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Anna BonifaziView profile

Professor

• Discourse Analysis
• Pragmatics
• Cognitive Linguistics
• +4 more

Prof. Anna Bonifazi holds a Professorship in Discourse Studies at the Department of Linguistics, University of Cologne. Her research spans Discourse Analysis, Pragmatics, Cognitive Linguistics, and Multimodal Communication with a focus on Ancient Greek Linguistics and Oral Epic traditions. She investigates phenomena like anaphoric cohesion in long texts, multimodal storytelling in film and oral traditions, and ancient Greek particles' discursive functions. Her work explores how multimodal elements (e.g., visual, musical, gestural) interact in communication, particularly analyzing films (e.g., mystery genres), ancient texts (e.g., Homeric epics), and South Slavic oral narratives. She examines cross-modal iconicity, viewpoint blending, and the cognitive underpinnings of discourse structures. Recent publications address topics like anaphoric strategies in crime stories and syntactic patterns in ancient Greek conversational frameworks. Prof. Bonifazi's research often bridges classical philology and modern cognitive theories, emphasizing embodied cognition and the interplay between language and other semiotic systems. She collaborates internationally on projects involving gesture analysis in storytelling and ancient papyrological studies. Her interdisciplinary work integrates linguistics with musicology, film studies, and cognitive science. Administratively, she oversees the Discourse Studies research group at Cologne and mentors assistants like Dr. Sandra Debreslioska and Madeleine Frings. Her lab focuses on multimodal discourse analysis using experimental and corpus-based methods.

Hongwei WangView profile

Researcher

• Natural Language Processing
• Interconnected Systems
• Large Language Models
• +6 more

Dr. Hongwei Wang is a Senior Researcher at Tencent AI Lab Seattle , specializing in applied machine learning for Natural Language Processing and Interconnected Systems . His work bridges Knowledge Graphs , Recommender Systems , and Graph Neural Networks , with a focus on large language models and retrieval-augmented generation. Ph.D. (2018), B.E. (2014) in Computer Science from Shanghai Jiao Tong University Postdoctoral Researcher : Stanford University (2019-2021), University of Illinois Urbana-Champaign (2021-2022) Dr. Wang’s research explores integrating Knowledge Graphs with Graph Neural Networks to enhance recommendation systems, language models, and information retrieval. His work spans Retrieval-Augmented Generation , Representation Learning , and GAN-based Graph Modeling , with recent papers on State-Space Exploration for LLM agents and Semantic Watermarking . His 15 most recent publications (2022-2024) focus on Retrieval Granularity , Interactive Memory , and Agent Systems , with keywords spanning Computer Science , Machine Learning , and Knowledge Graphs . Trends highlight advancements in Token-Level Semantic Matching , Schema-Guided Event Prediction , and Multi-Document Summarization . Scientific Awards: 2020 CCF Outstanding Doctoral Dissertation Award 2018 Google Ph.D. Fellowship Dr. Wang contributes to open-source projects like DKN and RippleNet , with 11 repositories on GitHub. He actively engages in Knowledge Graph Conferences (KDD, WWW, AAAI) and studies Chinese Classical Poetry and Film Arts .

Tim OatesView profile

Professor

• Machine Learning
• Artificial Intelligence
• Deep Learning
• +7 more

Dr. Tim Oates is a Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County . His research spans machine learning, artificial intelligence, and brain-machine interfaces, with a focus on weakly supervised methods, human-in-the-loop reinforcement learning, and grounded policy development for robotics. Ph.D., Computer Science, University of Massachusetts, Amherst, 2000 M.S., Computer Science, University of Massachusetts, Amherst, 1997 B.S., Computer Science and Electrical Engineering, 1989 Current research threads include: Developing non-invasive brain injury severity assessment via medical time series Modeling human brain development through computational frameworks Designing algorithms for autonomous robotic learning Recent publications highlight AI security mechanisms (backdoor detection via tensor decomposition, matrix factorization) Medical applications (3D artery reconstruction, skin lesion diagnosis, EEG denoising) Neuro-symbolic integration (holographic representations, language-guided reinforcement learning) Mathematical reasoning (schema-based problem solving, subitizing algorithms) Contact: oates@cs.umbc.edu | Office: 336 Information Technology and Engineering (ITE) Building

Mojgan A. JadidiView profile

Associate Professor

• Geospatial Visual Analytics
• Building Information Modeling (BIM)
• 3D GIS Integration
• +5 more

Dr. Mojgan A. Jadidi serves as Associate Professor in the Teaching Stream and Director of Common Engineering & BSc Science within the Department of Civil Engineering at York University's Lassonde School of Engineering. A Professional Engineer (P.Eng) and founder of the GeoVA Lab, she leads research at the intersection of geospatial analytics, digital infrastructure, and innovative engineering education aligned with UN Sustainable Development Goals. Education: PhD in Geomatics, Université Laval (2014) MSc in Earthquake and Seismology Engineering, ROSE Center (Italy) & Université Joseph Fourier (France) BSc in Civil-Survey Engineering, Iranian University of Science and Technology Research Focus: Her pioneering work in Geospatial Visual Analytics spans 2D/3D environments, Building Information Modeling (BIM) and 3D GIS integration, and Spatial Quantum Computing applications for smart cities. She develops Infrastructure Digital Twins using sensor data fusion while revolutionizing engineering education through gamification and augmented/virtual reality pedagogies that transform complex spatial concepts into immersive learning experiences. Research Trends: Recent publications (2021-2023) demonstrate convergent innovation across three domains: (1) Building energy optimization through BIM-graph analytics, (2) Transportation safety via AI-driven situational awareness, and (3) Educational technology using VR sandboxes and visual-verbal comics. These works consistently integrate quantum computing principles and UN SDG frameworks to solve urban sustainability challenges. Scientific Recognition: ASEE Zone III Best Paper Award (2023) ASEE Saint Lawrence Best Research Paper & Poster (2022) 3D GeoInfo Conference Best Paper (2018) NSERC Postdoctoral Fellowship (2016) ESRI Student Award (2011) Erasmus Mundus Scholarship (2006) Research Leadership: As Associate Director of York's ESRI Center of Excellence, she manages multi-source funding from NSERC, Mitacs, and York University internal grants. Her professional service spans global organizations including ISPRS Commission IV (Secretary), IEEE Women in Engineering (Member), PEO Etobicoke (Chair), and buildingSMART Canada (Committee Member), driving standards for BIM and digital twin implementation in urban infrastructure. Lab Innovation: The GeoVA Lab develops cutting-edge tools including the TopoSurvey Game for immersive surveying education, PAN-Lassonde XR Sandbox for virtual lab experiences, and quantum computing frameworks for bike-sharing optimization, establishing new paradigms in spatial data interaction and engineering pedagogy.

John M. HendersonView profile

Professor

• Cognitive Psychology
• Visual Cognition
• Scene Perception
• +3 more

John M. Henderson is a Distinguished Professor at the University of California, Davis , affiliated with the Visual Cognition Lab . He holds additional roles at the Center for Mind and Brain , Center for Vision Science , Center for Neuroscience , and Plasticity and Memory Program . As an editor for Collabra: Psychology and associate editor for Journal of Experimental Psychology: General , he contributes to open science and cognitive research dissemination. Ph.D. in Cognitive Psychology, University of Massachusetts, Amherst (1988) M.S. in Cognitive Psychology, University of Massachusetts, Amherst (1986) B.S. in Psychology, University of Massachusetts, Amherst (1983) Professor Henderson’s research investigates how visual information is acquired, recognized, and integrated into cognitive systems to guide behavior. His work combines scene perception , reading processes , and visual memory using eye tracking , fMRI , brain stimulation , and computational modeling . Recent studies explore semantic guidance of attention in natural scenes, neural correlates of fixation duration, and developmental attentional patterns. His 15 most recent publications (2023-2025) reflect a focus on semantic processing in visual cognition , scene perception , and computational modeling of attention . Topics include meaning-based attentional guidance, deep learning applications in scene analysis, and neural mechanisms of memory-guided eye movements. Collaborations span cognitive neuroscience, developmental psychology, and AI-driven scene understanding. Scientific honors include: Google Scholar Classics recognition (2017) for groundbreaking 2006 paper Fellow of the Association for Psychological Science, American Psychological Association, and Psychonomic Society Grants from the National Eye Institute and National Institute on Aging support his work on visual cognition and aging. His lab trains students in cognitive methods and interdisciplinary research, bridging psychology, neuroscience, and computational modeling.

Craig KnoblockView profile

Research Professor

• Knowledge Graphs
• Data Semantics
• Schema Alignment
• +5 more

Craig Knoblock serves as Keston Executive Director of the Information Sciences Institute (ISI) at the University of Southern California (USC), Vice Dean of the USC Viterbi School of Engineering, and Research Professor of Computer Science and Spatial Sciences. He also directs the Data Science Program and the Center on Knowledge Graphs at USC. His educational background includes a Ph.D. and M.S. in Computer Science from Carnegie Mellon University (1991, 1988) and a B.S. with honors in Computer Science from Syracuse University (1984). Knoblock's research focuses on data semantics , specializing in source modeling, schema and ontology alignment, entity and record linkage, data cleaning, Web data extraction, and knowledge graph construction. His work bridges computer science, geospatial analysis, and artificial intelligence to solve complex data integration challenges. Recent projects emphasize historical map digitization, geospatial knowledge graphs, and smart city applications. His 300+ publications demonstrate consistent contributions to knowledge graphs and geospatial data integration, with a growing emphasis on historical map analysis and urban applications. The research trajectory shows increasing interdisciplinary collaboration across computer vision, geoinformatics, and domain-specific applications. IEEE Fellow (2020) ACM Fellow (2017) AAAI Fellow (2004) Robert S. Engelmore Memorial Lecture Award (2014) Donald E. Walker Distinguished Service Award (IJCAI, 2018) Use-Inspired Research Award (USC Viterbi, 2018) As Executive Director of ISI, Knoblock oversees one of USC's premier research centers with significant federal funding. His leadership extends to directing the Center on Knowledge Graphs and the Data Science Program. While specific grant details aren't provided, his extensive publication record and leadership roles indicate substantial research funding across data integration, knowledge representation, and geospatial applications. His work bridges theoretical computer science with practical applications in historical preservation, urban planning, and resource management through collaborative projects with government agencies and industry partners. Knoblock leads the Center on Knowledge Graphs at USC, focusing on developing techniques for building and utilizing knowledge graphs across diverse domains. His team combines expertise in artificial intelligence, geospatial analysis, and data integration to tackle challenges in historical map digitization, urban applications, and resource discovery. The research group maintains strong connections with both academic and government partners through the Information Sciences Institute's extensive network.

Egor KostylevView profile

Associate Professor

• Foundations of AI
• Symbolic AI
• Description Logics
• +9 more

Egor Kostylev serves as an Associate Professor in the Department of Informatics within the Faculty of Mathematics and Natural Sciences at the University of Oslo. His research focuses on the theoretical foundations connecting symbolic and sub-symbolic artificial intelligence, particularly examining relationships between formal logic systems and machine learning approaches. His educational background includes an MSc (Specialist, 2005) and PhD (Candidate, 2009) from Lomonosov Moscow State University under Prof. Vladimir A. Zakharov. He subsequently held research positions at the University of Edinburgh (2010-2013) and the University of Oxford (2013-2020) before joining the University of Oslo in 2020. Kostylev's research interests center on bridging symbolic AI formalisms with sub-symbolic approaches. He investigates connections between various logics (Description Logics, Temporal Logics, Datalog), query languages (SPARQL, Regular Path Queries, OTTR), and machine learning formalisms (Graph Neural Networks, Markov Logic Networks). His work addresses critical challenges in Explainable, Trustworthy, and Green AI through theoretical foundations that connect different AI paradigms. His publication record demonstrates consistent high-impact contributions in theoretical computer science and AI, with numerous publications in top venues including AAAI, LICS, Journal of the ACM, and ICLR. His recent work shows a clear trajectory toward unifying logical reasoning with neural network approaches, particularly through graph neural networks and their connections to logical formalisms. The research spans theoretical foundations of knowledge representation, temporal reasoning in knowledge bases, and the logical expressiveness of modern neural architectures. As a research leader, Kostylev supervises multiple PhD students including Shuwen (Aurora) Liu, Maximilian Pflüger, Roxana Pop, Dongzhuoran Zhou, and Erik Snilsberg. He serves as a Research Theme Leader for the Integreat SFF: Norwegian Centre for Knowledge-driven Machine Learning. His teaching responsibilities include IN3020/4020 Database Systems courses. He leads the Data and Knowledge Management (DKM) research group at the University of Oslo, which focuses on foundational aspects of knowledge representation, database theory, and the intersection with modern machine learning techniques. The group actively collaborates with international researchers and contributes to advancing theoretical understanding of how symbolic and neural approaches to AI can complement each other.

Michael GohView profile

Professor

• Intercultural Competence
• Cultural Intelligence
• Diversity, Equity, and Inclusion in Higher Education
• +3 more

Michael Goh serves as Professor and Campbell Leadership Chair in Education and Human Development at the University of Minnesota's College of Education and Human Development, where he coordinates Graduate Programs in Higher Education. Previously (2017-2022), he held the systemwide position of Vice President for Equity and Diversity across the university's five campuses. His educational foundation includes: PhD in Counseling and Student Personnel Psychology from the University of Minnesota MS in Counseling and Counselor Education from Indiana University BA in Psychology and Sociology (double major) from Indiana University Goh's research centers on cultural intelligence (CQ) and intercultural competence development within higher education contexts, examining cross-cultural counseling practices, leadership for inclusive excellence, and culturally intelligent frameworks for mental health services. His interdisciplinary approach bridges psychology, sociology, and educational leadership to address diversity, equity, and inclusion challenges in global academic environments, with particular focus on international student development and culturally responsive administration. His 2020-2025 publications reveal consistent exploration of intercultural competence in higher education internationalization, with strong emphasis on Asian case studies (Japan, Singapore) and practical applications for DEI leadership. Recurring themes include global citizenship education frameworks, equity-driven institutional transformation, and cross-cultural therapeutic expertise development, demonstrating both theoretical depth and practical implementation strategies across diverse educational settings. Notable recognitions include: Fellow of the American Psychological Association (2024) Commissioner for Midwestern Higher Education Compact (2021-2025) Senior Fellow at Minnesota Institute for Trauma Informed Education (2021-present) Pacific Circle Consortium Honors Recognition for cross-border scholarly work (2018) Exemplary Diversity Leadership Award from American Counseling Association (2010) Multiple distinguished teaching and mentorship awards from University of Minnesota His grant leadership includes NSF-funded Louis Stokes North Star STEM Alliance targeting underrepresented students in STEM fields, and NIH Partners in Research Grant developing culturally responsive children's mental health practices. Current projects focus on linguistic access to mental health services and multinational studies of expert counselors across Asia. Goh previously directed the University of Minnesota's Institute for Diversity, Equity, and Advocacy (IDEA), fostering interdisciplinary equity research, and maintains connections with the Workforce Development and Research Lab through his focus on culturally intelligent leadership development.

Arnab NandiView profile

Professor

• Database Systems
• Human-in-the-Loop Data Analytics
• Next-Generation Query Interfaces
• +10 more

Arnab Nandi is a Professor in the Department of Computer Science & Engineering at The Ohio State University. His work bridges human interaction with data infrastructure, focusing on database systems, LLM-augmented analytics, and immersive query interfaces. Education: PhD in Computer Science & Engineering from the University of Michigan Leadership: Co-founder of OHI/O Hackathon Program and STEAM Factory interdisciplinary network Research spans human-in-the-loop data analytics , vibe querying (natural language + gestural interfaces), LLM integration into education, and climate response systems . Key projects include Omni (multimodal exploration), GestureDB , and Icarus (clinical pipelines). Recent publications analyze LLM-driven query stacks (HILDA 2025), video analytics (SIGMOD 2022), and data sunglasses for cognitive limits (HILDA 2025). Awards include NSF CAREER Google Faculty Research Award IEEE TCDE Early Career Award ACM Distinguished Member Advises students in database innovation , with alumni at Amazon, AWS, Roblox, and Meta. Teaches CSE 3241 (Database Systems), CSE 5889 (Software Startups), and CSE 5242 (Advanced Databases).

• Data Quality
• Database Systems
• Data Profiling
• +7 more

Felix Naumann is a Professor at the Hasso Plattner Institute in Potsdam, Germany, specializing in data management and database systems. His research focuses on data quality, data profiling, entity resolution, functional dependencies, and database optimization. He has authored over 300 publications in top-tier conferences and journals, including VLDB, SIGMOD, and ICDE. His work bridges theoretical foundations with practical systems, such as TASHEEH for data cleaning and PRISMA for privacy-preserving schema matching. He serves as an editor for the ACM Journal of Data and Information Quality and has led initiatives on hybrid conference formats and inclusive computing. Key contributions include: Data Quality: Pioneered studies on data quality's impact on machine learning and developed tools like AutoTSAD for anomaly detection. Data Dependency Discovery: Advanced functional and inclusion dependency mining algorithms, including Hitting Set Enumeration methods. Schema Matching & Integration: Created systems like BrewER and Frost for entity resolution and schema alignment. Educational Impact: Led massive open courses in data engineering with over 10,000 participants. His work emphasizes practical applications in cultural heritage data (ReCLAIM), Wikipedia table analysis, and cross-platform data systems (RHEEMix). He collaborates extensively with industry and academia, addressing challenges in dynamic datasets and data governance.

Steven HuprichView profile

Professor

• Personality Disorders
• Psychoanalytic Therapy
• Clinical Assessment
• +2 more

Steven K. Huprich is Professor of Psychology and Associate Director of the Doctoral Program in Clinical Psychology at the University of Detroit Mercy. He specializes in personality disorders, psychoanalytic psychotherapy, and clinical assessment. Huprich holds a Ph.D. from the University of North Carolina at Greensboro and completed clinical training at SUNY Upstate Medical University and Columbia University. His research examines classification and assessment of personality pathology, with focus on malignant self-regard as a unifying construct for depressive, narcissistic, and self-defeating personalities. Huprich has published over 125 peer-reviewed articles and authored/edited six textbooks, including 'Personality Disorders and Pathology: Integrating Clinical Assessment and Practice in the DSM-5 and ICD-11 Era' (APA, 2022). Honors include the Theodore Millon Award from the American Psychological Association (2013) for mid-career excellence in personality research. He maintains a clinical practice specializing in psychoanalytically-informed psychotherapy and diagnostic assessments.

Birbili MariaView profile

Professor

• Early Childhood Education
• Curriculum Development
• Teacher Education
• +4 more

Professor Maria Birbili serves as a Professor in the Department of Pedagogy at the School of Early Childhood Education, part of the Faculty of Education at Aristotle University of Thessaloniki. Her academic specialization lies in Applied Pedagogy within early childhood education, with a focus on curriculum development, teacher education, and child-centered learning practices. She holds a DPhil in Educational Studies (2000) and has contributed significantly to advancing educational methodologies through her research. Her academic career includes teaching courses such as *Assessment in Education*, *Early Childhood Pedagogy and Practice I*, and *Teaching Methodology*. Her research interests emphasize early childhood educational policies, teacher mediation in curriculum design, and the integration of computational thinking in K-12 education. She actively explores themes like parent involvement, professional development for educators, and cross-cultural curriculum comparisons, as seen in her work contrasting Greece and New Zealand’s early childhood policies. Birbili’s publications reflect a commitment to bridging theory and practice, addressing challenges in curriculum implementation, child questioning strategies, and pedagogical documentation. Her work often highlights the need for systemic reforms in Greece’s centralized educational framework to better support teachers and families. She is also engaged in digital initiatives, such as building online teacher communities and leveraging learning analytics for educational insights. Education: DPhil in Educational Studies (2000) Affiliation: School of Early Childhood Education, Aristotle University of Thessaloniki Contact: mmpirmpi@nured.auth.gr | +30 2310991273

• Autobiographical Memory
• Prospective Cognition
• Cognitive Biases in Depression
• +5 more

Dr. Rachel Anderson is a Senior Lecturer in Psychology at the University of Hull, where she has been a faculty member since 2010. She completed her PhD at Goldsmiths, University of London in 2007 and previously held positions at Leeds Beckett University and the Institute of Psychiatry, Psychology, and Neuroscience. Her academic affiliations include: Department of Psychology, University of Hull Centre for Psychological Science Cognition and Development Research Group Dr. Anderson's research primarily focuses on the intersection of memory and future thinking, with particular emphasis on how these cognitive processes are affected in emotional disorders like depression. Her work explores several key areas: The cognitive processes underlying memory and future thinking, including their similarities and differences The functional purpose of memory and future thinking in relation to sense of self, psychological well-being, and goal-oriented behavior The relationship between memory/future thinking impairments and psychological distress The role of these cognitive processes in chronic health conditions such as Parkinson's disease Potential therapeutic applications of memory and future thinking for promoting psychological and physical well-being Dr. Anderson has published extensively from 2007-2024, with recent work focusing on emotional anticipation in depression, visual imagery effects on memory, and physical activity memories. Her publications reveal a consistent research trajectory examining how memory and future thinking interact with emotional states, particularly in depressive disorders, with significant implications for understanding cognitive mechanisms underlying emotional disorders. Dr. Anderson has made notable contributions to understanding how episodic simulation affects future expectancies, how memory biases operate in depression, and how visual imagery influences both prospective and autobiographical memory processes. Her work bridges fundamental cognitive psychology with clinical applications. As an educator and mentor, Dr. Anderson actively supervises PhD students whose research aligns with her interests in false memories, autobiographical memory, and emotional disorders. She has successfully guided multiple students to completion while maintaining an active research program.

Tova MiloView profile

Professor

• Databases
• Big Data Management
• Machine Learning for Data Management
• +7 more

Professor Tova Milo is the Chair for Information Management at the School of Computer Science, Tel Aviv University, leading the prominent Databases Lab (DB Group). Her research spans databases, big data management, crowd-based data sourcing, and business process querying, with significant contributions to data integration and semi-structured data systems. Her research interests focus on innovative approaches to data management challenges through machine learning integration, crowd computing, and business process analysis. Key projects include Business Process Querying (BPQ) for analyzing BPEL specifications, MoDaS for crowd-based data management, and PROX for data provenance summarization. Her work bridges theoretical foundations with practical applications in fraud detection, recommendation systems, and data cleaning. Analysis of her recent publications reveals strong trends in human-in-the-loop data management systems, with growing emphasis on crowd integration for data cleaning and knowledge acquisition. Her research increasingly combines traditional database theory with machine learning techniques for scalable big data processing, while maintaining focus on business process modeling and provenance tracking. ACM PODS Alberto O. Mendelzon Test-of-Time Award (2010) ERC Advanced Investigators grant MoDaS (2011) The Weizmann Prize for Exact Sciences (2017) VLDB Women in Database Research award (2017) IEEE TCDE Impact award (2022) ISF Breakthrough Research Grant (2022) Doctorate Honoris Causa, University of Zurich (2023) ACM Fellow Member of Academia Europaea Professor Milo has advised over 30 graduate students including PhD candidates like Yael Amsterdamer and Ohad Greenshpan, and numerous MSc students working on projects including MoDaS, BPQ, and EDOS. Her research has been supported by major grants including the ERC Advanced Investigators grant and ISF Breakthrough Research Grant. She actively collaborates with industry partners including IBM and Microsoft, particularly in business process management standards. She directs Tel Aviv University's Databases Lab, which maintains the DB Group with multiple research streams including business process querying (BPQ), crowd-based data management (MoDaS), and self-adaptive data dissemination (EDOS/COLT). The lab operates from the Schreiber Building (M-20) and maintains strong international collaborations, particularly with European institutions through the ERC-funded MoDaS project.

Axel PolleresView profile

Professor

• Semantic Web
• Knowledge Graphs
• Ontology Reasoning
• +7 more

Axel Polleres is a full professor at the Institute for Data, Process and Knowledge Management in Vienna University of Economics and Business (WU Wien). He leads the department of Information Systems and Operations Management while maintaining active research in knowledge graphs, semantic web technologies, and ontology engineering. PhD and Habilitation from Vienna University of Technology Former positions at University of Innsbruck, Universidad Rey Juan Carlos, DERI Ireland, and Siemens AG Co-chair of W3C SPARQL working group Editorial board member for Semantic Web Journal and IJSWIS His research focuses on: Querying and reasoning over ontologies Graph schema languages (SHACL, SPARQL) Wikidata constraint formalization Ontology reuse in collaborative platforms Crisis management knowledge graphs FAIR data principles implementation Recent publications analyze knowledge graph evolution, constraint validation methodologies, and semantic web standardization efforts. Key topics include: OWL/RDF interoperability solutions Unit conversion systems for Wikidata Partition-based query processing frameworks Network resilience analysis for urban planning Open data platform discovery tools Temporal analysis of collaborative knowledge graphs He has co-organized major conferences like ISWC2023 and ESWC workshops while maintaining active roles in European research projects. Current work involves spatiotemporal knowledge graphs for city resilience and semantic web infrastructure development.