Alessandro Fogli is a PhD Student at Imperial College London in the Department of Computing, affiliated with the Large-Scale Data & Systems (LSDS) Group . His research focuses on systems support for data analytics in cloud environments, including distributed systems, resource management, and query processing. Education PhD in Computer Science, 2019–Present, Imperial College London MSc in Computer Science, 2015–2017, Roma Tre University BSc in Computer Science, 2012–2015, Roma Tre University His research spans Distributed Systems , Databases , Data Analytics , and Modern Hardware . Recent work examines chiplet-based processor architectures and runtime mapping systems, with applications in performance optimization and hardware-aware query execution. Scientific Contributions Co-developed CHARM (2025), a runtime mapping system for chiplet heterogeneity Published in VLDB (2024) on OLAP processing for chiplet-based CPUs Contributed to HeatWave at Oracle Labs, improving query offloading to in-memory accelerators
Amine Mhedhbi is an Assistant Professor at Polytechnique Montréal in the Department of Computer Engineering and Software Engineering. He is affiliated with the Institute for Data Valorization (IVADO) and the Software Engineering for Machine Learning Applications (SEMLA) group. His research focuses on data management systems, particularly graph-structured databases, multimodal data engineering, and AI-driven query optimization. Ph.D. in Computer Science from University of Waterloo Former technical advisor to enterprise companies Prior applied research leadership at Distyl AI and internships at Microsoft Research His recent work explores integrating large language models (LLMs) into database systems, optimizing SQL generation, and advancing graph database architectures. Key projects include GraphflowDB and FlockMTL , addressing scalability and declarative semantic applications. Scientific awards include: NSERC Discovery Grant with Discovery Launch Supplement (2025) Cheriton School Distinguished Dissertation Award (2024) Microsoft Research Ph.D. Fellowship (2020) VLDB Best Paper Award (2018) He supervises graduate students in database systems and machine learning applications and serves on program committees for top-tier conferences like VLDB and SIGMOD.
Wayne de Fremery serves as Professor of Information Science and Entrepreneurship and Director of the Francoise O. Lepage Center of Global Innovation at Dominican University of California's Barowsky School of Business. He concurrently directs the Korea Text Initiative at the Cambridge Institute for the Study of Korea and owns Tamal Vista Insights LLC, an AI accessibility software firm. Previously, he spent twenty years as Associate Professor of Korean Studies at South Korea's Sogang University and represents Korea at ISO as Convener of WG 9 on document semantics. His Harvard PhD, Seoul National University MA, and Whitman College BA underpin interdisciplinary research bridging literary studies, bibliography, design, information science, and artificial intelligence. Current work focuses on cultural continuity, sustainability, and innovation's role in preserving heritage, exemplified by his MIT Press-contracted book Cats, Carpenters, and Accountants: Bibliographical Foundations of Information Science . Award highlights include dual National Library of Korea grants for AI-driven Korean text processing (2020-2021), South Korea's Ministry of Culture Citation (2020), Seagate's Data for Good Award (2019), and literary honors including the Benjamin Franklin Award (2005). National Library of Korea Research Grant (2021) for historical Korean OCR South Korean Ministry Citation (2020) for National Library contributions Seagate Data for Good Award (2019) for 'Sijo Reborn as Data' Microsoft Cloud for Good Grant (2016-2019) for Project Mo문oN Benjamin Franklin Award (2005) for literary criticism He leads the Korea Text Initiative and Project Mo문oN with funding from National Library of Korea, Microsoft, and Seagate, advising students in information science entrepreneurship while advancing ISO standards for semantic metadata.
Shui Yu is a Professor of the School of Computer Science in the Faculty of Engineering and Information Technology at the University of Technology Sydney (UTS), where he also serves as the Deputy Chair of the UTS Research Committee. His academic career spans over 20 years in Australia and 7 years in China, with additional teaching experience in Hong Kong and Indonesia. He has developed more than 10 units in cybersecurity, computer science, data analytics, and computer games, serving as the Course Director for Computer Science undergraduate programs. Professor Yu's research interests center on cybersecurity, privacy, networking aspects of Big Data, and applied mathematics for computer science. He pioneered the field of 'networking for big data' in 2013 and edited the seminal book 'Networking for Big Data' published in 2015. His work has practical applications in industry, including Amazon Cloud's auto-scale strategy against distributed denial-of-service attacks. Current research focuses include privacy and security concerns associated with big data, security issues in smart grids, anonymous transactions on Blockchain, and anonymous communication for web browsing privacy. Analysis of his recent publications reveals a strong research trajectory spanning cybersecurity, privacy-preserving technologies, networking for big data, and applied mathematics. His work shows increasing focus on quantum-resistant cryptography, federated learning security, and adversarial robustness in AI systems. The interdisciplinary nature of his research bridges theoretical foundations with practical applications in IoT, blockchain, and cloud environments. Fellow of IEEE (2023) Distinguished Lecturer of IEEE Communications Society (2018-2021) Distinguished Visitor of IEEE Computer Society (2022-2024) Professor Yu has secured numerous research grants from the Australian Research Council, including current projects on privacy and fairness in high intelligence models (DP240100955), improved security and privacy for online platforms (LP220200808), and secure blockchain for financial applications (LP220100453). He has served on editorial boards of multiple IEEE journals including IEEE Communications Surveys and Tutorials, IEEE Communications Magazine, and IEEE Internet of Things Journal. His service extends to organizing major conferences such as IEEE Globecom 2015 and IEEE INFOCOM 2016-2017.
Patrizia Paggio serves as an Associate Professor and Senior Researcher within the Department of Nordic Studies and Linguistics at the University of Copenhagen's Faculty of Humanities, concurrently holding a full professorship at the University of Malta's Institute of Linguistics and Language Technology since September 2011. Her scholarly work centers on the intricate relationship between verbal and nonverbal communication modalities, with international recognition for advancing methodologies in multimodal analysis. Academic Background: PhD in Computational Linguistics from the University of Copenhagen (1997), dissertation: "The Treatment of Information Structure in Machine Translation" Professor Paggio's research program investigates how gestures, head movements, and other nonverbal cues interact with spoken language to construct meaning in natural communication. She has pioneered methodologies for constructing and analyzing multimodal corpora, while maintaining technical expertise in machine translation systems, grammar engineering, and content-based querying frameworks. Her theoretical work spans formal syntactic structures, discourse phenomena, information packaging, and ontological representations for linguistic data, consistently bridging computational methods with linguistic theory. Analysis of her recent publications (2020-2025) reveals a sustained focus on computational approaches to nonverbal communication, particularly the automatic detection and annotation of head movements and gestures in both physical and digital environments. Key contributions include the GEHM Zoom corpus for online interaction analysis, eye-tracking studies of emoji processing, and diachronic modeling of historical language change. Her work strategically integrates eye-tracking, corpus linguistics, and machine learning techniques, establishing her at the convergence of linguistic theory, cognitive science, and artificial intelligence applications. Professional Leadership: Coordinator of the international GEHM (Gestures and Head Movements in Language) research network Organizer of MULTIMODAL CORPORA 2018, 4th European/Nordic Symposium on Multimodal Communication, and LREC2022 Workshop on People in Vision, Language and the Mind
Albert Gatt is a Professor of Natural Language Processing at Utrecht University's Department of Information and Computing Sciences, where he also serves as Programme Director for AI & Data Science. He holds an Associate Professor position (on leave) at the University of Malta's Institute of Linguistics and Language Technology. His research focuses on Natural Language Generation (NLG), multimodal models, and under-resourced language support, particularly for Maltese. He leads projects like NL4XAI and MASRI, addressing challenges in explainable AI and speech recognition. Education: Advanced degrees in computational linguistics and AI (not explicitly detailed in text). Key Projects: Multilingual NLG, Vision-Language benchmarks, Maltese ASR, and NLP evaluation methodologies. Research interests span data-to-text generation, vision-language interfaces, and evaluation practices. His work bridges computational linguistics with cognitive science, emphasizing human-AI collaboration. Notable contributions include the TUNA corpus, SimpleNLG toolkit, and foundational studies on referring expression generation. Publications (2025-2024) explore robust fine-tuning, LLM evaluation, and visual-linguistic grounding. Collaborations span academia and industry, addressing ethical AI and language equity. Supervises a global team of researchers and PhD students across multiple institutions, fostering innovation in NLG, multimodal AI, and Maltese language tech.
Brendan S. Gillon is a Professor in the Department of Linguistics at McGill University, Montreal. His research focuses on semantics, pragmatics, and formal methods in linguistics, with notable contributions to the study of classical Indian logic and Sanskrit grammar. He holds a PhD in Philosophy from MIT (1984) and has taught at MIT, University of Alberta, University of Toronto, and McGill since 1991. His work bridges linguistics and philosophy, addressing topics like the semantics of noun phrases, context sensitivity, and cross-cultural language studies. Education: PhD in Philosophy (MIT, 1984), MA in East Asian Studies (University of Michigan, 1975), MA in Sanskrit/Indian Studies (University of Toronto, 1979) Fellowships: Senior Research Fellow at American Institute of Indian Studies (1986-1987), Numata Fellow at Ryokoku University (2006) Research interests include the semantics-pragmatics distinction, mass-count noun distinctions, and formal methods applied to syntax and semantics. Collaborations include projects on lexical semantics and brain-language interactions. He has advised numerous PhD students on topics ranging from comparative linguistics to historical theories of language. Publications span over 50 articles and edited volumes, including Semantics: A Reader (Oxford UP, 2004) and works on Dharmakīrti’s logic and classical Sanskrit grammar. His lectures have been delivered globally, emphasizing interdisciplinary connections between linguistics, philosophy, and cognitive science.
Hugo Ledoux is an academic affiliated with the Faculty of Architecture and the Built Environment at Delft University of Technology, specializing in Urban Data Science. His research focuses on 3D geospatial modeling, including CityGML standards, terrain analysis, and automated reconstruction of urban structures. He has contributed to projects like the DeltaDTM coastal terrain model and the cjdb database solution for CityGML. Education: Not explicitly detailed in text, but inferred through academic roles and publications. Research interests emphasize 3D geoinformation systems, remote sensing applications, and urban data science. His work addresses challenges in 3D city models, building reconstruction, and geospatial validation tools like Val3dity. Recent efforts include improving global terrain models using ICESat-2 and GEDI lidar data. Publications span automated building reconstruction workflows, terrain accuracy assessments, and semantic-guided facade modeling. Awards include the Best Presentation at 3DGeoInfo 2020 and the U.V. Helava Award for Best Paper in 2011. Ledoux has supervised 4 academic works and actively participates in conferences, editorial activities, and open-source software development for geospatial applications. Labs/Teams: Involved in TU Delft’s 3D geoinformation research, contributing to tools like 3dfier and CityJSON for 3D data interoperability.
Per Östberg is a Professor of Speech and Language Pathology at Karolinska Institutet's Department of Clinical Science, Intervention and Technology. He holds a PhD (2008) and Docent title (2013) from Karolinska Institutet. His research focuses on language dysfunction in neurological disorders, including aphasia, apraxia of speech, and dysphagia, with emphasis on stroke and neurodegenerative diseases like Parkinson's. He co-authored the textbook Klinisk neurovetenskap (2015) and leads projects on patient-reported outcomes and speech-language rehabilitation. Education: Doctor of Philosophy, Karolinska Institutet, 2008 Docent, Karolinska Institutet, 2013 Research Interests: His work spans speech disorders in acquired brain injury, longitudinal studies of apraxia recovery post-stroke, and validating clinical tools like the Eating Assessment Tool. He collaborates internationally on registries for stroke aphasia (I-PRAISE) and investigates neuroimaging correlates of speech motor recovery. Publications Trends: Recent work includes comparative studies of anomia in neurological conditions, randomized trials of aphasia treatments, and eye-tracking analysis in reading disorders. Key themes are interdisciplinary approaches to language assessment and rehabilitation efficacy. Advising: Supervised Martin Cederlöf's thesis on psychosis phenotype risks. Affiliated with Karolinska University Hospital and the Enheten för logopedi F67 unit.
Dominique Ritze is a Research Fellow at the Data and Web Science Group of the University of Mannheim. Her research focuses on ontology alignment, semantic web technologies, linked open data integration, and knowledge organization systems. She collaborates with Prof. Dr. Christian Bizer and Prof. Dr. Kai Eckert on projects like InFoLiS II, aiming to advance data integration and semantic web applications. Education: MSc Computer Science (Diplom-Informatikerin) Research Interests: Dominique’s work bridges theoretical and applied aspects of semantic web technologies. Key areas include ontology evaluation frameworks, cross-domain data integration, and the development of tools for provenance tracking and data reuse. She has contributed to methodologies for aligning knowledge organization systems (KOS) and enhancing discovery systems with linked data. Publications Trends: Her articles from 2010-2015 emphasize ontology alignment (e.g., OAEI evaluations), semantic web applications, and data integration techniques. Notable contributions include the ICE-Map visualization for KOS evaluation and the Mannheim Search Join Engine for cross-website table integration. Awards: No scientific awards explicitly listed in the provided texts. Projects & Teams: Active in the Data and Web Science Group, leading projects on web table matching and semantic data integration. Collaborates with global research networks through initiatives like the Ontology Alignment Evaluation Initiative.
Dr. Enayat Rajabi is an Associate Professor of Business Analytics at the Shannon School of Business, Cape Breton University. Holding a PhD in Information and Knowledge Engineering from the University of Alcala (Spain) and a postdoctoral fellowship from Dalhousie University, his research focuses on the intersection of Machine Learning, Knowledge Graphs, and Data Analytics. He actively applies these technologies in healthcare, smart cities, and social media crisis response contexts. Education PhD in Information and Knowledge Engineering, University of Alcala (Spain) Postdoctoral Fellowship, Dalhousie University Research Interests His work bridges Knowledge Graphs with Machine Learning, emphasizing explainability and practical applications. Key areas include: Explainable AI for clinical decision-making Knowledge Graph applications in healthcare systems Social media analytics for emergency response Smart city data integration Generative modeling for tabular data Recent Publications Trends Recent articles highlight: Explainable AI in healthcare settings Industrial breakdown prediction systems Social media influencer detection Smart city infrastructure modeling Advanced data synthesis techniques Continued focus on Knowledge Graph applications
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.
Soon Lay Ki is an Associate Professor at the School of Information Technology, Monash University Malaysia, where she also serves as Associate Head (Graduate Research) since November 2018. Her academic journey began with roles at Multimedia University (MMU), where she was a Senior Lecturer and Deputy Dean (Research and Innovation) from 2016 to 2018. PhD in Web Engineering, Soongsil University, Korea Master of Science in Database, Universiti Putra Malaysia Bachelor of Computer Science, Universiti Putra Malaysia Her research centers on applied natural language processing and data management , with a focus on analyzing domain-specific and social media content. Her work spans aspect-based sentiment analysis , cyberbullying detection , misinformation detection , and relation extraction from conversational texts. Recently, her research has expanded into digital health , particularly emotion-aware mental health chatbots and emotion detection via video data. The most recent articles highlight a strong trend in AI for social good , including legal reasoning, mental health, accessibility, and public health. Her publications appear in high-impact journals and conferences such as Artificial Intelligence and Law , IEEE Transactions on Dependable and Secure Computing , and ACL-affiliated workshops. She has received notable scientific awards, including: ITEX'24 Silver Award for 'MOBOT' mental health chatbot (2024) Silver Medal at Malaysia Technology Expo 2023 for the same innovation The Incubator Grant: Bolster Category (2023) Dr. Soon has graduated seven PhD and three Master’s students, one of whom received the MMU Best Master Thesis Award in 2015. She leads multiple research grants, including FRGS-funded projects and industry collaborations with Telekom Malaysia and Intel . She is currently a Chief Investigator or Primary Chief Investigator on six active projects, including WHinc, WAge, and Epsilon, often in collaboration with Monash Australia and SEACO. She is part of key research teams such as the Action Lab at Monash University Australia and the South East Asia Community Observatory (SEACO) , contributing to inclusive research infrastructure and public health data access initiatives.
Professor Hanem El-Farahaty is a Professor in Linguistics, Translation and Interpreting at the School of Languages, Cultures and Societies, University of Leeds, UK. She holds a PhD from the University of Leeds and has two BA degrees and an MA from the University of Mansoura, Egypt. Her academic roles focus on Arabic media, translation, and interpreting, with a strong research orientation in legal and political discourse. PhD in Translation Studies, University of Leeds MA in Applied Linguistics, University of Mansoura BA in English Language and Education, University of Mansoura BA in English Language and Literature, University of Mansoura Her research interests span comparative linguistics , Arabic/English legal translation , corpus-based translation , media and political translation , and critical discourse analysis , with a particular focus on political satire and multimodal communication in the Arab world. She investigates gender, modality, and constitutional language using corpus-driven methodologies. The analysis of her recent publications reveals a consistent trend in legal corpora development , deontic modality , and political discourse in Arabic and English contexts. Her work bridges translation theory, legal linguistics, and digital media, often using multimodal and corpus-based approaches to analyze political satire and constitutional texts. Her scientific recognition includes: Senior Fellow of the Higher Education Academy (SFHEA) Fellow of the Chartered Institute of Linguists (FCIL) She supervises PhD students and is actively involved in academic leadership, having organized major conferences and panels on Arabic translation and Middle Eastern studies. She has received no explicit mention of grants, but her extensive publication and conference activity suggest sustained research funding. She is a member of several professional bodies, including BRISMES, IATIS, FLITE, and ILLA. She contributes to academic communities through editorial roles, including membership in the Scientific Committee for the Leeds Language Scholar Journal and the International Journal of Translation, Interpretation, and Applied Linguistics . Her work is deeply embedded in interdisciplinary research, connecting linguistics, law, politics, and digital culture.
Markus Stocker is a researcher leading the Knowledge Infrastructures Lab at TIB - Leibniz Information Centre for Science and Technology. He holds a PhD in Environmental Informatics from the University of Eastern Finland, an MSc in Environmental Sciences from the same university, and an MSc in Computer Science from the University of Zurich. His work focuses on research infrastructures, knowledge synthesis, and FAIR data principles, with a strong emphasis on environmental and earth sciences. He collaborates with major European infrastructures like ACTRIS, ICOS, and NEON. His research interests include neurosymbolic systems, digital scholarship, and the integration of research data across disciplines. Prior roles include a postdoc at PANGAEA (University of Bremen) and positions at Hewlett Packard Labs and Clark & Parsia. He actively contributes to the Research Data Alliance, co-chairing the WG Persistent Identification of Instruments. Markus has pioneered projects like the Open Research Knowledge Graph (ORKG) and the sciqa benchmark for scientific question answering. His work emphasizes interoperability, automation, and community-driven knowledge curation, addressing challenges in data management and scholarly communication.
