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Laurence BrandenbergerView profile

Researcher

• Political Science
• Legislative Politics
• Political Networks
• +4 more

Laurence Brandenberger is a Senior Researcher at the Chair of Systems Design, ETH Zurich. He holds a PhD and Master's degree in Political Science and Sociology from the University of Bern. His research focuses on legislative politics, political networks, and temporal dynamics of political collaboration. He employs computational methods like machine learning and network analysis to study decision-making processes and democratic transparency. Notable contributions include analyzing Swiss parliamentary records and developing relational event models. His work bridges theoretical insights with practical tools for enhancing political transparency, such as the DemocraSci Knowledge Graph. Education: PhD in Political Science (University of Bern), MA in Sociology (University of Bern) Affiliations: Chair of Systems Design, ETH Zurich His research interests prioritize understanding how social relations influence political decisions, spill-over effects in policy-making, and strengthening democratic transparency through accessible data platforms. He has authored multiple studies on legislative behavior, policy networks, and computational methodologies in political science.

Guanfeng LiuView profile

Professor

• Recommender Systems
• Privacy-Preserving Technologies
• Graph Neural Networks
• +5 more

Guanfeng Liu is the Vice Dean at the NNU-MQ Joint Institute, Macquarie University, within the Faculty of Science and Engineering. His research focuses on Recommender Systems, Privacy-Preserving Technologies, Graph Neural Networks, and Data Analytics. He leads projects like the 'Intelligent Health Data Analytics Platform' and 'Data-intensive Scheduling Optimisation in Database Systems.' His work emphasizes ethical AI, with contributions to trust prediction, spatio-temporal data analysis, and privacy-aware algorithms. In 2023, he won the Faculty of Science and Engineering Award for Inter-School Collaboration. Liu has published extensively in top journals like IEEE Transactions and ACM Conferences, with over 200 publications and a h-index of 34. Key projects include scalable document intelligence platforms and confidentiality preservation in graph learning. His research bridges theoretical advancements with practical applications in smart cities and healthcare, leveraging collaborative filtering and contrastive learning techniques.

• Recommender Systems
• Privacy Issues in AI
• Fake News Detection
• +3 more

Özlem Özgöbek is an associate professor at the Department of Computer Science at NTNU, focusing on recommender systems, privacy in AI, and fake news detection. She holds a part-time role as a program manager at the Norwegian Research Center for AI Innovation (NorwAI) since November 2023. She coordinates the International Work-Integrated-Learning in Artificial Intelligence (IWIL AI) project and actively participates in organizing committees such as the INRA Workshop Series and the Norwegian Big Data Symposium (NOBIDS). She collaborates with the Center for Excellent IT Education (Excited) center, contributing to advancements in semantic web technologies and machine learning applications in education. Her research bridges academic and industrial challenges, emphasizing ethical AI deployment and user-centric systems. Key contributions include multimodal fake news detection frameworks, educational technology innovations, and sustainable wardrobe recommendation systems leveraging Linked Open Data. She has organized multiple international workshops (e.g., INRA 2021–2023) and contributed to benchmarking initiatives like MediaEval’s news image analysis tasks. Her academic work spans over 30 peer-reviewed publications since 2014, including studies on classroom interaction tools, privacy perceptions in recommender systems, and the Adressa news dataset for evaluation purposes. She maintains strong ties with collaborative projects such as the IWIL AI initiative, fostering international education-industry partnerships in AI.

• Probabilistic Graphical Models
• Machine Learning
• Deep Generative Models
• +7 more

Thomas Dyhre Nielsen is a Professor (MSO) in the Department of Computer Science at Aalborg University . He is a member of the Distributed, Embedded and Intelligent Systems (DEIS) research group and contributes to the Artificial Intelligence and Machine Learning team at the university. His primary research interests lie in the theoretical and applied aspects of probabilistic graphical models , machine learning , and deep generative models . His work spans from foundational methods for learning and inference to the development of frameworks for solving complex decision problems. He is the co-author of the authoritative textbook Bayesian Networks and Decision Graphs . His recent research output reveals a strong trend towards interdisciplinary applications. His 15 most recent publications demonstrate significant work in bioinformatics , using graph neural networks and variational autoencoders for metagenomic binning, and in urban infrastructure , applying reinforcement learning and probabilistic models to optimize stormwater and wastewater management systems. He also has impactful research in healthcare , developing Bayesian network models for clinical risk prediction. Senior area editor for the International Journal of Approximate Reasoning . Principal or Co-Investigator on research projects such as the AMIDST project (developing a Java toolbox for scalable probabilistic machine learning) and PGM 2020 (organizing the International Conference on Probabilistic Graphical Models). Actively supervising research, as evidenced by a current opening for a PostDoc position in probabilistic machine learning. Professor Nielsen leads a research lab focused on probabilistic machine learning, which is part of the larger DEIS and AI/ML teams at Aalborg University. His group develops and applies advanced models to real-world problems in environmental science, healthcare, and intelligent systems.

• Artificial Intelligence
• Data Mining
• Machine Learning
• +7 more

Dr. M. M. El-Gayar is a faculty member at Mansoura University, affiliated with the Faculty of Computer and Information and the Department of Computer Science. He is also associated with New Mansoura University, reflecting his active role in Egypt's evolving academic landscape in computer science and engineering. His research spans key domains in modern computing, focusing on Artificial Intelligence , Data Mining , Deep Learning , and their applications in healthcare , cybersecurity , and the Internet of Things (IoT) . His work integrates computational intelligence to solve real-world problems such as disease diagnosis, medical image analysis, and secure digital systems. The recent publications highlight a strong trend toward applying hybrid and deep learning models in medical diagnostics, including breast cancer and heart disease prediction, as well as semantic analysis for pneumonia detection in pandemic contexts. His IoT research emphasizes optimization for device localization, while cybersecurity efforts focus on phishing mitigation. These interdisciplinary efforts reflect a cohesive research vision centered on intelligent systems for societal benefit. Dr. El-Gayar has received research support from prestigious organizations including the United States Agency for International Development and the British Council , underscoring the international relevance and impact of his work. He has contributed to multiple high-impact journals such as IEEE Access , Computers, Materials and Continua , and the International Journal of Electrical and Computer Engineering , demonstrating consistent scholarly output and collaboration.

Sajad HomayounView profile

Assistant Professor

• Cybersecurity
• Machine Learning
• Artificial Intelligence
• +7 more

Sajad Homayoun is a Tenure Track Assistant Professor at Aalborg University's Department of Electronic Systems within The Technical Faculty of IT and Design. His research focuses on integrating Artificial Intelligence with cybersecurity to develop advanced solutions for threat detection and mitigation. PhD in Computer Science Specialization in Cybersecurity & Machine Learning His technical expertise spans: AI-driven intrusion detection systems Adversarial machine learning (AML) Explainable AI (XAI) for security transparency Time series and graph analysis MLOps implementation with MLflow Python development using pandas, scikit-learn, and TensorFlow Research trends from his publications show strong emphasis on: Combating phishing attacks through ML Applying deep learning to crowdsensing trustworthiness Failure prediction in industrial systems Extracting behavioral patterns from ransomware Blockchain-based security applications He teaches Network Security courses during fall semesters and maintains active collaborations with researchers across Denmark and international institutions.

Gerhard WeikumView profile

Professor

• Databases
• Information Systems
• Knowledge Graphs
• +7 more

Gerhard Weikum is a leading professor and researcher in the field of databases and information systems at the Max Planck Institute for Informatics, part of the Max Planck Society in Saarbrücken, Germany. He heads the Databases and Information Systems department, where his work bridges foundational database research with cutting-edge applications in knowledge graphs, information retrieval, and natural language processing. His research interests include knowledge graph construction and curation, data integration, question answering, conversational search, and the integration of large language models with structured knowledge. He has made significant contributions to the development of methods for creating comprehensive and reliable knowledge bases from web and text sources, with a focus on scalability, quality, and usability. The recent publications highlight a strong trend towards hybrid AI systems that combine the strengths of knowledge graphs and large language models, particularly in the domains of conversational search, recommendation systems, and complex question answering over heterogeneous data. His work emphasizes efficiency, robustness, and faithfulness in AI-generated responses. ACM Web Conference WSDM CIKM Advances in Information Retrieval Journal of Web Semantics IEEE TKDE EMNLP He has advised numerous PhD students and researchers who have gone on to make their own contributions in areas such as knowledge base completion, data curation, and question answering. His scientific work is highly collaborative, involving researchers from various institutions and industrial partners. Gerhard Weikum is a central figure in the database and semantic web research communities, with a sustained record of high-impact publications and leadership in advancing the field of information systems.

Katarzyna Grzesiak-KopećView profile

Assistant Professor

• Computational Intelligence
• Machine Learning
• Computer-Aided Design
• +2 more

Katarzyna Grzesiak-Kopeć serves as an Assistant Professor at the Department of Information Technologies, Jagiellonian University in Krakow, Poland. Her academic profile bridges theoretical computer science with biomedical applications through computational intelligence and machine learning research. Education: MSc in Computer Science, Jagiellonian University (2001) PhD in Computer Science, AGH University of Science and Technology (2005) Research Focus: Dr. Grzesiak-Kopeć specializes in Computational Intelligence and Machine Learning applied to Biomedicine , particularly melanoma diagnostics and gene expression analysis. Her work integrates Diagrammatic Reasoning and Agent-Based Systems for creative design solutions, demonstrating interdisciplinary innovation across cognitive science and medical engineering. Publication Trends: Her 2006-2010 publications reveal consistent exploration of machine learning for biomedical image classification (melanoma detection), pattern discovery in biological data, and agent-assisted design systems. Key methodologies include wavelet transforms, hypergraph operations, and multiobjective optimization algorithms applied to healthcare and spatial planning challenges. Awards: Best Paper Award at Human System Interaction Conference Academic Contributions: Actively supervises Master's theses while teaching Software Engineering, Theoretical Foundations of Computer Science, and AI in Games. Participates in major research initiatives including Monte Carlo Simulations for CTA, Melanoma Computer-Aided Diagnostics, and Embedded Systems co-synthesis through grants like BASTION, PRELUDIUM, and KBN. Associated with Juna Network Academy for academic collaboration.

• Artificial Intelligence
• Knowledge Representation
• Automated Reasoning
• +3 more

Yuliya Lierler is a full Professor of Computer Science at the University of Nebraska Omaha (UNO), affiliated with the College of Information Science & Technology (IS&T). She holds the Cheryl Prewett Diamond Professorship since 2020 and received the Mentor of the Year Award in 2025. Her research focuses on artificial intelligence, particularly knowledge representation, automated reasoning, declarative problem solving, and natural language understanding. She co-directs the NLPKR lab and has authored over 70 peer-reviewed articles in top AI venues. Her books include Discrete Mathematics in a Nutshell and Digital Minimalism for Teens . Education: Ph.D. in Computer Science from the University of Texas at Austin (2010). Professional development includes training in AI-driven teaching methodologies and online education. Research Interests: She explores formal methods in AI, including logic programming semantics (e.g., Answer Set Programming), automated reasoning techniques, and applications in natural language processing. Her work bridges theoretical foundations with practical tools, such as the text2ALM information extraction system. Service & Leadership: Co-Chair of the 38th International Conference on Logic Programming (2022), Executive Committee member of the Association for Logic Programming (since 2020), and former Graduate Program Committee Chair (2018–2020). She has organized multiple international conferences and contributed to AI education initiatives. Awards: Recipient of the 2025 Mentor of the Year Award (Nebraska Women in Tech), 2024 Outstanding Research Award (IS&T College), and 2016 Best Student Paper Award (with Amelia Harrison). Labs & Teams: Co-director of the NLPKR lab, focusing on natural language processing and knowledge representation. Collaborates on projects integrating declarative programming with semantic understanding.

Fanzhen LiuView profile

Research Fellow

• Graph Mining
• Anomaly Detection
• Trustworthy AI
• +3 more

Dr Fanzhen Liu is a Postdoctoral Research Fellow at Macquarie University's School of Computing and a Visiting Scientist at CSIRO’s Data61 since March 2024, specializing in graph-based artificial intelligence research. He earned his PhD from Macquarie University in 2024, following Master of Research studies where he received the Executive Dean's Commendation for Academic Excellence. His research pioneers graph mining , anomaly detection , and trustworthy AI using deep learning methodologies. Key contributions include novel frameworks for graph anomaly detection with limited labels and comprehensive surveys on deep learning for community detection, addressing critical challenges in network security and social media analysis. His work bridges theoretical advancements with practical applications like e-commerce fraud detection. Analysis of his publication trajectory reveals consistent focus on graph neural networks and unsupervised learning , with increasing emphasis on real-world deployment challenges in trustworthy AI systems. Recent outputs demonstrate methodological innovations in data augmentation and residual analysis for graph-structured data. His distinguished recognition includes: Most Influential IJCAI2020 Paper Award HDR Research Rising Star (2022) Vice-Chancellor’s Commendation for Academic Excellence (2024) As a Postdoctoral Research Fellow, Dr Liu actively collaborates with CSIRO’s Data61 and contributes to Macquarie University's research ecosystem through high-impact publications and methodological innovations, though specific grant details and student supervision are not documented in the provided materials. He operates within Macquarie's School of Computing research community and maintains strategic partnerships with national research agencies through his CSIRO affiliation.

Yinghui WuView profile

Associate Professor

• Database Systems
• Data Management
• Data Mining
• +5 more

Yinghui Wu is the Theodore L. & Dana J. Schroeder Associate Professor in Computer Science & Engineering at Case Western Reserve University, affiliated with the Department of Computer and Data Sciences within the Case School of Engineering. His research focuses on devising novel data management and analysis techniques for applications in materials science, cyber-physical systems, cybersecurity, and energy systems. He holds a PhD in Computer Science from the University of Edinburgh (2011) and a BSc from Peking University (2007). Education: PhD Computer Science, University of Edinburgh, 2011 Bachelor of Science in Computer Science, Peking University, 2007 His research interests span Database Systems , Data Mining , and Applied Machine Learning , with multidisciplinary applications in energy systems and cybersecurity. Notable contributions include work on photovoltaic performance prediction using graph neural networks and anomaly detection in transmission protection systems. His awards include the 2023 IEEE Best Reviewer Award, 2020 IEEE BigData Best Paper Award, and 2015 ACM Google Faculty Research Award. He teaches undergraduate and graduate courses in data systems and data management. Recent publications highlight advancements in cyber-physical security analytics, knowledge graph fact checking, and ensemble-based anomaly detection in smart grids.

Patrick StöckleView profile

Lecturer

• IT Security
• Security Configuration
• Cloud Security
• +3 more

Patrick Stöckle is a Lecturer at the Technical University of Munich (TUM) and a Research Scientist at Siemens AG. His primary roles include cloud/container security research, academic teaching, and industry collaboration. He completed his Dr. rer. nat. (PhD) in Informatics at TUM (2017–2024), focusing on security configuration under Prof. Alexander Pretschner. Previously, he earned an M.Sc. (2017) and B.Sc. (2016) in Informatics from Karlsruhe Institute of Technology (KIT), specializing in software engineering and security. His research interests revolve around IT security configuration, DevSecOps automation, and secure software development. He co-developed tools like Scapolite for automated security hardening and contributed to industry standards like CIS Benchmarks. He has published at top conferences including IEEE/ACM ASE and ACM CODASPY. Teaching-wise, he led the Security Engineering course at TUM (2019–2022), introducing programming exercises that boosted student engagement. He also supervised 13 bachelor/master theses. He actively contributes to academic talks and industry workshops, focusing on container security, CI/CD optimization, and security configuration best practices.

• Data Mining
• Process Mining
• Knowledge Representation
• +5 more

Antonella Guzzo is an Assistant Professor (ING-INF/05) at the Faculty of Engineering, University of Calabria, affiliated with the Department of Electronic Informatics and Systems (DEIS). Her research spans Data Mining, Process Mining, Federated Learning, and Knowledge Representation, with applications in Cyber Security and Bioinformatics. Laurea (2000) and Ph.D. (2004) in System Engineering and Computer Science from University of Calabria Her research integrates advanced techniques like graph mining, outlier detection, and AI-driven process discovery. She contributes to projects like TOCAI.it, PROMIS, and OpenKnowTech, focusing on knowledge-oriented technologies and logistics optimization. Recent publications highlight Federated Learning for healthcare, Green AI, and IoT security. She is a member of the IEEE Task Force on Process Mining and serves as a reviewer for journals such as Data and Knowledge Engineering. Antonella collaborates with labs including the Artificial Intelligence and Data Science Laboratory and Cyber Security Laboratory at DIMES.

Hongyi ZhuView profile

Assistant Professor

• Artificial Intelligence
• Cybersecurity
• Mobile Health Analytics
• +6 more

Dr. Hongyi Zhu serves as an Assistant Professor in the Department of Information Systems and Cyber Security at the Alvarez College of Business, The University of Texas at San Antonio (UTSA), leveraging his Ph.D. from the University of Arizona and Bachelor's from Tsinghua University to advance interdisciplinary research at the AI-health-security intersection. Education: Ph.D. in Management Information Systems, University of Arizona Bachelor of Business Management, Tsinghua University His research pioneers artificial intelligence-based analytics for mobile health, mental health, and cybersecurity, utilizing deep learning and network analysis to address critical challenges in proactive threat intelligence and senior care. Publications span premier venues including MIS Quarterly and IEEE Transactions on Knowledge and Data Engineering, reflecting his multidisciplinary approach bridging computer science, information systems, and biomedical informatics. Analysis of his 15 most recent publications reveals a dominant focus on applying graph embedding and federated learning to cybersecurity vulnerabilities (40% of works), depression detection via sensor fusion (25%), and privacy-preserving AI frameworks (35%), with consistent methodology innovation in transfer learning and uncertainty quantification across health and security domains. Dr. Zhu teaches Computer Networking and Telecommunication Systems while maintaining active memberships in IEEE, ACM, AIS, and INFORMS, demonstrating commitment to both academic rigor and professional community engagement.

• Semantic Web
• Ontology Matching
• Graph Databases
• +7 more

Giuseppe Pirro is an Associate Professor at the Department of Computer Science, Sapienza University of Rome. His research focuses on Semantic Web technologies, graph databases, and adversarial social network analysis. Specializes in RDF(S) inference, ontology alignment, and knowledge graph analytics Develops methods for community deception in networks and privacy-preserving data publishing Active in temporal query languages and web cartography Research trends include: Advancing graph embedding techniques for knowledge representation Exploring intersection of differential privacy and neural networks Designing novel algorithms for community detection and network deception Improving query languages for semantic web and temporal data Scientific awards: ERC PE6_7 grant ERC PE6_10 grant KET Big data & computing recognition