David Chaves Fraga is an Assistant Professor in the Department of Electronics and Computing at the University of Santiago de Compostela (USC), specializing in Computer Science and Artificial Intelligence. He is also a researcher at CITIUS Research Center and maintains a collaboration with the Declarative Languages and Artificial Intelligence Group (DTAI) at KU Leuven. His research focuses on large-scale semantic data integration and its applications across various domains including DevOps, public procurement, and transportation. He is particularly recognized for his work in Knowledge Graph construction and lifecycle management. As co-chair of the W3C Knowledge Graph Construction Community Group and lead scientist for the European Public Procurement Data Space, he plays a significant role in shaping standards and practical implementations in the field. Dr. Chaves Fraga has published over 50 papers in the Semantic Web domain, with several appearing in top conferences like ISWC and ESWC. His work on 'Declarative Generation of RDF Collections and Containers from Heterogeneous Data' received the Best Paper Award at SEMANTiCS Conference. Margarita Salas Fellowship (2022) Best Paper Award at SEMANTiCS Conference (2024) He actively contributes to the academic community as a committee member for major conferences including ISWC, ESWC, WWW, SIGIR, and CIKM, and serves as a reviewer for leading journals in the Semantic Web field. Dr. Chaves Fraga is also involved in organizing two academic workshop series focused on Knowledge Graph Construction and Semantics for Transport.
Diego Calvanese serves as a Professor at the KRDB Research Centre for Knowledge and Data within the Faculty of Computer Science at the Free University of Bozen-Bolzano, where his work bridges theoretical foundations and practical applications in knowledge systems. An ACM Fellow and EurAI Fellow, he maintains active leadership in the global academic community through editorial roles including PeerJ Computer Science. His research spans knowledge representation and reasoning, ontology languages, description logics, conceptual data modeling, data integration, graph data management, data-aware process verification, and service modeling and synthesis, establishing him as a pivotal figure in formal methods for data-intensive systems. These interconnected domains drive innovations in semantic technologies and large-scale data management frameworks. Notable professional contributions include serving as Program Chair for PODS 2015 and KR 2020, General Chair for ESSLLI 2016, and a 2012-2013 visiting researcher position at the Technical University of Vienna as a Pauli Fellow of the Wolfgang Pauli Institute. His awards reflect exceptional impact: ACM Fellow EurAI Fellow Calvanese operates primarily through the KRDB Research Centre, which functions as his core academic hub for advancing knowledge representation methodologies and fostering international collaborations in data science.
Talin Babikian is an Associate Clinical Professor in the Department of Psychiatry and Biobehavioral Sciences at the David Geffen School of Medicine at UCLA. With extensive expertise in pediatric traumatic brain injury (TBI) and neuroimaging, Dr. Babikian's research focuses on understanding neurocognitive outcomes, recovery trajectories, and the application of advanced neuroimaging techniques to assess brain structure and function following pediatric TBI. Dr. Babikian's research interests span multiple areas within pediatric neurotrauma, including neuroimaging techniques (particularly DTI and MRS), neurocognitive assessment following TBI, white matter integrity disruptions, long-term outcomes of pediatric concussion, and the impact of family environment on recovery. Her work frequently examines how brain injury affects developing neural systems and cognitive functioning in children and adolescents, with emphasis on translating research findings to clinical practice. Analysis of Dr. Babikian's recent publications reveals a strong focus on collaborative, large-scale research initiatives, particularly through the ENIGMA consortium. Her work increasingly addresses health disparities in concussion care, racial biases in sports-related concussion assessment, and the biopsychosocial factors influencing recovery. There's also a growing emphasis on multimodal approaches to understanding brain injury, combining neuroimaging, cognitive assessment, and clinical outcomes to develop more comprehensive models of TBI recovery. Dr. Babikian has been actively involved in significant research funding, most notably as Principal Investigator on the NIH-funded study "Neuroimaging and Recovery after Pediatric Brain Injury" (F32NS053169, 2006-2008). She is a key contributor to several major collaborative efforts including the UCLA RAPBI study, the ENIGMA Pediatric TBI working group, and the CARE4Kids study examining persistent post-concussive symptoms in adolescents. Her work has contributed substantially to our understanding of the neurobiological underpinnings of cognitive and emotional difficulties following pediatric TBI.
Dr. Julie A. Van Dyke serves as Senior Research Scientist and Vice President of Research and Strategic Initiatives at Haskins Laboratories, an independent research institute affiliated with Yale University and the University of Connecticut. She holds adjunct professorships at McMaster University and the City University of New York Graduate Center while maintaining affiliate status at UConn's CT Institute for Brain and Cognitive Sciences. Her NIH-funded research investigates neurocognitive mechanisms underlying language comprehension and reading disorders. Her educational background includes: Ph.D. in Cognitive Psychology from University of Pittsburgh M.Sc. in Computational Linguistics from Carnegie Mellon University Honors B.A. in Computer Science and Linguistics from University of Delaware Dr. Van Dyke's research centers on cue-based retrieval theory, examining how retrieval interference causes comprehension failures in both normative processing and clinical populations (dyslexia, ADHD, age-related decline). She pioneered the application of speed-accuracy tradeoff methodology to diagnose direct-access retrieval mechanisms and investigates individual differences in eye-movement control during reading. Her work bridges cognitive theory with clinical applications through machine learning classifiers for reading disability and fixation-related brain imaging of word-by-word processing. Analysis of her recent publications reveals consistent focus on retrieval interference mechanisms across multiple methodologies: eye-tracking studies establish individual skill factors in oculomotor control, neuroimaging work identifies neural correlates of interference resolution, and machine learning approaches develop diagnostic classifiers. Key trends include the role of predictive timing in fluency disorders and dissociation between grammatical vs. semantic interference effects. Her scientific recognition includes: NIH/NICHD Institutional Post-doctoral National Research Service Award NIH/NICHD Individual Post-doctoral National Research Service Award Dr. Van Dyke actively shapes her field through editorial roles as Associate Editor for Journal of Experimental Psychology: General and Section Editor for Language and Linguistics Compass. Her NIH-funded grants support collaborative projects with researchers including Victor Kuperman, Luca Campanelli, and Clint Johns, focusing on retrieval interference, reading disability mechanisms, and neural underpinnings of comprehension. Current initiatives integrate computational linguistics with cognitive neuroscience to model real-time processing during reading. As Vice President at Haskins Laboratories, she leads a multidisciplinary team utilizing eye-tracking, EEG, fMRI, and machine learning to investigate language processing. Her lab collaborates with the CT Institute for Brain and Cognitive Sciences and maintains strong ties with UConn's neuroscience community, with ongoing data collection for fixation-related brain imaging studies.
Dr. Huseyin Dagdeviren serves as Senior Lecturer and Director of Employability in the School of Computer Science and Engineering at the University of Westminster, where he actively contributes to the Centre for Parallel Computing (CPC). With over two decades of academic experience, he bridges theoretical research with industrial applications through major EU-funded initiatives. His educational foundation includes a BA (Honours) in Business Administration and MSc in Information Systems. This interdisciplinary background informs his research approach, combining technical cloud computing expertise with strategic business perspectives. Dr. Dagdeviren's research centers on complex information systems, with specialized focus on Cloud Computing, Requirements Engineering, and Strategic Management of IT. His work drives innovation in cloud-to-edge orchestration and digital manufacturing through Horizon 2020 projects like CloudiFacturing and DIGITbrain, addressing critical industry challenges in SME digital transformation. Analysis of his 2004-2023 publications reveals an evolving trajectory from foundational database and UML education research toward cutting-edge distributed systems. Recent work (2021-2023) dominates in cloud/edge computing, demonstrating strategic alignment with funded projects and industrial relevance in manufacturing simulation and digital twin deployment. As Director of Employability, he oversees career development programs while securing substantial EU grants including CO-VERSATILE (2020) and DIGITbrain (2020). These multi-institutional projects provide robust funding for the CPC lab and create direct industry pathways for students. The Centre for Parallel Computing operates as his primary research hub, facilitating international collaboration across 10+ European countries. Students benefit from exposure to large-scale EU consortia, industry partnerships with Siemens/Bosch, and hands-on development of production-grade cloud orchestration platforms.
Luciano Baresi is a Full Professor at the Polytechnic University of Milan (Politecnico di Milano), Italy, affiliated with the Department of Electronics, Information and Bioengineering. He earned his laurea (MSc) and PhD in Computer Science from the same institution and has held visiting positions at the University of Oregon (USA), Tongji University (China), and the University of Paderborn (Germany). His research spans software engineering, with current focuses on self-adaptive systems, edge computing, and AI/ML-based software. His work integrates formal methods with practical applications, emphasizing autonomous systems, cloud-edge continuum, and federated learning. Recent publications highlight AI-driven advancements in software testing, resource optimization, and educational tools. Key research themes include: AI/ML for autonomous driving testing and data augmentation Serverless computing at the edge Federated learning system architectures Containerization and cloud resource management Awarded for impactful contributions: RE 2020 Most Influential Paper ICSOC 2020 Best Paper SEAMS 2022 Best Paper He advises 14+ PhD students and leads projects like Ketonet (health app), WHO's Essential Items Estimator, and dynaSpark. As Editor-in-Chief of Proceedings of the ACM on Software Engineering and senior editor for multiple journals, he shapes academic discourse in adaptive systems and software engineering.
Thomas Degueule is a researcher at CNRS (Centre National de la Recherche Scientifique) in France, actively contributing to software engineering research since 2015. He serves on program committees for major conferences including ASE, ICSE, and SLE, with primary research interests in Software Evolution, Empirical Software Engineering, and Domain-Specific Languages. His work focuses on breaking change analysis in APIs and libraries, client-library compatibility testing, and dependency management. He develops practical tools like Roseau for source-based breaking change detection and investigates semantic versioning impacts in ecosystems like Maven Central. His empirical approach leverages large-scale repository analysis to address real-world software maintenance challenges, particularly in Java ecosystems. Recent publications (2023-2025) show consistent contributions to breaking change analysis and compatibility testing, appearing in top venues like ASE, ICSE, and ISSTA. His research bridges theoretical insights with practical tooling for software evolution challenges, demonstrating strong empirical methodology and tool-oriented contributions. No scientific awards are documented in the available information. Degueule has advised no publicly listed students and holds no mentioned research grants. His organizational roles include Program Co-Chair for SLE 2023 and committee positions across multiple conferences, reflecting significant service to the software engineering community.
Kijung Shin is an Associate Professor at KAIST (Korea Advanced Institute of Science and Technology), holding dual appointments in the Kim Jaechul Graduate School of AI and the School of Electrical Engineering (Computer Division). He leads the Data Mining Lab and teaches multiple courses including Graph Mining and Social Network Analysis, Data Mining and Search, and other foundational courses in electrical engineering and AI. Education Ph.D. in Computer Science, Carnegie Mellon University (February 2019) M.S. in Computer Science, Carnegie Mellon University (December 2017) B.S. in Computer Science and Engineering, Seoul National University (August 2015) B.A. in Economics (Double Major), Seoul National University (August 2015) Research Interests Professor Shin's research primarily focuses on data mining, graph algorithms, and network science, with particular expertise in hypergraph analysis, tensor decomposition, and graph neural networks. His work bridges theoretical foundations with practical applications, developing algorithms that can efficiently analyze complex real-world networks. His recent research has expanded into multimodal learning, integration of large language models with graph neural networks, and applications in recommendation systems, satellite imagery analysis, and biological data analysis. His approach combines rigorous mathematical analysis with practical implementation, resulting in numerous open-source software tools that have been widely adopted in both academia and industry. His research has significant implications for social network analysis, fraud detection, recommendation systems, and scientific discovery in various domains. Research Trends Professor Shin's recent publications show a clear trajectory toward more complex network structures, particularly hypergraphs that capture higher-order interactions beyond simple pairwise relationships. His work increasingly integrates traditional graph algorithms with deep learning approaches, especially focusing on how graph neural networks can be improved and made more interpretable. There's also a growing emphasis on practical applications in areas like satellite imagery analysis, medical data, and recommendation systems that address real-world challenges. Scientific Awards Received the PAKDD Best Survey Paper Award for 'Multi-Behavior Recommender Systems: A Survey' (2025) Selected as one of the best short paper candidates of ACM RecSys 2024 (top 7) for 'Revisiting LightGCN' (2024) Selected for oral presentation (2.6% of accepted papers) at AAAI 2024 for 'VITA: 'Carefully Chosen and Weighted Less' Is Better in Medication Recommendation' (2024) Received the IEEE ICDM Best Student Paper Runner-up Award for 'TensorCodec: Compact Lossy Compression of Tensors without Strong Data Assumptions' (2023) Received the SIGKDD Best Research Paper Award and CogX Award for Best Student Paper in AI for 'FRAUDAR: Bounding Graph Fraud in the Face of Camouflage' (2016) Received the Best Senior Thesis Award from Seoul National University (2015) Received the Samsung Humantech Paper Award (1st in Computer Science) (2015) Teaching and Mentoring Professor Shin has taught multiple graduate and undergraduate courses at KAIST since 2019, including Graph Mining and Social Network Analysis, Data Mining and Search, and foundational courses in electrical engineering. He has also co-organized tutorials at major conferences including AAAI, KDD, ICDM, and CIKM on advanced topics in hypergraph neural networks and real-world hypergraph analysis. As the leader of the Data Mining Lab, he mentors numerous graduate students and postdoctoral researchers, fostering a collaborative research environment that has produced significant contributions to the field of data mining and network analysis. Research Leadership Professor Shin leads the Data Mining Lab at KAIST, which focuses on developing novel algorithms for analyzing complex networks and high-dimensional data. The lab has produced numerous influential software tools including D-Cube, M-Zoom, CoreScope, and DenseAlert, which are widely used in both academic research and industry applications. His research group maintains active collaborations with institutions worldwide and has received funding from various sources to support their innovative work in data mining and network analysis.
Marek Mutwil serves as an Associate Professor in the Department of Plant and Environmental Sciences at the University of Copenhagen, specializing in plant biochemistry with research spanning genomics, systems biology, and computational approaches to plant science. His work bridges experimental biology and data-intensive methodologies to address fundamental questions in plant environmental responses. His research program focuses on deciphering regulatory networks in plant stress adaptation, particularly through cross-species analyses of abiotic stress mechanisms in hydroponic systems. A significant portion of his recent work involves developing computational infrastructure for plant science, exemplified by the PlantConnectome knowledge graph that integrates literature-derived biological relationships across 71,000+ plant research articles. This dual emphasis on experimental stress physiology and bioinformatics resource development positions his work at the intersection of molecular plant biology and data science. Analysis of his 2025 publications reveals a consistent trajectory toward integrative plant systems biology, combining hydroponic crop stress experiments with large-scale knowledge graph applications. These works demonstrate growing emphasis on translational bioinformatics tools that convert fragmented plant science literature into structured, queryable biological networks. Scientific Awards: No awards were documented in the provided text. Advising and Grants: The source material contains no references to graduate students, postdoctoral trainees, or research funding sources. Labs and Teams: While affiliated with the Section for Plant Biochemistry, no specific laboratory structure, team members, or collaborative consortia are described in the available information.
Dr. Takayuki Ito is Professor at Nagoya Institute of Technology in the Computer Science & Engineering school. He earned his Doctor of Engineering from Nagoya Institute of Technology in 2000. His academic journey includes positions as a JSPS research fellow, associate professor at JAIST, and visiting scholar at prestigious institutions including USC/ISI, Harvard University, and MIT (visited twice). He has served as a board member of IFAAMAS (International Foundation for Autonomous Agents and Multiagent Systems). Dr. Ito's research primarily focuses on multi-agent systems, automated negotiation, argumentation frameworks, and AI-mediated discussion platforms. His work spans theoretical foundations of argumentation semantics to practical applications in sustainable development, urban planning, and online citizen engagement. He has developed the D-Agree platform for facilitating large-scale online discussions, with notable implementations in Afghanistan for municipal policy-making and SDG implementation. His publication record demonstrates significant contributions to understanding how AI can mediate human discussions, with experiments involving thousands of participants in countries like Afghanistan. His research shows how argumentative agents can improve responsiveness in discussions while also potentially polarizing debates by reinforcing initial stances. His work bridges theoretical computer science with practical social applications, particularly in contexts with challenging participation constraints. Board member of IFAAMAS Developer of D-Agree discussion support system Conducted large-scale experiments in Afghanistan with over 1,000 participants Expert in multi-agent negotiation protocols Dr. Ito's research has substantial implications for democratic processes, particularly in contexts where traditional face-to-face meetings are problematic due to security concerns, cultural restrictions, or pandemic conditions. His work demonstrates how AI mediation can overcome barriers to equal participation, especially for women and religious minorities in restrictive societies.
