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Jan LellmannView profile

Professor

• Variational Image Processing
• Optimization on Manifolds
• Image Registration
• +7 more

Jan Lellmann is a Professor at the Institute of Mathematics and Image Computing of the University of Lübeck, with affiliations to Fraunhofer MEVIS . His research focuses on variational image processing , emphasizing the systematic formulation of prior knowledge into energy functions for improved accuracy and data efficiency. Applications span medical imaging , earth sciences , and biological data analysis . He develops non-smooth optimization methods for problems with combinatorial aspects like image segmentation . His recent work includes manifold-constrained optimization and quantum algorithms for imaging tasks. He has contributed software libraries like MFOPT (for manifold optimization) and COAL (for convex energy minimization). Scientific Awards: Best Student Paper Award at SSVM 2021 Honorable Mention at CVPR 2016 Recent Research Trends: Integration of quantum computing with classical image registration. Advancements in Riemannian geometry for protein dynamics and cryo-EM. Development of meta-learning frameworks for adaptive image alignment. Focus on non-smooth and higher-order regularization for sparse data reconstruction.

• Formal Methods
• Graph Databases
• Property Graph Schema
• +5 more

Ştefania-Gabriela Dumbravă is an Associate Professor at Institut Polytechnique de Paris (ENSIIE/Samovar Laboratory). Her research focuses on formal methods for graph database algorithms , including schema design, query processing, and verification. She contributes to international standards through co-chairing VLDB 2026 Demonstration Track and organizing workshops like VLDB Summer School 2025 . Dumbravă serves on editorial boards (e.g., ACM Transactions on Database Systems ) and program committees for top-tier conferences ( SIGMOD , ICDE ). Research Interests include formal verification of graph databases , property graph schema constraints , certified inference engines , and distributed graph systems . She integrates proof assistants (Coq, Isabelle) with practical database development, emphasizing reliability and mechanical correctness . Her work spans theoretical foundations (e.g., category theory , type theory ) and real-world applications (e.g., transit accessibility analysis , SDN controllers ). Selected Scientific Awards : EASST Best Software Science Paper Award (ICGT 2025) SIGMOD Best Paper Award (2023) VLDB Best Regular Paper Runner-Up (2022) D4Gen Hackathon 3rd Prize Teaching includes Graph Databases , Functional Programming , and Formal Languages at ENSIIE, with additional roles at Université Paris-Saclay, ENS Lyon, and Université Rennes 1. She supervises research internships and participates in gender parity initiatives (e.g., Paris-Saclay Women & Science Mentorship Program).

Jin-Dong KimView profile

Associate Professor

• Natural Language Processing (NLP)
• Bioinformatics
• Data Mining
• +3 more

Jin-Dong Kim is a Project Associate Professor at the Database Center for Life Science (DBCLS) , part of the Research Organization of Information and Systems (ROIS) . His work bridges Natural Language Processing and Bioinformatics through innovative projects like PubAnnotation and LODQA. Research focus: Biomedical text mining and knowledge base construction Key projects: OntoFinder, OntoCloud, and DialoQ systems Leadership roles: Convener of ISO TC37 SC4 WG1 on language resources Email: jdkim@dbcls.rois.ac.jp Kim's research interests span NLP , Bioinformatics , and Semantic Web technologies. His recent publications emphasize semantic interoperability , biomedical event extraction , and ontology-driven data integration . Community contributions include: Area Chair, ACL 2023 Resources and Evaluation track Chair, COLING 2024 Ethics Committee Editorial Board member for multiple journals including Journal of Language Resources and Evaluation His work has advanced question-answering systems , annotation frameworks , and biomedical knowledge graph construction through collaborative initiatives like the BioHackathon series.

Mutahira KhalidView profile

Researcher

• Semantic and Web Technologies
• Data Retrieval and Search Systems
• Neuro-symbolic AI
• +2 more

Mutahira Khalid is a Researcher at the Knowledge Infrastructures Lab under the German National Library of Science and Technology (TIB). With a Master’s degree in Information Technology from NUST Pakistan and a Bachelor’s in Information Technology from UAF Pakistan, her work focuses on neuro-symbolic AI frameworks for enhancing scientific data usability. Education: MSc & BSc in Information Technology Research: Semantic Technologies, Knowledge Graphs, Medical Data Systems Her publications highlight the application of knowledge graphs in medical data interoperability, neuro-symbolic approaches to trustworthy medical coding, and NLP-driven comorbidity risk scoring. She actively contributes to open science through her work on explainable AI systems for clinical code prediction. Contact: mutahira.khalid@tib.eu

Disha PurohitView profile

Research Fellow

• Artificial Intelligence
• Data Mining
• Knowledge Graphs
• +4 more

Disha Purohit is a Research Fellow at the Scientific Data Management Research Group of TIB (German National Library of Science and Technology) and a Ph.D. candidate in Computer Science at Leibniz Universität Hannover. She holds an MSc in Internet Technology and Information Systems from Leibniz Universität Hannover and a BSc in Computer Engineering from Dwarkadas J. Sanghvi College of Engineering, India. Current research focuses on Transparent AI over Knowledge Graphs with Causality Collaborates in TrustKG (Leibniz Association-funded) and P4-LUCAT (ERAMed-funded) projects Key research areas: Artificial Intelligence, Data Mining, Hybrid AI systems, and Medical Informatics Contact: disha.purohit@tib.eu , Phone: 0511 762-12153 Her recent publications prioritize neuro-symbolic AI integration, knowledge graph validation frameworks, and medical applications of hybrid systems. She has contributed to open-access datasets for lung cancer research and bias detection mechanisms in ML pipelines. Current projects emphasize explainability and causal reasoning in healthcare knowledge graphs. Research infrastructure includes the Leibniz Universität Hannover's Data Science & Digital Libraries framework and TIB's Scientific Data Management lab. Her work bridges symbolic reasoning with machine learning for clinical decision support systems.

Hassan HusseinView profile

Researcher

• Semantic and Web Technologies
• Human-Computer Interaction
• Web Accessibility
• +5 more

Hassan Hussein is a PhD student at Leibniz University Hannover and a Researcher in the Data Science & Digital Libraries group at TIB (Leibniz Information Centre for Science and Technology). His academic journey includes a Master's in Human-Computer Interaction from Siegen University, a Fulbright Scholarship for computer science studies in the U.S., and specialized training in web development from an IBM-managed program funded by the Egyptian government. His research integrates Semantic Web Technologies, Human-Computer Interaction, and Augmented Reality , with emphasis on accessibility and user-centered design. Key domains include natural user interfaces, UX engineering, and the application of knowledge graphs in scholarly communication. Recent publications focus on advancing the Open Research Knowledge Graph (ORKG) , addressing reproducibility, FAIR data principles, and AI-augmented research workflows. Earlier work explores augmented reality for civic engagement in urban planning. Awards: Fulbright Scholarship Egyptian Ministry of Communications Scholarship He contributes to TIB's research infrastructure and collaborates on projects involving semantic technologies, though no specific labs, teams, or grants are detailed in the source.

Gullal CheemaView profile

Researcher

• Multimodal Analysis
• News Analysis
• Fake News Detection
• +5 more

Gullal Cheema is a Researcher at the Visual Analytics department of Technische Informationsbibliothek (TIB), the German National Library of Science and Technology. Based in Hannover, Germany, Cheema is actively engaged in cutting-edge research at the intersection of computer vision, natural language processing, and multimedia analysis with a particular focus on news media and social platforms. Research interests primarily center around multimodal analysis, with specialization in news video processing, fake news detection, claim verification, and image-text relation understanding. Cheema's work bridges the gap between technical AI approaches and real-world media analysis challenges, particularly in the context of misinformation detection and verification. The research spans multiple modalities including text, images, and video, with applications in social media analysis, news verification, and hate speech detection. Cheema's publication record shows a consistent trajectory of impactful research in multimodal AI, with a strong emphasis on practical applications for media verification. Recent work has focused on speaker role identification in news videos, multimodal claim detection, and the analysis of image-text relations in news contexts. The research demonstrates expertise in both theoretical approaches and practical system development for complex multimodal challenges. Best Paper Award at International Conference on Multimedia Retrieval (ICMR 2024) for work on speaker roles and situation types in news videos Multiple publications at premier venues including ACM conferences, IEEE workshops, and Frontiers in Artificial Intelligence Active participation in major evaluation labs like CLEF CheckThat! and MediaEval Cheema is an active member of the Visual Analytics research group at TIB, contributing to projects that address contemporary challenges in media analysis and verification. The work has significant implications for understanding and combating misinformation in digital media landscapes.

Jan-Frederik PietschmannView profile

Professor

• Nonlinear Partial Differential Equations
• Gradient Flows
• Parameter Identification in PDEs
• +3 more

Jan-Frederik Pietschmann is a Professor for Inverse Problems at the University of Augsburg's Faculty of Mathematics, Natural Sciences and Technology. He previously held the same position at TU Chemnitz (2018–2023) and served as a Research Associate at institutions including the University of Münster and TU Darmstadt. University of Cambridge (PhD, Mathematics, 2008–2011) University of Münster (Diploma, Mathematics and Physics, 2008) Habilitation in Mathematics, University of Münster, 2018 His research focuses on the analysis of nonlinear PDEs , gradient flows , and parameter identification in PDEs . Recent work explores applications to cell invasion , pedestrian dynamics , neurite growth , and organic semiconductors . Publications highlight his expertise in optimal transport , cross-diffusion systems , and uncertainty quantification . Notable trends in his 15 most recent articles include Nonlinear PDEs for biological and physical systems Optimal transport on networks and metric graphs Computational methods for inverse problems His current team includes Gianna Götzmann, Carmen Tretmans, and Team Assistant Juliane Krautz. Contact details: jan-f.pietschmann@uni-a.de , +49 821 598-3926.

• Software Security
• Complex Systems
• Artificial Intelligence
• +6 more

Pierre Parrend is a researcher at INSA de Lyon's College of Engineering, focusing on software security, complex systems, and artificial intelligence applications. He has published extensively on topics including OSGi platform security, graph-based attack detection, and biologically inspired computing models. His research bridges cybersecurity with machine learning, emphasizing explainable AI for threat detection. Recent work includes Agentic AI frameworks for offensive security Graph processing methodologies for ML Multilayer architectures in cyber-physical systems Key collaborations include Pierre Collet, Stéphane Frénot, and Julio Navarro. While no formal education details appear in the data, his work spans 2006-2025 with significant contributions to zero-day attack detection and security architecture.

• Computer Science
• Education
• Machine Learning
• +3 more

Brian Broll is a Vanderbilt University researcher in the College of Engineering with a focus on computer science education , distributed computing , and machine learning accessibility . He has collaborated extensively with Dr. Ákos Lédeczi and other researchers to develop educational tools like NetsBlox and DeepForge for K-12 and higher education contexts.

• Algorithms
• Graph Theory
• Computational Complexity
• +4 more

Ivona Bezáková is a Professor in the Department of Computer Science at the B. Thomas Golisano College of Computing and Information Sciences, Rochester Institute of Technology. With a research career spanning nearly two decades, she has established herself as a prominent figure in theoretical computer science and computer science education, with over 70 publications from 2005 to 2025. Her academic journey shows progression from foundational work in theoretical algorithms to broader educational applications. Early research focused on sampling methods and counting problems in graphs, while recent work emphasizes innovative educational approaches using pencil puzzles and board games as teaching contexts. Bezáková's research spans algorithms , graph theory , and computational complexity , with particular contributions to sampling methods and counting problems. Her educational work has significantly influenced computer science pedagogy, especially through puzzle-based learning approaches. The evolution of her publications shows a strategic integration of theoretical foundations with practical educational applications. Her publication record demonstrates consistent productivity across top venues including SIAM Journal of Computing, ACM Transactions, and leading conferences such as STOC and SIGCSE. Recent work shows increasing focus on the intersection of theoretical computer science with educational applications and AI-enhanced learning tools. Over 20 publications in SIGCSE (computer science education conference) Multiple publications in top theoretical venues (SIAM, STOC, SODA) Long-standing collaborations with researchers like Daniel Stefankovic (19 papers), Leslie Ann Goldberg (13 papers), and Zack Butler (10 papers) As an educator and mentor, Bezáková has guided several students including Angelina Brilliantova, Hannah Miller, and Wenbo Sun. Her work on feedback tools for theoretical CS courses demonstrates commitment to improving student learning experiences. She leads research at the intersection of theoretical computer science and educational innovation, developing methods that help students grasp complex concepts through engaging activities.

Simon WernerView profile

Researcher

• Knowledge Graph Embeddings
• Temporal Knowledge Graphs
• Hypergraphs
• +4 more

Dr. Simon Werner is a Researcher at the University of Trier within Faculty II , specifically the Computational Linguistics and Digital Humanities team. He serves as a Study Advisor for the Language, Technology, Media program with a focus on Artificial Intelligence and Computational Linguistics and Natural Language Processing . His research focuses on Multimodal document representation , with key interests in Knowledge Graph Embeddings , Temporal Knowledge Graphs , Hypergraphs , Contextualized Embeddings , Natural Language Processing , Representation Learning , and Explainable AI . His recent publications explore dynamic entity representations, multimodal alignment using human perception, and temporally contextualized knowledge graph embeddings for recommendation tasks. The articles reveal strong contributions to Knowledge Graph Embeddings and NLP through innovative applications of Transformers , contextualization , and multimodal learning . The work bridges AI and digital humanities with technical advancements in representation learning .

• Machine Learning
• Big Data
• Computational Linguistics
• +4 more

Jakob Andersen is a Researcher at the Department of Computer Science, Faculty of Engineering and Computer Science, Hamburg University of Applied Sciences. His work focuses on machine learning, big data, and computational linguistics, with specific emphasis on text classification, uncertainty sampling, and human-in-the-loop systems. His recent publications explore real-time active learning, sustainable text classification, and edge device reliability. He contributes to open-source frameworks like Forum 4.0 and MINTFIT, focusing on user comment analysis and computer science education. Andersen is a member of the Association for Computational Linguistics (ACL) and has published extensively in top-tier conferences like ACL-IJCNLP, EACL, COLING, and IEEE BigData Congress since 2016.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +6 more

Xue Yang is a Professor in the Department of Computer Science and Engineering at Shanghai Jiao Tong University's School of Electronic Information and Electrical Engineering. With an extensive publication record spanning from 2023 to 2026, Dr. Yang has established herself as a prominent researcher in multiple interdisciplinary fields at the intersection of computer science and practical applications. Dr. Yang's research interests span a wide spectrum of cutting-edge computational fields including Artificial Intelligence , Machine Learning , Quantum Computing , Image Processing , and Healthcare Applications of AI . Her work demonstrates a remarkable ability to bridge theoretical computer science with practical applications across diverse domains such as biomedical informatics, oceanography, and e-commerce. The interdisciplinary nature of her research is evident in publications that combine computer vision with medical applications, quantum computing with image processing, and machine learning with environmental monitoring. An analysis of Dr. Yang's recent publications reveals a strong focus on developing novel algorithms and computational frameworks that address complex real-world problems. Her work shows particular strength in generative models , feature learning architectures , and data-driven optimization approaches. The consistent publication output across multiple high-impact venues demonstrates sustained research productivity and influence in her fields of expertise. Exponential stability analysis for nonlinear systems Advanced machine learning architectures for biomedical applications Quantum-classical hybrid computing models Blockchain applications for network security Computer vision techniques for specialized imaging scenarios

• Quantum Key Distribution
• Neuroscience
• Machine Learning
• +3 more

Dr. Yijun Wang is a researcher affiliated with the Chinese Academy of Sciences, State Key Laboratory on Integrated Optoelectronics, and Institute of Semiconductors in Beijing, China. His work spans multiple disciplines including quantum key distribution, brain-computer interfaces, and machine learning applications in cybersecurity and biomedical imaging. Key Research Areas: Quantum cryptography, Neural signal processing, Transformer-based models, Network security, and Digital health systems. Recent publications highlight advancements in EEG authentication systems 2026 , radar-based activity recognition 2025 , and quantum communication security 2025 . His work demonstrates interdisciplinary expertise bridging theoretical algorithms and practical engineering solutions. Scientific contributions include: Developing attention mechanisms for image restoration 2025 Creating reliability-enhanced BCIs via graph-driven fusion 2025 Designing semi-supervised solvers for CAPTCHA breaking 2021 Dr. Wang's collaborations appear across journals like Pattern Recognition , IEEE Transactions , and conferences including AAAI 2025 , ICLR 2025 , and EMBC 2023 . Current projects integrate LLMs with digital phenotyping for hypertension management 2025 and explore self-taught reasoning mechanisms 2025 .