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Pei-Yu HouView profile

Assistant Professor

• Operations Management
• Statistical Data Analysis
• Natural Language Processing
• +3 more

Pei-Yu Hou is an Assistant Professor in the Department of Business Administration at National Chengchi University's College of Commerce, specializing in Operations and Supply Chain Management. Dr. Hou joined the university in August 2024 and maintains an active research agenda that bridges technical computing with business applications. Dr. Hou's research expertise spans Operations Management, Statistical Data Analysis, Natural Language Processing, and Database Management. Their work demonstrates an interdisciplinary approach combining computer science techniques with business administration principles, particularly in supply chain systems. The research trajectory shows progression from technical engineering foundations to practical business applications. Dr. Hou has published in top-tier venues including the Proceedings of the 2022 International Conference on Management of Data (SIGMOD '22), with research focusing on knowledge-graph embeddings and pathway analysis. Their current scholarly work shows strong connections between artificial intelligence methodologies and operational business challenges, particularly in supply chain management. Operations Management Statistical Data Analysis Natural Language Processing Database Management Supply Chain Management AI Applications in Business Dr. Hou is currently Principal Investigator for significant research projects funded by the National Science and Technology Council focused on utilizing AI models to construct and explore supply chain networks, indicating a growing research program with practical business implications.

Artur d'Avila GarcezView profile

Professor

• Neural-Symbolic Computation
• Neural Networks
• Applied Logic
• +10 more

Professor Artur d'Avila Garcez is a leading academic in Neural-Symbolic Computation at City St George's, University of London, where he serves as Professor of Computer Science and Director of the Research Centre for Machine Learning. He earned his PhD in Computing from Imperial College London (2000) and holds prestigious fellowships from the British Computer Society (FBCS) and Higher Education Academy (FHEA). With over 150 publications across journals like Artificial Intelligence and Neural Computation , and conferences including AAAI and NeurIPS, he pioneers hybrid AI systems merging machine learning with symbolic reasoning. Education: PhD (2000) and MEng (1993) in Computing Awards: Nuffield Foundation Grant (2002-2004), Daiwa Foundation Grant (2006) His research focuses on Neural-Symbolic Computing , integrating logic-based reasoning with deep learning for explainable AI. Key contributions include Logic Tensor Networks and Knowledge Extraction from CNNs, impacting fields like medical diagnostics and financial technology. He has mentored 22 PhD students and co-authored two foundational books in the field. He serves as Editor-in-Chief for Neurosymbolic AI and on editorial boards of Machine Learning and Journal of Logic and Computation . His work addresses trust in AI , accountability , and human-like computing through EU and industry-funded projects like Smart Big Data Platform and Safety Validation of Autonomous Vehicles.

Soumen ChakrabartiView profile

Professor

• Graph Neural Networks
• Knowledge Graphs
• Natural Language Processing
• +3 more

Soumen Chakrabarti is a Professor in the Department of Computer Science and Engineering at Indian Institute of Technology Bombay. His work bridges graph theory, natural language processing, and knowledge representation, focusing on scalable solutions for complex information retrieval problems. He has held visiting positions at Carnegie-Mellon University and Google. Education: B.Tech from IIT Kharagpur, PhD from UC Berkeley Key research areas: Graph Neural Networks, Knowledge Graphs, Code-Switched Text Analysis, Temporal Reasoning His recent publications emphasize neural graph matching , code-switched NLP , and multi-modal QA systems , often integrating symbolic reasoning with deep learning. Awards include the WWW 1999 Best Paper Award and ECML/PKDD 2008 Best Student Paper . He leads projects like CRUSH4SQL for text-to-SQL parsing and CSAW for annotated web search. His lab collaborates with IBM and Google on graph representation learning and temporal knowledge graph completion.

Christoph WehnerView profile

Researcher

• Interactive Machine Learning
• Explainable AI
• Knowledge Graphs
• +5 more

Christoph Wehner is a doctoral candidate and Research Assistant at the Faculty of Information Systems and Applied Computer Sciences, Otto-Friedrich-Universität Bamberg. He works in the Cognitive Systems Group on interactive and interpretable machine learning methods applied to knowledge graphs. PhD research focuses on integrating human knowledge and preferences into AI systems Active in industrial AI applications for manufacturing and automotive sectors Specializes in reinforcement learning with rule-based expert feedback His work combines knowledge graphs with explainable AI techniques to solve real-world problems in root cause analysis and failure mode detection. Current research trends include: Developing hybrid human-AI systems for link prediction Creating interpretable structures from knowledge graph embeddings Applying causal discovery algorithms to industrial processes Thesis advising focuses on practical implementations of knowledge graph-based AI systems in production environments.

Leticia Arco GarciaView profile

Researcher

• Natural Language Processing
• Complex Networks
• Deep Learning
• +5 more

Leticia Arco Garcia is a postdoctoral researcher at Vrije Universiteit Brussel working in the Department of Informatics and Applied Informatics with affiliations at Federated Labs AI and Robotics. Her research spans computational linguistics, complex network analysis, and more recently, interdisciplinary applications in archaeobotany. Her research interests focus on Natural Language Processing , Complex Networks , and Deep Learning methodologies. Early work centered on community detection in complex networks and customer behavior analysis, while recent publications demonstrate expansion into archaeobotanical applications using morphometric analysis of phytoliths. She employs advanced computational techniques including LSTM networks, case-based reasoning, and multi-objective optimization across diverse domains from smart power grids to electronic money systems. Analysis of her 15 most recent publications reveals an interdisciplinary trajectory moving from pure computer science applications toward biological/archaeological domains. The earliest works focus on network construction and community detection, while more recent papers apply similar analytical frameworks to botanical specimens, indicating methodological transfer between fields. Her work consistently applies computational rigor to complex data structures across diverse application areas. Her scientific contributions include: Development of novel network construction methodologies for large purchasing collections Advancements in overlapping community detection using rough clustering Application of deep learning to sentiment analysis and customer behavior prediction Recent innovative work applying morphometric analysis to archaeobotanical specimens Dr. Garcia actively presents her research at international conferences including INTERSPEECH and specialized meetings on phytolith research. Her collaborative network spans multiple institutions, with co-authorship on interdisciplinary projects connecting computer science with archaeology and plant science. While specific grant information isn't detailed in the available text, her consistent publication output across multiple domains suggests successful research funding.

Frédéric FlouvatView profile

Professor

• Data Science
• Artificial Intelligence
• Pattern Mining
• +7 more

Frédéric Flouvat is a Full Professor in computer science and data scientist at Aix-Marseille Institute of Technology , conducting research at the Computer Science and Systems Laboratory (LIS - UMR 7020) . As co-leader of the Data Centric AI (DCAI) team in the Data Science department, he focuses on data-centric AI, pattern mining, and spatio-temporal data analysis. His interdisciplinary work spans environmental monitoring, urban dynamics, and educational data mining. His research emphasizes Representation learning in Agent-Based Models Innovative approaches to data-centric AI Expert model integration for pattern discovery Spatio-temporal pattern extraction from complex data Applications to soil erosion, urban growth, and educational outcomes Scientific recognition includes INFORSID 2005 Young Researcher Best Paper Prize Best paper award at EGC'13 He has coordinated pedagogical projects like Development of the FabLab spirit and supervised numerous students across institutions including University of New Caledonia, Grenoble, Paris, Lyon, and Brest. Current research projects include SITI (ANR-funded urban dynamics study) and Descol IA (Carnot Star Institute health benefits research).

• Machine Learning
• Deep Learning
• Recommender Systems
• +7 more

Dr. Ruihai Dong is an Assistant Professor at the School of Computer Science, University College Dublin, affiliated with the Insight Centre for Data Analytics. His research bridges Machine Learning, Deep Learning, and Recommender Systems with significant applications in finance and medical imaging. Develops AI-driven solutions for financial markets and healthcare Collaborates with industry partners like Eagle Alpha and Samsung Funded by Enterprise Ireland for commercialization studies Research focuses on: Recommender systems (news, finance, conversational AI) Graph neural networks for financial risk modeling Medical imaging under limited labeling constraints Algorithmic transparency and ethical AI applications 3D CNNs & transformers for seismic analysis Large language models for multilingual evaluation Recent publications emphasize: Hybrid architectures for multimodal data Explainable AI techniques in financial and news domains Geometric deep learning for microstructure and seismic data Curriculum strategies in active learning Media bias mitigation frameworks Latent space modeling for asset embeddings Scientific contributions recognized through: Outstanding Research Award 2018 by UCD School of Computer Science Industry collaborations include work with: Eagle Alpha (financial analytics) SkillPages (data-driven platforms) Samsung (technology innovation)

• Computational Philology
• Digital Humanities
• Semantic Change
• +8 more

Thora Hagen is a researcher affiliated with the University of Würzburg , specifically within the Faculty of Humanities and the Institute of German Philology . She works within the Chair of Computational Philology and Modern German Literary History, contributing to the intersection of digital humanities and traditional philology. Her contact details include the email thora.hagen@uni-wuerzburg.de . Research Interests Thora's work focuses on computational philology , digital humanities , and historical text analysis . She explores semantic change , knowledge graph construction , and text similarity metrics using advanced natural language processing techniques. Her projects often involve historical German encyclopedias , literary comparisons , and gender representation studies . Scientific Awards No specific awards or fellowships are mentioned in the provided texts. Advising and Grants There is no explicit mention of advising roles or grant funding in the available texts. However, her involvement in collaborative research projects and computational literary studies implies potential participation in funded academic initiatives. Labs and Teams She is part of the Chair of Computational Philology and Modern German Literary History, collaborating with Prof. Dr. Fotis Jannidis and other team members. The group is based at the Hubland campus in Würzburg, focusing on integrating computational methods with philological research.

Steven SkienaView profile

Teaching Professor

• Data science
• Natural language processing
• Algorithms
• +4 more

Steven Skiena is a Teaching Professor in the Department of Computer Science at Stony Brook University , where he serves as Director of the Institute for AI-Driven Discovery and Innovation. He is also a co-founder and Chief Scientist at General Sentiment, a media measurement company. PhD in Computer Science from the University of Illinois in 1988. Skiena’s research focuses on the design of graph, string, and geometric algorithms with applications in biology, large-scale text analytics, and sentiment analysis. His work bridges machine learning and AI with diverse domains including social media, linguistics, and healthcare. His most recent publications highlight advancements in natural language processing , graph algorithms , vaccine development , and computational social science . Topics span identity analysis, network embeddings, and audio-based aging research. Scientific Awards : Fulbright Scholar, ONR Young Investigator Award, Chancellor's Award for Teaching, IEEE Computer Science and Engineering Teaching Award. Skiena advises graduate research projects and teaches courses such as CSE 101 , CSE 373 , and CSE 691 . He leads the Data Science Laboratory , which develops tools for text and network analysis.

Hossain ShahriarView profile

Professor

• mobile security
• web security
• mhealth security
• +7 more

Dr. Hossain Shahriar serves as Professor and Associate Director for the Center for Cybersecurity at the University of West Florida. His academic career bridges cybersecurity research with practical healthcare applications, focusing on critical protection of sensitive medical information systems. His research spans several vital cybersecurity domains: Mobile and web security mHealth applications security EHR systems and healthcare security HIPAA compliance checking mechanisms Malware analysis and vulnerability detection Quantum computing applications in security Blockchain security frameworks Dr. Shahriar's work demonstrates strong interdisciplinary focus, particularly at the intersection of healthcare and cybersecurity. His research portfolio shows increasing emphasis on quantum security applications, AI-powered defense mechanisms, and healthcare information protection systems. Recent publications reveal expertise in large language models for security applications, blockchain vulnerabilities analysis, and HIPAA compliance systems for mHealth applications. His scholarly output indicates strategic focus on both theoretical advancements and practical implementations addressing evolving cybersecurity threats across multiple domains. The research shows particular strength in developing automated systems for regulatory compliance checking and vulnerability detection. Dr. Shahriar has secured significant research funding from major federal agencies including National Science Foundation, National Security Agency, Department of Defense, and National Institutes of Health, demonstrating the practical relevance and impact of his work. His educational contributions include developing hands-on learning modules for cybersecurity education with emphasis on authentic learning experiences for students. He has held leadership roles as Program Chair (IEEE ICDH), Publication Chair (ACM SAC), and Proceedings Chair (IEEE COMPSAC), indicating his standing within the broader cybersecurity research community. His teaching portfolio includes Ethical Hacking, AI in Cybersecurity, Health Information Security and Privacy, and other critical cybersecurity courses developed with open-source materials.

Mizuho IwaiharaView profile

Professor

• Database Systems
• Web Information Systems
• Text Mining
• +3 more

Professor Mizuho Iwaihara is affiliated with Waseda University's Faculty of Science and Engineering and Graduate School of Information, Production, and Systems. Her research focuses on database systems, web information retrieval, text mining, security/privacy, and social media analysis. She has led significant projects on Wikipedia edit history analysis, knowledge graph construction, and privacy-preserving frameworks. Key research areas: Database Query Processing, Web Information Systems, Text Mining, Knowledge Management, Social Media Her recent publications address semantic analysis of collaborative content, topic evolution tracking, and privacy behavior modeling. Over 85 papers with 349 citations reflect her impact in database and social media research. Scientific achievements include: Best Demo Award (2014) for WikiReviz Best Paper Award (2008) at IFIP e-Business Conference EC-Web2006 recognition Grants from Japan Society for the Promotion of Science (JSPS) span multiple projects on knowledge graph development, social content analysis, and privacy-preserving systems. She supervises numerous graduate students and leads the Data Engineering Laboratory at Waseda University.

• Artificial Intelligence
• Machine Learning
• Neural Networks
• +6 more

E. Alonso is a Professor in the Department of Computer Science at City, University of London, with extensive contributions to artificial intelligence, machine learning, and cognitive science. His work spans theoretical foundations of AI, practical applications in power systems, automotive diagnostics, and ethical considerations in generative AI. Faculty member at City, University of London since at least 2010 Active researcher with publications extending through 2025 Collaborates extensively with researchers in AI, power systems, and cognitive science Regular presenter at major AI conferences including IJCAI, AAAI, and IEEE conferences Professor Alonso's research focuses on the intersection of theoretical AI and practical applications. His work in neural networks spans reinforcement learning, time series forecasting, and multi-agent systems. He has made significant contributions to understanding the mathematical foundations of AI systems, particularly in agent representations and learning mechanisms. His recent work increasingly addresses the ethical and legal implications of AI, particularly concerning copyright issues in generative AI training and moral value alignment in democratic AI systems. Analysis of Professor Alonso's recent publications (2023-2025) reveals a strong trend toward practical AI applications with theoretical grounding. His work spans multiple domains including power systems optimization using graph neural networks, automotive diagnostics with specialized neural architectures, and creative AI approaches to generalization. A notable pattern is his ability to bridge theoretical AI concepts with real-world engineering problems, particularly in energy systems and automotive applications. His research increasingly incorporates ethical considerations, reflecting the growing importance of responsible AI development. Professor Alonso has supervised numerous students who have gone on to publish with him in top venues. His collaborative approach is evident in the diverse range of co-authors across different disciplines. While specific grant information isn't detailed in the provided materials, his sustained publication record across multiple domains suggests successful funding acquisition for his research programs. His work appears connected to the Centre for Computational and Animal Learning Research, suggesting involvement in interdisciplinary teams exploring the connections between biological learning systems and artificial intelligence. Recent collaborations with researchers in medical imaging and radiotherapy implementation further demonstrate the breadth of his research impact across different application domains.

• Natural Language Processing
• Computational Linguistics
• Machine Learning
• +3 more

Frederic Bechet is a researcher affiliated with Aix-Marseille Université, CNRS, and the LIF UMR 7279 laboratory. His work spans Natural Language Processing, Computational Linguistics, and Machine Learning, focusing on task structure analysis, factual knowledge robustness, and semantic-driven evaluation methodologies. Research Interests: Semantic Parsing, Question Answering, Text Summarization, and Multitask Learning. Recent Trends: Empirical studies on task inclusion via statistical deficiency, POS-driven specialization in Mixture-of-Experts models, distractor-based factual evaluation, and temporal knowledge decay in LLMs. His work on WikiFactDiff introduces a realistic framework for atomic fact updates in causal language models. Collaborations: Co-authored with experts in semantic annotation (Géraldine Damnati), QA systems (Alexis Nasr), and adversarial learning (Gabriel Marzinotto). Labs/Teams: Involved in the DECODA corpus for call-center analysis and the MEDIA corpus for dialogue understanding.

• Graph Neural Networks
• Geometric Deep Learning
• Representation Learning for Graphs
• +5 more

Rex Ying is an Assistant Professor of Computer Science at Yale University, where he leads the Graph and Geometric Learning Lab. His research focuses on developing expressive, scalable, and explainable algorithms for graph-structured data through graph neural networks and geometric learning. His educational background includes: Ph.D. in Computer Science from Stanford University B.S. in Computer Science from Duke University Ying's research spans graph neural networks, geometric deep learning, and non-Euclidean foundation models. His work addresses the challenge of representing real-world data expressed as graphs, with applications in recommender systems, anomaly detection, social network analysis, protein networks, drug discovery, and physical simulations. He develops techniques that combine relational reasoning, multimodal learning, and foundation models to create efficient and scalable approaches for complex real-world data beyond just text and images. Analysis of his publication record shows a strong focus on advancing graph representation learning, with particular emphasis on hyperbolic geometry for hierarchical data, explainability of graph neural networks, and applications across diverse domains including biology, physics, and social sciences. His work demonstrates a consistent trajectory from theoretical foundations to practical implementations. His scientific achievements include: Baidu Scholarship 2019 Area Chair for LoG 2022 Conference Co-Lead of PyTorch Geometric Library for GNNs Lead organizer of SimDL Workshop at ICLR 2021 Blue Sky Best Paper Award at ACM KDD 2025 for RephQA Ying actively mentors students and researchers, seeking Ph.D. candidates passionate about pushing frontiers in GNNs and geometric deep learning. He has received funding including an NSF core program award on building foundation models for scientific discovery. His lab collaborates across disciplines, working on applications in biology, medicine, chemistry, physics, neuroscience, social networks, and supply chain. The Graph and Geometric Learning Lab focuses on three main research thrusts: Geometric and Graph Learning, Multimodal Foundation Models, and Trustworthy AI, with applications spanning multiple scientific domains.

• Artificial Intelligence
• Case-Based Reasoning
• Experience-Based Learning Systems
• +12 more

Professor Ralph Bergmann is a distinguished academic at the University of Trier, where he holds the position of Professor of Business Information Systems since 2004. He also serves as the research group leader for Experience-Based Learning Systems (EBLS) at the German Research Center for Artificial Intelligence (DFKI) Trier branch since 2020. His academic career spans multiple institutions with appointments at the University of Hildesheim (2001-2004) and the University of Kaiserslautern (1997-2001). Dr. Bergmann's research focuses on the integration of data-oriented AI methods with semantic technologies to create hybrid AI systems. His work centers on experience-based learning systems that combine machine learning, case-based reasoning, ontologies, and knowledge graphs for modeling explicit knowledge. This research enables the redesign, adaptation, and flexible execution of processes with applications across diverse domains including Industry 4.0, construction, crisis management, service, medicine, cooking, and political argumentation. His recent publications demonstrate a strong trend toward integrating traditional case-based reasoning with modern AI techniques like large language models and knowledge graphs. The research shows increasing application in healthcare (particularly oncology and emergency response), IoT-enabled process monitoring, and argumentation analysis. His work bridges theoretical AI foundations with practical implementations across multiple industries. Professor Bergmann has led approximately 35 research projects funded by prestigious organizations including the European Union, German Research Foundation (DFG), Federal Ministry of Education and Research (BMBF), and the State of Rhineland-Palatinate. With an h-index of 40, he has authored more than 200 scientific publications including four books and 13 conference proceedings. He actively contributes to the academic community through service on editorial boards including Engineering Applications of AI and the International Journal of Intelligent Information Technologies. His leadership extends to program committees for major conferences in artificial intelligence and case-based reasoning. Professor Bergmann heads the Artificial Intelligence and Intelligent Information Systems research group at the University of Trier and leads the Experience-Based Learning Systems research group at DFKI. His teams work on multiple projects including AI-AIM (AI-based anonymization in medicine), KIAFlex (interactive AI assistance for predictive and flexible control in discharge and transition management), DZW (Digital Twin Water Management), myRPA (experience-based Robotic Process Automation), and SPELL (Semantic Platform for Intelligent Decision and Operational Support in Control Centers).