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Lin QikaView profile

Research Fellow

• Natural Language Processing
• Artificial Intelligence in Healthcare
• Multi-modal Representation Learning
• +3 more

Lin Qika is a Research Fellow at the Saw Swee Hock School of Public Health, National University of Singapore (NUS). His research focuses on advancing natural language processing (NLP) and AI applications in healthcare, particularly leveraging large language models (LLMs) for robust healthcare solutions. He holds a PhD from Xi’an Jiaotong University (2023), an M.S. from Beijing Institute of Technology (2019), and a B.S. from the same institution (2016). His expertise spans multi-modal representation learning, neuro-symbolic systems, and logical reasoning applied to healthcare challenges. Notable research includes developing frameworks like TECHS for explainable extrapolation reasoning and integrating knowledge graphs with LLMs. His work emphasizes practical healthcare applications, such as depression detection and medical diagnostics. Lin Qika’s recent publications (2022–2025) explore cutting-edge topics like contrastive graph representations, knowledge graph completion, and adversarial attacks on knowledge embeddings. He actively contributes to conferences like ACL, SIGIR, and KDD, showcasing innovations in AI-driven healthcare and multimodal reasoning. No scientific awards or funded grants are explicitly mentioned in the provided information.

Serge GaspersView profile

Professor

• Algorithms and complexity
• Graph algorithms
• Quantum algorithms
• +8 more

Professor Serge Gaspers is a faculty member in the School of Computer Science and Engineering at the University of New South Wales (UNSW), specializing in algorithms for computationally intractable problems. His research focuses on parameterized algorithms, quantum algorithms, and graph theory, with applications in computational social choice and constraint satisfaction. He joined UNSW in 2012 as an ARC DECRA Fellow and later held an ARC Future Fellowship. Gaspers obtained his PhD from the University of Bergen (Norway) in 2008, followed by postdoctoral positions in Montpellier, Santiago, and Vienna. His research interests include algorithms for NP-hard problems, quantum computing, and fair resource allocation. He has been awarded grants totaling over A$2 million, including an ARC Discovery Project (DP210103849) on improved algorithms via random sampling and collaborations with Data61/CSIRO and NICTA. Notable awards include the ARC Future Fellowship (2014), IJCAI 2013 Most Educational Video Award, and DECRA (2012). Teaching: Gaspers teaches COMP6741 - Algorithms for Intractable Problems . His advising spans parameterized algorithms, quantum algorithms, and graph algorithms. Research grants highlight his work in algorithms, with a focus on turbocharging heuristics and computational complexity of resource allocation problems.

Manfred JaegerView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Bayesian Networks
• +5 more

Manfred Jaeger is an Associate Professor at the Department of Computer Science, Technical Faculty of IT and Design, Aalborg University. His research focuses on Artificial Intelligence , Bayesian Networks , and Graph Neural Networks , with significant contributions to probabilistic reasoning and relational learning. University: Aalborg University School: Technical Faculty of IT and Design Department: Department of Computer Science Jaeger's research explores inductive and probabilistic reasoning , statistical relational learning , and model checking . His recent work integrates heterogeneous graph neural networks with relational Bayesian network encodings to enhance reasoning capabilities in complex systems. Key trends in his publications include relational deep learning , probabilistic inference , and graph-based modeling . He has contributed to applications in social network community detection , reinforcement learning for MDPs , and latent variable models for graph learning . Jaeger collaborates on projects involving incomplete data analysis , modularization of complex tasks , and probabilistic logic . His datasets on multi-multi-instance learning networks are publicly available for research use.

DENG YangView profile

Assistant Professor

• Natural Language Processing
• Information Retrieval
• Large Language Models
• +7 more

Dr. Yang Deng is a tenure-track Assistant Professor at the School of Computing and Information Systems, Singapore Management University, and a Lee Kong Chian Fellow. Previously, he was a Postdoctoral Research Fellow at NExT++ (National University of Singapore). His research focuses on Natural Language Processing, Information Retrieval, and Large Language Models, with special interests in Proactive Conversational AI, Trustworthiness of LLMs, and Human-Centered Information Seeking. He has published over 40 papers in top-tier venues including ACL, EMNLP, WWW, and SIGIR. PhD from The Chinese University of Hong Kong (2023) Research Advisor to CHEANG Chi Seng Research Domains: Natural Language Processing Information Retrieval Large Language Models Human-Agent Interaction Digital Transformation Trustworthy AI Scientific Recognition: Lee Kong Chian Fellowship Google South Asia & Southeast Asia Research Awards 2024 EMNLP 2024 Outstanding Area Chair NeurIPS 2024 Best Reviewer

Mel ChekolView profile

Assistant Professor

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

Mel Chekol is an Assistant Professor at Utrecht University, affiliated with the Data Intensive Systems group within the Science faculty. He holds a PhD from INRIA Rhône-Alpes and a double MSc from Vienna University of Technology and Free University of Bozen-Bolzano. His research focuses on knowledge graphs, spatio-temporal data integration, probabilistic inference, and scalable machine learning applications. Previously, he worked at institutions including INRIA Nancy Grand Est, University of Mannheim, and the National Institute of Informatics in Tokyo. Key research interests include reasoning in knowledge graphs, temporal data modeling, and applying language models to enhance knowledge representation. He contributes to projects like the Utrecht Platform for Applied Data Science and has collaborated on frameworks such as the EXMO and WAM teams. His work emphasizes practical applications of AI and data science in governance and sustainability. Mel has published extensively in venues like VLDB Journal, ISWC, and AAAI, focusing on topics like rule learning, temporal knowledge graphs, and scalable inference systems. His research bridges theoretical advancements with real-world data challenges.

Raj SunderramanView profile

Professor

• Databases
• Data Mining
• Big Data
• +6 more

Raj Sunderraman is a Professor of Computer Science at Georgia State University, specializing in databases, data mining, and logic programming. His research focuses on deductive databases, semantic web technologies, bioinformatics, and graph data modeling. He holds a B.E. (Honors) in Electronics Engineering from Birla Institute of Technology and Science, an M.Tech. in Computer Technology from Indian Institute of Technology Delhi, and a Ph.D. in Computer Science from Iowa State University. His research projects include NeuronBank—a tool for cataloging neuronal circuitry—and work on scalable graph storage systems for big data. He has developed a programming environment for protein structure data and contributed to the paraconsistent relational data model. His teaching and research emphasize practical applications in bioinformatics, geoinformatics, and software systems. Key areas of exploration include reasoning with incomplete/inconsistent data, deductive database semantics, and graph query languages. Recent work involves lambda calculus visualization tools and 3D perception benchmarks for UAVs. He has authored over 150 publications and a textbook on Oracle 10g programming.

Davide MottinView profile

Associate Professor

• Graph Theory
• Machine Learning
• Data Mining
• +4 more

Davide Mottin is an Associate Professor at the Department of Computer Science, Aarhus University. His primary research focuses on graph theory, machine learning, and data mining, with significant contributions to knowledge graphs, algorithm design, and interdisciplinary applications in drug discovery and material science. He holds a leadership role in large international conferences such as CIKM 2024 as a Program Chair. His research explores scalable graph algorithms (e.g., subgraph matching, alignment), robust knowledge graph cleaning, and leveraging large language models for scientific tasks. Mottin has pioneered work on spectral methods for graph analysis (e.g., NetLSD, VERSE embeddings) and developed frameworks for interactive data exploration (e.g., X2Q, MetaExp systems). Key contributions include FUGAL for graph alignment and Ucode for community detection Active in reproducibility efforts, as seen in retraction notices and algorithmic redesigns Focus on practical applications in drug discovery via evolution-based models (EvolMPNN) He has authored over 60 peer-reviewed publications and holds grants supporting interdisciplinary research at the intersection of computer science and life sciences. Mottin is affiliated with the university's AI and data science initiatives, contributing to both theoretical advancements and real-world system implementations.

Ognjen SavkovicView profile

Assistant Professor

• Knowledge Graphs
• Semantic Web
• Database Management
• +7 more

Ognjen Savkovic is an Assistant Professor (RTD-a) at the KRDB Research Center for Knowledge and Data, Faculty of Engineering, Free University of Bozen-Bolzano, Italy. He is based at the NOI Techpark in Bolzano and is actively involved in research on Knowledge Graphs, Semantic Web, and data quality. His work bridges formal logic and machine learning to enhance data management systems. His research interests include Knowledge Graphs, Semantic Web, Database Management, Data Quality, Artificial Intelligence, and Logic Reasoning. He focuses on schema validation (e.g., SHACL), property graph schemas (PG-Schema), and integrating machine learning with declarative languages like Datalog for industrial applications such as welding quality monitoring and cloud resource configuration. His recent publications show a strong trend in semantic technologies for industrial applications, particularly in collaboration with Bosch and Siemens. His work spans theoretical foundations of schema languages and practical implementations in smart manufacturing and cloud systems. He has published in top venues including WWW, ISWC, CIKM, and SIGMOD. Scientific Awards: No specific awards mentioned. Ognjen Savkovic advises students and collaborates on research projects, though specific students are not listed. His research involves significant grant-funded collaborations, particularly in EU-level industrial AI and semantic technology initiatives. He has contributed to projects involving Siemens and Bosch, focusing on semantic diagnostics and scalable data science solutions. He is a key member of the KRDB Research Center, contributing to a vibrant research team working on knowledge representation, reasoning, and industrial applications of semantic technologies.

• Conceptual Modeling
• Knowledge Graphs
• Model-Driven Engineering
• +5 more

Syed Juned Ali is a Researcher (Univ.Ass.) at TU Wien's Business Informatics Group. His work focuses on conceptual modeling, knowledge graphs, and model-driven engineering. He holds BSc and MSc degrees and contributes to academic publications in top venues like Science of Computer Programming. Key projects include CM2KGcloud (a knowledge graph transformation platform) and GGMF (a modularization framework). He teaches Model Engineering (VU 188.923) and collaborates with Prof. Dominik Bork on AI-driven decision systems. Professional activities include conference paper reviews and contributions to enterprise architecture research. Research interests span AI integration in decision models, automated model modularization, and cross-language modeling tools. His work bridges theory and practice through tools like EAKG Toolkit for enterprise architecture analysis and genetic algorithm-based frameworks for scalable model decomposition.

• Cybersecurity
• Information Security
• Security Assurance
• +7 more

Basel Cat is a Professor and Head of the Department of Information Security and Communication Technology at NTNU's Faculty of Information Technology and Electrical Engineering. His research focuses on cybersecurity, information security assurance, and innovative approaches to cyber range technologies. Key areas include vulnerability analysis, privacy protection, and AI-driven security solutions. Research Interests Cybersecurity incident triage and attack chain analysis Quantitative security evaluation models for web and cloud services Automated vulnerability injection in source code IoT security and cognitive digital twin architectures Adversarial AI applications in cybersecurity exercises Publications highlight advancements in cybersecurity education, attack-defense scenario modeling, and the integration of AI/LLMs in security frameworks. Recent work explores frameworks for evaluating cloud services and OWASP-aligned web application security standards. Collaborations involve development of hybrid IoT cyber ranges and frameworks for security assurance metrics. His research has been featured in top-tier journals like Computers & Security and Journal of Information Security and Applications .

Jakob Lykke AndersenView profile

Associate Professor

• Algorithms
• Graph Transformation
• Chemical Reaction Networks
• +7 more

Jakob Lykke Andersen is an Associate Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark (SDU), where he conducts research in algorithms with applications in cheminformatics and complex systems. He also holds a former external appointment as a Research Fellow at the Tokyo Institute of Technology (2015–2017). Research Interests: His work lies at the intersection of computer science and theoretical chemistry, focusing on algorithmic methods for analyzing chemical reaction networks. He employs hypergraphs, mixed-integer linear programming, and probabilistic models to study metabolic pathways, reaction databases, and prebiotic systems. His research emphasizes computational efficiency, formal modeling, and software implementation. Publication Trends: His recent publications (2019–2025) show a consistent focus on graph-based modeling of chemical systems, rule extraction from reaction databases, thermodynamic feasibility, and stochastic analysis. These works appear in high-impact journals in cheminformatics, bioinformatics, and complex systems, reflecting strong interdisciplinary collaboration. Scientific Contributions: While no specific awards are listed, his sustained research output and leadership in funded projects highlight significant contributions to algorithmic cheminformatics. Grants and Projects: He is actively involved in two major ongoing research projects: (1) Software Infrastructures for Teaching at Scale (funded by Innovation Fund Denmark, 2022–2025), and (2) DIREC (Danish Research Center for Digital Economy, 2020–2025), indicating active engagement in both educational technology and core computational research. Advising and Outreach: While no students are listed, he participates in academic advising through project supervision. He contributes to public discourse through media appearances on topics such as mathematics in health (e.g., intestinal system modeling in obesity) and educational well-being. Labs and Teams: He collaborates with interdisciplinary teams, including researchers from bioinformatics, chemistry, and computer science, particularly through projects involving Merkle, Flamm, Fagerberg, and Stadler. His work is associated with algorithmic cheminformatics and software framework development groups at SDU.

• Natural Language Processing
• Computer Vision
• Multimodal Learning
• +2 more

Shantanu Nath serves as a Research Fellow in the Department of Information Engineering and Computer Science at the University of Trento, hosted by the Interdepartmental Center for Mind/Brain Sciences (CIMEC). His work bridges computer science and cognitive neuroscience through interdisciplinary research. His research interests span several key areas: Natural Language Processing Computer Vision Multimodal Learning Knowledge Representation Cognitive Science He specifically investigates integrating lexical knowledge bases like WordNet with multimodal neural networks such as CLIP for image-language understanding tasks, focusing on semantic disambiguation and cognitive modeling applications. Nath's recent publication in SemEval-2023 demonstrates a trend toward hybrid AI systems combining structured knowledge resources with deep learning for complex multimodal challenges, reflecting growing interest in interpretable and knowledge-enhanced AI approaches. He is actively affiliated with CIMEC, an interdepartmental research center fostering collaboration between engineering, computer science, and neuroscience to advance the scientific study of mind and brain mechanisms.

Jieying ChenView profile

Assistant Professor

• Knowledge Representation and Reasoning
• Semantic Web
• Ontology Engineering
• +5 more

Roles and Affiliations: Jieying Chen is an Assistant Professor at the Faculty of Science, Department of Artificial Intelligence at Vrije Universiteit Amsterdam (Netherlands). She is also affiliated with the Network Institute. Previously, she held roles as Senior Research Associate at the University of Oxford, Postdoctoral Researcher at the University of Oslo, and Research Associate at the University of Manchester. She has industrial experience as an IT consultant at Boston Consulting Group (Nordics). Education: PhD from Université Paris-Saclay (France), Master's in Computational Logic (TU Dresden, Germany via Erasmus Mundus scholarship). Research Interests: Focus on Knowledge Representation and Reasoning (Description Logics, Ontology Modularity), Semantic Web (Ontology Modeling), AI for Social Goods (Bias Detection with LLMs), and applications in Cyber Physical Systems. Combines KR with NLP/ML techniques for ontology alignment and knowledge extraction. Recent Trends in Work: Recent publications emphasize ontology text alignment, bias detection in governmental documents using LLMs, and knowledge-based fault diagnosis in industrial systems. Her work bridges symbolic AI with machine learning, particularly in sustainability and industrial applications. Grants and Projects: Lead WP2 in the Zorro project (NWO-funded), Co-PI on VU-UT Alliance grant for fairness in HRM via LLM bias mitigation. Collaborates on ConCur (EPSRC-funded) and SIRIUS (Norwegian R&D center). Labs/Teams: Active in the Zorro project consortium involving ASML, Philips, and TNO-ESI. Leads ontology engineering initiatives in collaboration with industry partners like Bosch and Aibel AS.

• Algebraic Geometry
• Low-Dimensional Topology
• Machine Learning
• +3 more

Benjamin Ruppik is a researcher at the Chair of Algebraic Geometry within the Faculty of Mathematics and Natural Sciences at Heinrich-Heine-Universität Düsseldorf. His work bridges pure mathematics and applied machine learning, focusing on topology-driven approaches to computational problems. He holds a PhD (2022) and a Master's in Mathematics (2018), with expertise in low-dimensional topology and its intersections with natural language processing and dialogue systems. Research Interests Algebraic Geometry 4-Manifold Topology and Homotopy Classification Applications of Topology in Machine Learning Dialogue Systems and Emotion Recognition Active Learning and Label Correction Recent Research Trends Ruppik's recent articles emphasize integrating topological methods into machine learning frameworks, particularly in analyzing latent spaces of language models and enhancing dialogue systems with emotion-aware components. His work on 4-manifold classification demonstrates foundational contributions to geometric topology. Awards and Grants No specific awards or grants mentioned in the provided text. Labs and Collaborations Part of the Chair of Algebraic Geometry at HHU, collaborating on projects merging pure mathematics with computational applications.

• Technology Enhanced Learning
• User/Student Modeling
• Intelligent User Interfaces
• +4 more

Dr. Beate Grawemeyer is an Assistant Professor in the CEES School of Science at Coventry University, specializing in computer science and artificial intelligence applied to education. Her work focuses on adaptive learning environments, user modeling, and affective computing to enhance learning experiences, particularly for neurodiverse learners. She holds a Doctorate in Computer Science and Artificial Intelligence from the University of Sussex (2007) and an MSc in Knowledge-Based Systems (1999). Education: Doctorate (2007) and MSc (1999) from University of Sussex Her research interests include technology-enhanced learning, intelligent user interfaces, and the design of systems that adapt to individual learner characteristics. Recent work explores affect-aware feedback in programming education, synthetic data generation for student risk prediction, and the impact of non-technical explanations in AI systems. Her publications span adaptive feedback mechanisms, emotion-aware learning environments, and the cognitive impacts of external representation use. Collaborations focus on interdisciplinary applications of AI in education and neurodiversity-inclusive design.