Find Academic Supervisors

Michael MartinView profile

Professor

• Semantic Web Technologies
• Data Science
• Linked Data Management
• +4 more

Prof. Dr. Michael Martin serves as Professor for Data Management Systems at TU Chemnitz, where he heads the Emergent Semantics research group as part of the AKSW network. His academic work bridges theoretical research and practical applications of semantic technologies, with strong connections to both TU Chemnitz and the University of Leipzig where he teaches courses in E-Business, Software Engineering and Software Management. Michael Martin's research focuses on Engineering of Web Applications using Semantic Web Technologies, Data Science, and Management of Linked (Open) Data. His work explores how semantic technologies transform traditional web applications into data-driven systems, with particular emphasis on knowledge graph engineering, LLM-assisted semantic technologies, and industrial applications in sectors like steel and copper production. Recent work demonstrates growing interest in applying these technologies to crisis management and resilience research. His publication record shows a clear evolution from foundational semantic web technologies toward integrating large language models with knowledge graphs. Current research trends include developing benchmarks for LLM capabilities in knowledge graph engineering (LLM-KG-Bench), creating ontology-based digital representations for industrial processes (KupferDigital, StahlDigital), and building practical tools for geo-spatial data integration with semantic technologies. These works demonstrate increasing sophistication in applying semantic technologies to real-world industrial challenges. Michael Martin leads significant research projects including LEDS (Linked Enterprise Data Services funded by BmBF), SlideWiki (EU-funded), and previously contributed to major initiatives like LOD2, LATC, OntoWiki, and the Digital Agenda Scoreboard of the European Commission. His work demonstrates strong grant acquisition capabilities across both national and European funding programs, with clear translational impact from academic research to practical applications. As head of the Emergent Semantics research group within the AKSW network, Martin leads a team focused on practical applications of semantic technologies. The group develops tools like OntoWiki for semantic web application development and participates in creating knowledge graph platforms for industrial applications and crisis management, demonstrating strong industry-academia collaboration and real-world impact of semantic technologies.

Alberto José Bugarín DizView profile

Professor

• Machine Learning
• Natural Language Generation
• Soft Computing
• +5 more

Alberto José Bugarín Diz is a Full Professor of Artificial Intelligence at the University of Santiago de Compostela (USC), where he serves as coordinator of the Intelligent Systems Group (GSI), founded in 1990 and recognized as a competitive reference group by the Xunta de Galicia since 2006. He previously served as Deputy Director of the School of Engineering of the University of Santiago de Compostela (ETSE) and currently acts as the USC coordinator of the Degree in Artificial Intelligence taught across the three universities in Galicia. His research focuses on various areas of artificial intelligence and its applications, including natural language generation (Data-To-Text systems), soft computing, automatic learning, and approximate knowledge and reasoning representation. He has co-authored nearly 230 scientific papers and participated in over 63 R&D projects and innovation contracts, serving as principal researcher in 25 of them. His work spans healthcare applications, manufacturing processes, conversational agents, and linguistic descriptions of data, with particular emphasis on making AI systems more transparent and understandable to users. Prof. Bugarín Diz is actively involved in the AI community, currently serving as a Board Member of the Spanish Society for Artificial Intelligence (AEPIA), Secretary of the Spanish Society for Natural Language Processing (SEPLN), and as a member of the Organizing Committee of the 27th European Conference on Artificial Intelligence (ECAI), which took place in Santiago de Compostela in October 2024. He also participates in the TELSEC4TAI network about 'Trustworthy AI: technical, ethical, legal, cultural and socio-economic challenges.' His recent publications reveal a strong focus on explainable AI, trustworthy systems, and the application of fuzzy logic to complex reasoning problems, particularly in medical diagnosis, automotive manufacturing, and conversational systems. His work consistently bridges theoretical AI concepts with practical applications that address real-world challenges in healthcare, industry, and human-computer interaction. Board Member, Spanish Society for Artificial Intelligence (AEPIA) Secretary, Spanish Society for Natural Language Processing (SEPLN) Organizing Committee Member, 27th European Conference on Artificial Intelligence (ECAI 2024) Coordinator, Intelligent Systems Group (GSI) USC Coordinator, Degree in Artificial Intelligence (Galicia)

Manuel Lama PenínView profile

Professor

• Machine Learning
• Process Mining
• Predictive Monitoring
• +6 more

Manuel Lama Penín is a Full Professor at the Research Center on Intelligent Technologies (CiTIUS) with the University of Santiago de Compostela. His research bridges machine learning, process mining, and semantic modeling to address challenges in healthcare, industry, and e-learning. He has led 29 of 51 R&D projects and authored over 150 scientific works, including 52 journal articles. Research Areas: Process Mining & Conformance Checking Predictive Monitoring & Drift Detection Semantic Modeling & Service Computing Social Workflow Analysis & Data-to-Text AI in Healthcare & Educational Technologies Notable Projects: TRANSFIRESAUDE (2023, EU Health R&I Ecosystems) AQUATECHInn 4.0 (2023, Aquaculture Education) INCEPTION (2021, Concept Drift in Process Mining) Scientific Recognition: Recipient of the Transfer Technology award from the Galician Royal Academy of Sciences for co-founding InVerbis Analytics, a spin-off commercializing process mining research. Article Trends: His recent publications focus on explainable AI integration in EU regulations, drift detection methodologies, and natural language generation for process documentation. Collaborative efforts emphasize cloud-based solutions, healthcare process formalization, and predictive modeling across sectors.

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.

• Databases
• Information Systems
• Data Quality
• +5 more

Isabelle Comyn-Wattiau is a full professor at ESSEC Business School and affiliated with the Conservatoire National des Arts et Métiers (CNAM) , contributing to the CEDRIC laboratory. With over two decades of academic activity, her work bridges computer science, information systems, and digital humanities through conceptual modeling, data quality, and reverse engineering. ESSEC Business School (since 2013) CNAM - CEDRIC Laboratory (since 2001) Her research interests focus on database design, quality assessment, and modeling techniques, particularly for historical data and business intelligence applications. Key themes include: Conceptual modeling of prosopographic databases Uncertainty detection in historical data Quality metrics for web applications Security requirements engineering with ontologies Mapping multidimensional schemas to graph models Evaluation frameworks for design science research Her article trends show consistent contributions to database evolution (2001-2023), with recent work emphasizing design science research (2019-2023) and historical data quality (2019-2021). Collaborative patterns reveal long-term partnerships with Jacques Akoka and Nathalie Prat across multiple domains.

Erhard RahmView profile

Professor

• Data Integration
• Knowledge Graphs
• Privacy-Preserving Data Analysis
• +4 more

Prof. Dr. Erhard Rahm is a leading computer science academic at the University of Leipzig , affiliated with the Institute of Computer Science under the Faculty of Mathematics and Computer Science. With over 35 years of research contributions, he specializes in scalable data integration, privacy-preserving record linkage (PPRL), and graph analytics for knowledge graphs and digital twins. Key research areas: Data Integration for Knowledge Graphs, Privacy-Preserving Data Analysis, Dynamic Graph Management Recent work focuses on hybrid digital twins, medical image analysis, and secure health data processing 2016-2021: Speaker of GI's Database Systems and Information Systems division Scientific Contributions: His 15 most recent publications (2023-2025) span: Privacy-preserving machine learning (federated learning, PPRL with LLMs) Graph-based data transformations (Temporal Knowledge Graphs, Property Graph Streams) Automated schema matching and entity resolution systems Biomedical data applications (cell confluence assessment, stress detection) Selected Honors: VLDB Ten-Year Best Paper Award (2011) ICDE Influential Paper Award (2013) MSR Outstanding Collaborator Award (2016) Leadership Roles: Currently Vice President of the Gesellschaft für Informatik (since 2022), former DFG reviewer (2016-2024), and active in NFDI infrastructure (2019-2022). He leads research at ScaDS.AI, focusing on big data integration and scalable graph analytics.

Ben WellnerView profile

Lecturer

• Natural Language Processing
• Discourse Parsing
• Machine Learning
• +5 more

Ben Wellner is a Lecturer in the Department of Computer Science at Brandeis University and a Lead Scientist at The MITRE Corporation's Information Technology Center. His research spans Natural Language Processing, Discourse Analysis, Temporal Reasoning, and Biomedical Informatics. Current teaching: Statistical Natural Language Processing Key collaborations: Andrew McCallum, James Pustejovsky, Lynette Hirschman, and others Methodological focus: Sequence models, weakly supervised learning, and graph partitioning techniques Research trends from his publications include: Developing machine learning approaches for discourse structure analysis Creating adaptive systems for information extraction and coreference resolution Applying NLP techniques to biomedical data normalization Building temporally-aware models for text analysis

Yada ZhuView profile

Researcher

• Machine Learning
• Time Series Analysis
• Graph Learning
• +5 more

Dr. Yada Zhu is a Researcher at IBM T. J. Watson Research Center , affiliated with the Future of Computing – Finance Research team. She leads initiatives in foundational AI/ML capabilities for financial decision-making and risk management. Research Focus: High-dimensional time series analysis, heterogeneous data modeling, graph learning, and statistical applications in finance, e-commerce, and smart energy. Grants: Principal Investigator for MIT-IBM AI Lab projects funded by Refinitiv and a major financial institution. Leadership: Technical lead in analytics projects contributing to commercialized IBM products. Scientific Achievements: IBM Research Division Outstanding Technology Achievement Award High Valuable Innovation Award Her publications span AI/ML, statistical modeling, and financial applications, with recent work on graph transformers, time-series unification, and zero-shot tool optimization for LLM agents. She serves on editorial boards for statistics journals and as Senior Program Committee member for AAAI.

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).

• Political Opinions
• Machine Learning
• Automated Text Analysis
• +7 more

Nick Beauchamp is an Associate Professor in the Department of Political Science at Northeastern University. His research focuses on how political opinions form and evolve through discussion and deliberation, leveraging techniques from machine learning, automated text analysis, and social network analysis. His affiliations include The Roux Institute in Portland, ME, and Northeastern’s campuses in Boston, MA, and London, UK. University: Northeastern University Department: Political Science Rank: Associate Professor Beauchamp’s research integrates computational methods with political science to analyze political discourse, ideology prediction, and social dynamics. His recent work explores large language model personalization, stance detection, and temporal reasoning in political contexts. Selected trends from his publications include advancements in Natural Language Processing for political analysis, applications of Machine Learning to social networks, and interdisciplinary collaborations bridging Data Science and Public Health .

Babak EsfandiariView profile

Professor

• Artificial Intelligence
• Agent-Based Systems
• Network Computing
• +6 more

Babak Esfandiari is a Professor at the Faculty of Engineering and Design , Department of Systems and Computer Engineering , Carleton University. His research focuses on Agent-based systems , Network computing , Artificial intelligence applications , Object-oriented design , and Network management . He leads the NM-AI Research Lab and contributes to Sustainable Energy initiatives. Ph.D. from Université Montpellier II (France) Email: babak@sce.carleton.ca Office: MacKenzie Building, Room 4478 His research bridges Artificial Intelligence , Network Engineering , and Software Engineering , with applications in Mobile Robotics , Digital Twin Emulation , and Network Optimization . He has developed frameworks for BDI Agents , Trust Management , and Network Simulation , as evidenced by his publications. He teaches advanced courses such as SYSC 5103 Software Agents , SYSC 3110 Software Development Lab , and SYSC 4806 Software Engineering Lab . His work has influenced interdisciplinary domains including Energy Policy and Human-Agent Interaction .

• 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)

Aurélie LEBORGNEView profile

Lecturer

• Spatio-temporal Data Analysis
• Graph Modeling
• Frequent Pattern Mining
• +3 more

Aurélie Leborgne is a Lecturer in Computer Science at the University of Strasbourg's ICube laboratory, affiliated with the Department of Computer Science at IUT. She completed her PhD in Computer Science at INSA Lyon (2016), a Master's at the University of Caen (2012), and a University Diploma in Higher Education Pedagogy at Strasbourg (2022). Her research spans two domains: 1) Computer Science focusing on spatio-temporal graph modeling, pattern mining, and applications in medical imaging/environmental monitoring; and 2) Pedagogy investigating emotional awareness, collaborative learning, and teaching methodologies. She leads significant research projects including the ANR JCJC-funded MoS-T (2021-2025) on spatio-temporal pattern mining and METEC-Graphe (2019-2022) on spatio-temporal data modeling. She currently advises PhD candidates Assaad O. Zeghina and Romain Perrin, and has supervised three Master's students. Her teaching portfolio includes programming (C#, Java, C++), web technologies, AI, databases, and professional development courses at IUT. Research responsibilities include leading ANR projects and serving on laboratory councils. Teaching leadership includes implementing reflective practices across the department (2021-2023) and coordinating internships (2017-2018).