Find Academic Supervisors

Georg Groh is an Adjunct Professor at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology . His research focuses on modeling social context, social interaction mediated by IT systems, and ML-based natural language processing. He holds a doctorate (2005) and habilitation (2012) from TUM, with prior studies in physics and computer science. Key research areas include social signal processing, network analysis, and bias detection in AI systems. Notable awards include the 2019 Supervisory Award and 2016 Honorary Teaching Certificate. His work bridges computational methods with societal impacts, particularly in health informatics and ethical AI. Recent projects explore LLM hallucination detection, bias profiling, and cross-lingual text classification. Education: PhD in Computer Science (2005), TUM Habilitation in Computer Science (2012), TUM Studies in Physics (University of Kaiserslautern) and Computer Science (Universities of Hamburg, Kaiserslautern, TUM) Research Interests: Groh’s work spans social computing, NLP, and ethical AI . Current projects address bias in language models, hate speech detection, and data-driven health interventions. His methodologies emphasize contextual analysis of social interactions, leveraging ML and graph-based techniques. Awards: 2nd place Supervisory Award (2019) Best Paper Awards (2016, 2008) Advising & Grants: Advised on projects like Nutrilize (nutrition recommender system) and contributed to EU-funded initiatives on mHealth systems. Active in designing AI systems for dietary logging and stress management.

• Applied Computer Science
• Social Signal Processing
• Social Network Analysis
• +4 more

Michael E. Cotterell is a Senior Lecturer of Computer Science and Undergraduate Coordinator at the University of Georgia's School of Computing. He holds a B.S. (2011) and Ph.D. (2017) in Computer Science from UGA. His roles include directing the Computer Science Undergraduate Assistant (CSUA) and UGAHacks experiential learning programs. He chairs the Undergraduate Program & Curriculum Committee and serves on UGA’s University Council. Dr. Cotterell’s teaching career began in 2012 with Software Development courses, transitioning to full-time instructor in 2015. He has won multiple teaching awards, including the Teaching Excellence in Computer Science Award (2018, 2020) and was promoted to Senior Lecturer in 2021. He has also served as a UGA Online Learning Fellow, Writing Fellow, and Teaching Academy Fellow. His research interests span computational education, active learning methodologies, and technical fields like big data analytics, ontology-based semantics, and energy informatics. His work bridges pedagogical innovation with applied computational science. Dr. Cotterell’s publications focus on improving educational practices, predictive analytics, and interdisciplinary applications of computer science. His most recent work emphasizes student sentiment analysis in active learning environments and scalable energy systems modeling. Awards include the 2016 Outstanding Faculty Teaching Award and multiple fellowships recognizing his contributions to pedagogy and online education. He oversees undergraduate program development and coordinates experiential learning initiatives, emphasizing hands-on learning through hackathons and assistantship programs.

• Computer Science Education
• Active Learning
• Big Data Analytics
• +3 more

Ying Zhang, PhD, is an Assistant Professor in the Department of Pathology at the University of California, Irvine (UCI) School of Medicine. Her research focuses on digital libraries, usability evaluation, metadata frameworks, and information systems. She has contributed to studies on digital humanities integration, search algorithm optimization, and user interaction design in digital environments. Her work frequently addresses challenges in library systems, search functionality, and metadata standards for moving image collections. Key areas of interest include improving digital library interfaces, analyzing user behavior in web searches, and developing holistic evaluation models for digital infrastructure. Her research spans topics like neural network applications for clickthrough prediction and the impact of field weights on search performance. She has also explored cultural aspects of librarianship, such as technologies in Chinese librarianship and cross-cultural digital resource management. Dr. Zhang's publications reflect a focus on bridging technical systems with user-centric needs, emphasizing both theoretical models and practical applications in digital information management. She has consistently engaged with issues of usability, metadata efficacy, and the evolving role of libraries in the digital age.

• Digital Libraries
• Information Science
• Usability Evaluation
• +4 more

Dr. Yan Gong is a Lecturer in Computer Science at Bournemouth University, Faculty of Science and Technology, Department of Computing and Informatics. He holds a PhD in Computer Science from Loughborough University (2023) and a Master’s degree in Communications and Signal Processing with distinction from Newcastle University (2012). Before academia, he worked over six years as a lead AI engineer in industry. Research Interests: Dr. Gong specializes in cutting-edge areas of artificial intelligence, including Natural Language Processing (NLP), Cross-modal Learning, Generative AI, and AI Agents. His work bridges theoretical advances with real-world applications, particularly in multimodal information retrieval and neural search systems. He is passionate about solving practical problems through AI and actively collaborates with industry partners. Publication Trends: His recent publications (2021–2024) focus on improving cross-modal information retrieval using deep learning, especially Vision Transformers and semantic embedding techniques. Key themes include hard negative mining, relation-focused learning, and neural search engines for artwork and general domains, published in high-impact journals like Pattern Recognition and ACM Transactions on Knowledge Discovery from Data . Scientific Service: Reviewer, Pattern Recognition (Elsevier) Reviewer, ACM MM 2023 Conference Guest Editor, IEEE Journal of Biomedical and Health Informatics Reviewer, Neural Networks , Knowledge and Information Systems , AI Communications Teaching and Advising: Dr. Gong is the unit leader for COMP7076 (Industrial Skills and Professional Issues) in the MSc Human-Centred Artificial Intelligence program. He supervises postgraduate students and welcomes PhD applicants interested in NLP, Generative AI, and multimodal AI. While no specific students are listed, he emphasizes mentorship and real-world research translation. Labs and Research Groups: Though not explicitly named, Dr. Gong is affiliated with AI and computing research activities at Bournemouth University, contributing to the university's research in human-centred AI and intelligent systems. His collaborations with Dr. Georgios Cosma and others suggest active participation in a research team focused on multimodal learning and information retrieval.

• Natural Language Processing
• Cross-modal Learning
• Generative AI
• +7 more

Lect. dr. Cristian Văideanu is a Lecturer at the Department of Mathematics, Faculty of Mathematics of the Alexandru Ioan Cuza University of Iași (UAIC). His research focuses on set-valued analysis, formal concept analysis, and fuzzy set theory, with contributions to mathematical analysis and discrete mathematics. He has published works on measurable multifunctions, concept lattices, and web document analysis. Research Interests include: Formal Concept Analysis and Lattice Theory Set-Valued Analysis and Multifunctions Fuzzy Set Theory and Similarity Relations Mathematical Analysis and Measure Theory Recent Articles highlight his exploration of threshold conceptual structures in web documents, lower/upper concept lattices, and measurable multifunctions. These publications reflect his interdisciplinary approach spanning mathematics and computer science. Teaching involves courses in formal languages, automata theory, and mathematical analysis. His educational materials include problem collections for calculus and sequences/series of functions. Affiliations include the UAIC Mathematics Department , with ongoing contributions to academic conferences and collaborative research projects.

• Mathematical Analysis
• Set-Valued Analysis
• Fuzzy Set Theory
• +3 more

Elena Cabrio is a Full Professor at Université Côte d'Azur, with affiliations at Inria, CNRS, and I3S. She is a member of the Wimmics Research Team and a Chairholder at the 3IA, actively contributing to interdisciplinary research in artificial intelligence and natural language processing. Her research focuses on Natural Language Processing , Argumentation Mining , and Information Extraction . She aims to design debating technologies for advanced decision support systems, facilitating the exchange of information and opinions in critical domains such as healthcare and politics. Her work leverages machine learning and interdisciplinary approaches to model human argumentation in textual data. Elena Cabrio defended her HDR (Habilitation to Direct Research) in October 2020, marking a significant milestone in her academic career. As a 3IA Chairholder, she leads cutting-edge research initiatives and contributes to the development of AI technologies with societal impact. She is also involved in doctoral and postdoctoral training through seminars and collaborative research programs. She is affiliated with multiple research entities, including the Wimmics Research Team at Inria, which specializes in Web-based Information Systems and Knowledge Management. Her work bridges computer science, linguistics, and social sciences, aiming to create interpretable and human-centric AI systems.

• Natural Language Processing
• Argumentation Mining
• Information Extraction
• +5 more

Janna Omeliyanenko is a researcher at the Chair of Data Science (Informatics X) , University of Würzburg, Faculty of Mathematics and Computer Science. She has been actively contributing to the DMIR research group since 2019, focusing on integrating structured knowledge representations (knowledge graphs) with unstructured language models to enhance AI performance. Her work bridges theoretical advancements in NLP with practical applications in knowledge graph completion and media bias analysis. Her teaching responsibilities include courses such as NLP and Text Mining , Selected Chapters from Machine Learning , and XtAI Labs , reflecting her interdisciplinary expertise in explainable AI and deep learning. Based in Room 50.03.018 at the Institute Building Artificial Intelligence, Campus Hubland Nord, Würzburg, Germany, she can be reached at janna.omeliyanenko@uni-wuerzburg.de. Research interests center on knowledge graph integration with LLMs, continual learning for graph completion, and bias detection in multilingual contexts. Her publications at ISWC and AAAI conferences demonstrate technical rigor in combining semantic web technologies with modern machine learning approaches. Recent work explores adaptive pre-training strategies (CapsKG/PreAdapter) and cross-linguistic social media analysis.

• Knowledge Graph Integration
• Natural Language Processing
• Machine Learning
• +3 more

Albin Zehe is a Researcher at the Chair of Data Science (Informatik X) within the Institute of Computer Science at the University of Würzburg's Faculty of Mathematics and Computer Science. He holds an M.Sc. degree and has maintained an active research and teaching profile since at least 2016, with his office located in Room 03.021 of the CAIDAS Building on the Hubland Nord campus. Zehe's research primarily focuses on applying Natural Language Processing techniques to literary analysis, with a special emphasis on sentiment analysis in German literature. His work bridges computer science and humanities, developing computational methods to analyze novels and other literary works. He has expressed particular interest in Tolkien's writings, reflected in his 2022 publication analyzing Tolkien's Legendarium using NLP and Graph Neural Networks. His publication record shows a progression from literary analysis (2016-2018) to broader NLP applications including social media analysis (Twitch.tv in 2020) and time series forecasting (2020). Recent work has expanded to graph-based analysis of literary universes and scene segmentation in fiction. Zehe has served as PC member for major conferences including ACL (2021 & 2023), EMNLP (2021 & 2022), ECMLPKDD (2020 & 2021), ECAI (2020), and AAAI (2020-2022), demonstrating his active role in the academic community. He has taught Machine Learning for Natural Language Processing every summer semester from 2018 to 2023, along with the Natural Language Processing Practicum , and previously taught courses in algorithms, information retrieval, and Web 2.0 technologies. He has co-organized academic challenges including the ChAT challenge at ECML-PKDD 2020 and the Shared Task on Scene Segmentation at KONVENS 2021.

• Natural Language Processing
• Text Mining
• Sentiment Analysis
• +3 more

Philippe Cudré-Mauroux is a Full Professor at the University of Fribourg, Switzerland , where he leads the eXascale Infolab . He has held visiting researcher positions at MIT and Microsoft CISL , and serves on the Research Council of the Swiss National Science Foundation and the Scientific Advisory Board of the CHIST-ERA EU Research Programme . Research Interests: His work spans exascale information management , big data , AI , knowledge graphs , linked data , time series data repair , and emergent semantics . He focuses on building scalable, intelligent data systems that integrate storage, computation, and semantics. Publication Trends: His recent work emphasizes schema-aware knowledge graph completion , time series imputation and benchmarking , hardware-accelerated data systems , and large language models for data cleaning . His research bridges database systems, AI, and systems architecture, often targeting high-performance, real-world applications. Scientific Awards: ERC Consolidator Grant (2016) Google Faculty Research Award (2013) Verisign Internet Infrastructures Award (2012) Best Paper Awards at VLDB (2020), AAMAS (2019), and Swiss Data Science Conference (2020) EPFL Doctorate Award and Press Mention (2007) Best Mentor Award at ISWC (2010) Advising and Grants: He mentors a large group of researchers and students, many of whom are co-authors on his publications. He has secured significant funding, including a €2M ERC Grant and multiple Google and Amazon grants, supporting a vibrant research lab focused on next-generation data infrastructure. Labs and Teams: He leads the eXascale Infolab at the University of Fribourg, a dynamic research group actively publishing in top-tier venues and developing innovative tools for data management and AI integration.

• Exascale Information Management
• Big Data
• Artificial Intelligence
• +13 more

Antoine Zimmermann is a full professor at École des Mines de Saint-Étienne (France) and a researcher at Laboratoire d’Informatique, de Modélisation et d’Optimisation des Systèmes (LIMOS). He holds a doctorate in computer science from Grenoble University and has been a key figure in Semantic Web research since 2008. Education: Habilitation à diriger des recherches (2021) - Université Jean Monnet PhD in Computer Science (2008) - Université Joseph Fourier Master’s Degree (2004) - Université Joseph Fourier His research focuses on enabling interoperability through Semantic Web technologies, particularly in knowledge representation , linked data , and contextual information systems . He emphasizes heterogeneous data integration , ontology matching , and hypermedia agent communities , as evidenced by his leadership in projects like OpenSensingCity and MACMIA. Scientific accolades include multiple best paper and reviewer awards at prestigious venues like ESWC, MATES, and IoT. He actively contributes to standardization via W3C working groups and serves on editorial boards for journals including Transactions on Graph Data and Knowledge .

• Semantic Web
• Knowledge Representation
• Linked Data
• +3 more

Tim Finin is a Professor in the Computer Science and Electrical Engineering department at the University of Maryland, Baltimore County (UMBC), where he serves as Director of the UMBC Center for Artificial Intelligence and holds the Willard and Lillian Hackerman Chair in Engineering. With over 50 years of experience, his research focuses on knowledge graphs, natural language processing, machine learning, and applications to information systems security and social media. Education: Ph.D. in Computer Science from the University of Illinois (1980), M.S. in Computer Science (1977), and S.B. in Electrical Engineering (1971) from MIT Current Roles: Director, UMBC Center for AI; Hackerman Chair; Professor, UMBC Previous Roles: Adjunct Associate Professor at University of Pennsylvania; positions at Unisys, JHU HLT CoE, and MIT AI Lab Research Interests: Dr. Finin's work spans Knowledge graphs and semantic web technologies Natural language processing for cybersecurity Machine learning for data assimilation Privacy and security in distributed systems Social media analysis Quantum computing applications Recent Grant Trends: His funded research includes projects on knowledge graph optimization, AI cybersecurity, semantic manufacturing standards, and quantum machine learning. Grants from DoD, NSF, NIST, and industry partners like IBM and Google demonstrate his interdisciplinary impact. Scientific Honors: ACM Fellow (2018) AAAI Fellow (2013) IEEE Technical Achievement Award (2009) UMBC Presidential Research Professor (2012) Fellow, Foundation for Intelligent Physical Agents (1997) Academic Leadership: Dr. Finin has chaired UMBC's Computer Science department, served on the Computing Research Association board, and held editorial roles including Editor-in-Chief of the Journal of Web Semantics (2005-2016).

• Artificial Intelligence
• Knowledge Graphs
• Natural Language Processing
• +5 more

Wei Hu is a full Professor and Ph.D. supervisor in the Department of Computer Science and Technology at Nanjing University, China. He earned his Ph.D. and B.S. from Southeast University in 2009 and 2005, respectively, and joined Nanjing University faculty in 2009. He has held visiting positions at Stanford University (2014-2015), University of Texas at Arlington (2016-2017), and University of Toronto (2017) as a visiting scholar/professor. Research Interests focus on Knowledge Graphs : Representation learning, foundation models, and error detection Databases : Entity alignment, crowdsourcing, and blockchain integration Digital Medicine : Collaboration with Nanjing University's National Institute of Health Data Science Publication Trends show expertise in knowledge graph reasoning, federated learning, and biomedical applications. Recent works include in-context learning for graph reasoning and blockchain-based data fusion systems. Scientific Awards include Huawei 2025 Challenges Spark Award ASE 2024 Distinguished Paper Award CHIP 2021 Best Paper Award CCKS 2018 Best English Paper Award Nanjing University Study Abroad Program Awardee (2013) IBM China Excellent Student (2008) Advising involves leading the Knowledge Fusion Group at Nanjing University, mentoring 18 current and former Ph.D. and Master's students. Professional services include editorial roles at Transactions on Graph Data and Knowledge and Big Data Research , plus committee memberships in CCF, CIPSC, and JSCS.

• Artificial Intelligence
• Knowledge Graphs
• Semantic Web Technologies
• +5 more

Prof. Sören Auer is the Director of the German National Library of Science and Technology (TIB) and Professor of Data Science and Digital Libraries at Leibniz Universität Hannover's Faculty of Electrical Engineering and Computer Science . With academic positions at Dresden, Yekaterinburg, Leipzig, Pennsylvania, Bonn, and Fraunhofer Society, his career focuses on semantic technologies , knowledge engineering , and Artificial Intelligence Research Data Management Open Science Knowledge Graphs . He leads the Open Research Knowledge Graph (ORKG) initiative and co-founded DBpedia and eccenca.com . His research spans data science , AI-driven knowledge representation , and digital library systems . He has supervised numerous PhD theses and final-year theses while offering courses on Knowledge Engineering Semantic Web Technologies Data Integration Scientific Data Management . The technical focus includes semantic data interlinking , neuro-symbolic AI , and contextual metadata frameworks . As a recipient of prestigious awards including ERC Consolidator Grant SWSA Ten-Year Award ESWC 7-Year Best Paper Award OpenCourseware Innovation Award , Prof. Auer has led major projects like BigDataEurope and contributes to standards in W3C , NFDI , and EOSC . His recent publications demonstrate advancements in ontology alignment , LLM-driven schema discovery , and hybrid AI systems for scholarly knowledge organization.

• Semantic Technologies
• Knowledge Engineering
• Data Science
• +7 more

Prof. Dr. Marco C. Meier holds the Chair of Business Information Systems and Management Support at the Faculty of Business and Economics, University of Augsburg. His work bridges business, computer science, psychology, and engineering, focusing on digital transformation and human-centered management systems. Research Areas: Business Intelligence & Analytics, Persuasive Technology, Design Thinking, and Theme-Centered Interaction (Ruth Cohn). His team explores technologies like business analytics and information visualization, methods including design science research, and collaborative frameworks for meaningful decision-making. Recent Publications highlight gamification in information security education (HICCS 2023), educational game design (AMCIS 2025), and long-term contributions to ERP systems and SAP integration. Articles emphasize cross-disciplinary approaches to data overload and adaptive management support. Scientific Recognition: Nominated for Gesellschaft für Informatik Dissertation Prize (2000) Best Paper Award at International Conference Wirtschaftsinformatik (2001) Multiple "Best Teacher" and "Lecturer of the Year" awards (2009-2020) Academic Leadership: Former Managing Director of Bavarian Business Informatics Research Network (FORWIN), Deputy Director of Finance & Information Management Competence Center (FIM), and Head of Business Intelligence Department at Fraunhofer Project Group. Supervises PhD students and research assistants like Ramona Reinelt and Raphaela Stöckl. Teaching Philosophy: Advocates active learning through inverted classroom models and blended learning approaches. Requires prior research module completion for thesis supervision. Emphasizes methodological rigor and practical relevance in exams and research projects.

• Business Information Systems
• Management Support Systems
• Business Intelligence & Analytics
• +5 more

Frederico Fonseca is an Associate Professor in the College of Information Sciences and Technology at Pennsylvania State University. His research focuses on the conceptualization and implementation of information in computer systems, with a particular emphasis on ontologies related to the environment and geographic information systems (GIS). He is affiliated with interdisciplinary research themes in Health and the Environment and contributes to foundational work in information science. His research interests span ontology, geographic information systems, knowledge sharing, big data, information integration, hermeneutics, and the philosophical foundations of information. He explores how meaning is constructed, shared, and represented in digital environments, especially through computational ontologies and cyber-human systems. His work bridges technical, social, and philosophical dimensions of information science. The recent publications highlight a strong trend in the intersection of information theory, environmental modeling, and human cognition. His articles explore semantic structures in data, the epistemology of big data, and the socio-technical aspects of digital systems. Key domains include GIS, ontology engineering, digital divides, and ethical design, reflecting a deeply interdisciplinary approach grounded in both technical rigor and philosophical inquiry. Scientific Awards: No scientific awards mentioned in the provided text. Frederico Fonseca has been involved in significant research projects related to spatial data infrastructures, knowledge sharing, and semantic systems. While specific grants are not listed, his collaborative publications suggest involvement in federally or institutionally funded initiatives, particularly in geoinformatics and data science. He has advised or collaborated with multiple researchers and students, though no formal advisees are named in the text. He is associated with research groups focusing on cyber-human systems, geographic information science, and semantic technologies. His work contributes to broader teams exploring open data, information ethics, and environmental informatics, particularly in the context of global challenges like forest monitoring and digital equity.

• Ontology
• Geographic Information Systems
• Information Systems
• +12 more