities evolve over time. His work spans temporal relation extraction, entity linking, and biomedical text mining applications. The publication record reveals a strong focus on document-level information extraction with increasing emphasis on temporal aspects and knowledge integration. Recent work explores large language model applications for graph learning and calibration challenges in LLMs, showing evolution from traditional NLP tasks to cutting-edge foundation model research. His publications appear in top-tier venues including ACL, EMNLP, CIKM, and NeurIPS. His scientific recognition includes the prestigious Marie Skłodowska-Curie Postdoctoral Fellowship, supporting his research at Aarhus University. His work has produced several influential datasets including DWIE, TempEL, and BioDEX that have become benchmarks in document-level information extraction. Zaporojets maintains active collaborations with researchers at Ghent University (evidenced by his ugent.be email address) and has contributed to multiple interdisciplinary projects spanning computational linguistics, healthcare informatics, and knowledge representation. His technical contributions include open-source implementations of his research, demonstrating commitment to reproducible science.