Li Qin is a Chair and Professor of Management Information Systems at the Department of Accounting, Taxation, Law, and Information Sciences in Silberman College at Fairleigh Dickinson University (FDU). She holds a BA and BS from Tianjin University, China, and an MBA and PhD from Rutgers University, USA. Research Focus: Social media and commerce, information security/privacy, cross-cultural studies of information systems. Teaching: Courses include Information Systems for Managers, Strategic Management of Information Systems, and Database Applications in Business. Her scholarly work has been published in journals such as the Journal of Computer Information Systems , Internet Research , and International Journal of Human-Computer Interaction . Globally, she has served as a Visiting Professor at IESEG School of Management in France and taught in FDU’s partnered program in Chengdu, China. Scientific Awards Silberman Global Faculty Fellow
William Henrich Due serves as a Lecturer at the Department of Computer Science (DIKU), University of Copenhagen, within the Machine Learning section. His work intersects with the SCIENCE AI Centre and leverages the department's high-performance compute cluster for research in quantum computing, sustainable AI, and medical applications. Research focuses span quantum machine learning (biomolecular simulations, photonic processors), sustainable AI systems (energy efficiency, climate impact), and clinical applications (EEG analysis, medical imaging). His recent publications reveal strong activity in quantum-classical hybrid systems, with 8/15 recent papers addressing quantum computing challenges. The work emphasizes practical implementations in medical imaging and resource-constrained environments. His research aligns with DIKU's Machine Learning section priorities including medical imaging biomarkers and sustainable computing. Key infrastructure includes TreeSense for remote sensing and the department's dedicated compute cluster. No scientific awards were explicitly documented in the provided materials. Due contributes to DIKU's teaching mission as a Lecturer while engaging with the SCIENCE AI Centre's interdisciplinary initiatives. His work connects with medical imaging applications and quantum computing infrastructure development. Active in the Machine Learning section's research ecosystem, his work intersects with medical imaging analysis and quantum computing applications, utilizing specialized resources like TreeSense for environmental monitoring.
Maria Maistro is a Tenure Track Assistant Professor at the Department of Computer Science, Faculty of Science, University of Copenhagen. Her research focuses on Information Retrieval (IR) with emphasis on evaluation, reproducibility, click log analysis, learning to rank, expert search, and machine learning applications to IR. She contributes to the AMAOS project aiming to advance expert search methodologies. PhD in Computer Science (IR) from University of Padua (2017) MSc in Mathematics (probability, stochastic methods) from University of Padua (2014) Maria's research addresses critical challenges in IR evaluation through stochastic modeling and user signal analysis. She investigates fairness-relevance tradeoffs in recommender systems, cross-cultural retrieval frameworks, and explainability techniques for language models and recommendation algorithms. Her work combines theoretical foundations with practical applications in healthcare records, insurance domains, and culinary contexts. Recent publications (2024–2025) demonstrate her expertise in: Retrieval-Augmented Generation (RAG) for cross-cultural adaptation Fairness evaluation in recommender systems EEG-based semantic relevance analysis Session-based recommendation explainability Language model pruning consistency Reproducibility frameworks for IR experiments Maria actively participates in academic activities through conference organization roles, including: Organizer, CENTRE@CLEF 2019 Organizer, CENTRE@NTCIR 2019 Organizer, CENTRE@CLEF 2018 Organizer, LEARNER 2017
Markus Gross is a Professor of Computer Science at ETH Zurich, where he founded the Computer Graphics Laboratory in 1994. He also serves as the Chief Scientist of the Walt Disney Studios and Director of DisneyResearch|Studios, a position he has held since 2008. His work bridges academia and industry, with research that has been applied in Hollywood films, sports broadcasting, and medical applications. Professor Gross received his Master of Science in electrical and computer engineering and his Ph.D. in computer graphics and image analysis from Saarland University in Germany in 1986 and 1989. His research spans multiple domains of computer graphics and visual computing. Early in his career, he pioneered point-based graphics techniques that offered alternatives to traditional triangle-based rendering pipelines. More recently, his work has focused on digital humans, AI characters, and machine learning applications for visual computing. His research has led to significant practical applications, including the Medusa capture system used in Hollywood films, the blue-c immersive telepresence system, and the Liberovision technology now used by major sports broadcasters. Analysis of his recent publications reveals a strong focus on neural rendering techniques, particularly around Gaussian splatting and diffusion models. His work increasingly integrates AI with traditional computer graphics methods, with applications in digital humans, medical visualization, and video processing. Many papers demonstrate practical applications in film production, medical treatment planning, and interactive systems. Professor Gross has received numerous prestigious awards throughout his career: 2024 Eurographics Gold Medal 2021 Steven Anson Coons Award for outstanding creative contributions to computer graphics 2019 Technical Achievement Award from the Academy of Motion Picture Arts and Sciences 2013 Karl Heinz Beckurts-Preis 2013 Konrad-Zuse-Medaille für Informatik 2013 Technical Achievement Award from the Academy of Motion Picture Arts and Sciences 2012 Academy Sci-Tech Oscar award for Wavelet Turbulence Professor Gross has mentored numerous Ph.D. students throughout his career, with 20 Ph.D. students contributing to his blue-c project alone. His research has been supported by significant funding from both academic and industry sources, enabling the creation of multiple startups including Cyfex, Novodex, LiberoVision, Dybuster, and Animatico (acquired by Nvidia in 2022). He leads the Computer Graphics Laboratory at ETH Zurich and DisneyResearch|Studios, fostering collaboration between academic research and practical industry applications. His teams have developed groundbreaking technologies that have impacted film production, sports broadcasting, medical visualization, and educational technology.
Ludmila Ivanova Torlakova is an Associate Professor at the Department of Foreign Languages, University of Bergen, specializing in Arabic linguistics and phraseology. Her work explores idioms, figurative language, and metaphorical frameworks across political, literary, and educational contexts, with a focus on Modern Standard Arabic and its socio-cultural dimensions. Her research spans metaphor analysis in crisis communication (e.g., Oman's COVID-19 discourse), political rhetoric, and literary texts like Hanan al-Shaykh's novels. She investigates ʾiḍāfah constructions, body-part idioms, and pedagogical strategies for teaching multi-word expressions, contributing to lexicography and media studies. Torlakova’s publications highlight trends in Arabic phraseology, including the persistence of historical idioms in contemporary media and metaphorical framing in political and health-related discourses. Her academic lectures address topics such as curriculum design for Arabic programs and the intersection of language and cultural identity.
Jan G. Bjålie is a Professor and Vice Dean for Research and Innovation at the Faculty of Medicine, University of Oslo. He has led neuroinformatics development since the 1990s and held leadership roles including Head of the Department of Basic Medical Sciences (2009-2016) and Head of Infrastructure Development for EBRAINS. His research bridges neuroanatomy, computational neuroscience, and digital brain atlases. Doctor of Medicine, University of Oslo (1990) Associate Professor (1991), Professor (1997) in Basic Medical Sciences 2016-: First Leader, International Neuroinformatics Coordinating Facility 2017-: Coordinator, Human Brain Project Neuroinformatics 2019-2021: Head, International Brain Initiative Research Interests span neuroinformatics, brain architecture, and digital brain atlases. His work focuses on: Developing standardized frameworks for brain atlas utilization 3D spatial mapping of neural systems Neuroanatomical connectivity in rodent models Integration of multi-modal neuroscience data International collaboration in brain research Computational modeling of brain structure-activity relationships Recent Publications highlight advancements in automated brain imaging registration, developmental brain mapping, cross-cultural neuroscience perspectives, and genetic influences on brain composition. His neuroinformatics tools (e.g., DeepSlice, Waxholm Space, AtOM) enable global standardization of brain data analysis. Leadership Roles include: Vice Dean for Research and Innovation (2023-2026) Infrastructure Development Lead for EBRAINS (2019-) International Brain Initiative Head (2019-2021) Coordinating EU-funded Human Brain Project neuroinformatics Collaborations extend across major brain projects in the USA, Japan, and Europe through initiatives like the International Brain Initiative and ESFRI roadmap projects.
Tom Hanika is a Visiting Professor (W2) at the University of Hildesheim since April 2023, having previously been affiliated with the University of Kassel in the Department of Electrical Engineering/Computer Science. He also held a visiting professorship at Humboldt University of Berlin until February 2024 and has taught at the University of Würzburg. His academic journey includes interim professorships and lectureships across multiple German institutions. Dr. Hanika's research centers on the theoretical foundations of knowledge discovery in graph data structures with particular emphasis on formal concept analysis. He applies advanced mathematical techniques from algebra, geometric measure theory, topology, and logic to address fundamental challenges in artificial intelligence and machine learning. His work bridges pure mathematics with practical applications in data science, especially focusing on the 'curse of dimensionality' in high-dimensional data spaces. His publication record reveals a strong trajectory in intrinsic dimension analysis, conceptual measurement theory, and knowledge representation in formal contexts. The most recent works demonstrate increasing focus on large-scale geometric learning and the mathematical underpinnings of dimensionality effects in machine learning systems. His research consistently connects theoretical mathematics with practical data science applications. Dr. Hanika serves actively in the academic community as an Editorial Board Member and has chaired program committees for the International Conference on Formal Concept Analysis (ICFCA 2021) and the International Conference on Conceptual Structures (ICCS 2021). He regularly reviews for top journals including Discrete Applied Mathematics and Scientometrics. He leads the 'Dimension Curse Detector' project funded by the LOEWE Exploration program of the State of Hesse, and maintains significant open-source contributions including conexp-clj (a Formal Concept Analysis research tool) and BibSonomy (a scholarly social bookmarking system). These projects serve as both research platforms and community resources for the formal methods and data science communities.
Giovanni Sileno is an academic specializing in Artificial Intelligence, Logic Programming, and Knowledge Representation, with teaching appointments at the University of Amsterdam, EPITA Paris, and Université Pierre et Marie Curie. His primary affiliation is with the Informatics Institute at the University of Amsterdam where he serves as a Lecturer. His research interests span multiple domains within computer science, focusing particularly on formal methods for normative systems, agent-based programming, and logic-based knowledge representation. His work bridges theoretical computer science with practical applications in policy modeling, forensic science, and business information systems. Dr. Sileno has developed several notable software projects including AgentScriptCC for single-threaded intentional agents, DCPLschema for normative policy specifications, and libraries for normative primitives in Answer Set Programming. His GitHub activity shows consistent contributions from 2012 through 2025, indicating active engagement in both research and development. His publications reflect a strong focus on formal methods applied to practical problems, with particular emphasis on normative systems, agent architectures, and logic programming applications. The research trajectory shows evolution from foundational topics in logic and numeral systems toward increasingly sophisticated applications in policy modeling and agent-based systems. As an educator, he has taught across multiple institutions and disciplines, covering topics from basic programming paradigms to advanced knowledge representation and formal modeling techniques. His teaching spans undergraduate to master's level courses in information studies, cognitive science, data science, and forensic science.
Pardeep Sud is a Senior Lecturer in Mathematics and Statistics at the University of Wolverhampton's School of Mathematics and Computer Science , part of the Faculty of Science and Engineering. He holds a BSc (Hons) in Physics from Imperial College London and a Postgraduate Certificate in Learning and Teaching in Higher Education from the University of Wolverhampton. His research focuses on statistical cybermetrics, citation analysis, and bibliometric methodologies, contributing to journals like Quantitative Science Studies and Journal of Informetrics . Prior to academia, he worked in actuarial science (pension modeling) and natural gas industry R&D (fluid flow modeling). He teaches undergraduate/postgraduate modules including Operational Research, Calculus, and Financial Mathematics. Professional activities include participation in the 2020 Wolverhampton Impact Day, focusing on research impact and policy engagement. His work explores interdisciplinary topics such as gender disparities in academia, international collaboration impacts, and altmetrics evaluation. A key member of the Statistical Cybermetrics Research Group, his contributions bridge quantitative research methods with real-world applications in policy and industry. Teaching responsibilities span core mathematical techniques, advanced calculus, and financial modeling. Research output emphasizes data-driven analysis of scholarly communication, citation patterns, and digital scholarship trends. Over 18 peer-reviewed publications since 2011 reflect his expertise in bibliometric analysis, webometrics, and collaborative research dynamics.
Dr. Mark Greenwood is a Research Associate at the School of Computer Science, University of Sheffield. His work focuses on Natural Language Processing (NLP), Information Extraction, and Semantic Technologies. He has contributed to projects like the GATE framework, developing tools for text processing and social media analysis. His research addresses real-time semantic annotation, online abuse detection in political discourse, and healthcare data mining. Notable collaborations include work with the National Archives and medical institutions to improve text analysis in clinical records. Greenwood has authored over 30 peer-reviewed publications, with recent focuses on social media analytics and genomic data integration. His expertise spans dependency parsing, temporal expression extraction, and cross-media knowledge systems. Education details are not explicitly stated, but his academic trajectory includes significant contributions to computational linguistics and NLP since the early 2000s. Research interests are anchored in practical NLP applications such as question-answering systems, genic interaction extraction, and media influence quantification. He has led teams on EU-funded projects and advised on large-scale semantic search initiatives like Khresmoi. Current work emphasizes partisanship analysis in digital spaces and automated text pattern recognition.
Michael Gruninger is a Professor in the Department of Mechanical and Industrial Engineering at the University of Toronto, serving as Associate Chair of Undergraduate Studies. He holds a PhD and MSc in Computer Science from the University of Toronto and a BSc in Computer Science from the University of Alberta. His research focuses on semantic integration, process modeling, and mathematical logic applications in manufacturing and enterprise engineering. He contributed to the ISO 18629 standard for Process Specification Language. Research interests include ontologies, semantic web technologies, knowledge representation, and formal methods. He leads the Semantic Technologies Laboratory, advancing theories in mereotopology, spatiotemporal ontologies, and ontology engineering. Recent work emphasizes automated spatial reasoning in robotics and standards-based ontology development. Publications span ontology validation, mereological foundations, and applied semantic technologies. His work bridges theoretical computer science with practical enterprise systems and smart city applications. No awards are explicitly listed, though his contributions to ISO standards reflect industry impact. Advising and grants: No specific students/grants detailed here. His lab focuses on semantic technologies with applications in manufacturing and urban systems. Collaborations include NIST and the Industrial Ontologies Foundry.
Xuanjun (Jason) Gong is an Assistant Professor in the Department of Communication and Journalism at Texas A&M University. His research program bridges computational modeling, media psychology, and neuroscience to investigate sequential media selection, information diffusion, and neural correlates of media engagement. He employs diverse methodologies including fMRI, behavioral experiments, and large-scale social media analysis. Gong's research focuses on the computational mechanisms of media choice, exploring how curiosity, mood, and cognitive processes shape decisions in dynamic media environments. His work formalizes theories like mood management and flow states using drift-diffusion models and network neuroscience approaches. Key themes include prediction of media behaviors, cross-platform information spread, and neurocognitive foundations of audience engagement. His publications demonstrate consistent emphasis on developing integrative frameworks that combine communication theory with computational rigor. Recent work advances understanding of time-dependent media selection, neural dynamics during flow experiences, and social media discourse patterns during exogenous events. Gong's research has been recognized with a Graduate Student Award (CNS21). Awards & Honors: Graduate Student Award, CNS21 (2021)
Dean Rehberger is an Associate Professor in the Department of History at Michigan State University (MSU) and Director of MATRIX, MSU's digital humanities center. His research focuses on developing digital technologies for research and teaching, including semantic web, big data, digital libraries, and geospatial knowledge graphs. He leads interdisciplinary projects such as the KnowWhereGraph and the Enslaved.org hub knowledge graph, emphasizing ethical sustainability and disaster risk management frameworks. Education details are not explicitly stated in the provided text. Research interests include digital history, public history, and the application of computational methods to cultural heritage and emergency management. His work bridges geospatial analysis, ontology engineering, and humanities applications through projects like HIP Ontology and the S2 Discrete Global Grid System. Recent articles highlight advancements in knowledge graph architectures, geospatial data integration, and ethical frameworks for digital projects. Notable projects include the $1.5M Mellon Grant for the Enslaved.org database and collaboration with GLAM institutions (galleries, libraries, archives, museums). Grants: Mellon Foundation Grant (2023) for slave trade database Labs/Teams: MATRIX (digital humanities center at MSU)
Prof. Dieter De Witte serves as a Professor at Ghent University, dedicating 50% of his time to the Internet Technology and Data Science Lab (IDLab) while simultaneously contributing 50% to the Royal Museums of Fine Arts Belgium (RMFAB) in Brussels through a FED-tWIN mandate from Belspo. At RMFAB, he spearheads the strategic overhaul of digital infrastructure toward FAIR-compliant and data-driven systems, while at Ghent University he collaborates with Prof. Steven Verstockt on applied AI projects across heritage, mental healthcare, and education domains. His academic foundation includes a Master's in Engineering Physics from Ghent University (2008) followed by doctoral research on Big Data technologies and FAIR data for life sciences. Prior to returning to academia in 2021, he gained industry experience as an AI consultant and team lead at Telenet and Ordina. De Witte's research centers on AI-driven transformation of cultural heritage through FAIR data publication , collection enrichment , and intuitive querying interfaces . His technical expertise spans multimodal algorithms, image segmentation, pose estimation, large language models (LLMs), and semantic technologies including SPARQL and IIIF. Current projects focus on human-in-the-loop AI systems that combine diverse AI building blocks for practical heritage applications. Analysis of his 15 most recent publications reveals a clear trajectory from early bioinformatics work (2007-2018) on genomic motif discovery and life sciences data infrastructure toward contemporary cultural heritage applications (2023-2024). Recent outputs demonstrate innovative fusion of pose estimation, linked data frameworks, and multimodal AI for museum contexts, highlighting increasing specialization in AI enrichment of digital collections while maintaining core expertise in FAIR data principles. His FED-tWIN grant enables critical knowledge transfer between academic research and cultural heritage institutions, supporting development of next-generation digital infrastructure at RMFAB. Current projects involve creating AI tools for intuitive collection exploration and systematic enrichment of heritage assets through advanced computational methods. De Witte operates within Ghent University's Internet Technology and Data Science Lab (IDLab), participating in interdisciplinary teams developing applied AI solutions. His work bridges technical innovation with practical implementation in cultural institutions, focusing on sustainable, interoperable systems that enhance public access to digital heritage collections through cutting-edge AI interaction paradigms.
Annette ten Teije is a Full Professor at Vrije Universiteit Amsterdam (VU Amsterdam) with appointments in the Faculty of Science, Artificial Intelligence department, the Network Institute, and the Knowledge Representation and Reasoning research group. Her academic career spans several decades with a strong focus on the intersection of artificial intelligence and healthcare applications. Professor ten Teije's research interests center around Knowledge Representation, particularly in medical contexts. Her work bridges multiple domains including Semantic Web technologies, Ontology development, Neuro-Symbolic AI systems, and Clinical Decision Support. She has made significant contributions to the formalization of clinical guidelines, handling multimorbidity in healthcare systems, and developing design patterns for hybrid AI systems. Her research integrates machine learning with symbolic reasoning to create explainable and reliable AI systems for healthcare applications. Analysis of Professor ten Teije's recent publications reveals a strong trajectory toward neuro-symbolic AI approaches that combine the strengths of neural networks and symbolic reasoning. Her work increasingly focuses on explainability in medical AI systems, with numerous publications on feature selection, interaction detection, and narrative-based understanding. She has developed frameworks for shared understanding in multi-agent systems and created design patterns specifically for medical decision-making contexts. Her research consistently bridges theoretical AI advances with practical healthcare applications. Professor ten Teije has supervised 5 PhD theses as indicated in her academic profile and teaches courses including "AI in Health" and "Machine Learning and Reasoning for Health" for the 2024-2025 academic year. Her academic contributions extend to editorial work, including serving as editor for conference proceedings and special issues on Knowledge Representation for Healthcare Processes.
