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Katsiri EleftheriaView profile

Assistant Professor

• Diffuse Computing
• Sensor Networks
• Middleware
• +6 more

Eleftheria Katsiri is an Assistant Professor at the Department of Electrical and Computer Engineering, Democritus University of Thrace, specializing in Operating Systems and Internet of Things (IoT) technologies. She joined the university in December 2013 and has been an Associate Faculty Member at the Athena Research Center since 2014. University of Patras, B.Sc. in Computer and Informatics Engineering (1998) University of Cambridge, PhD in Computing (2005) Her research focuses on real-time data processing, sensor network simulation, and middleware development for IoT systems. She has contributed to environmental monitoring, healthcare informatics, and decentralized water management solutions. Notable scientific awards include the Domestic Studentship, CASE, and Pressed Steel Award scholarships during her PhD. She has coordinated research projects like ALPINE and Hydropolis and participated in eight international research initiatives. Coordinated ALPINE project (2013-2015), showcased at Thessaloniki International Fair 2016 Worked on EU-funded SPICE, iWidget, ICENI II, UbiMon, and Greek projects RFCure, Greenactions, CAP Contributed to 11 EU evaluation committees (46 proposals assessed) Developed 13 hardware/software demonstration systems (sensor networks, IoT apps, simulation tools)

• Information Retrieval
• Machine Learning
• Data Mining
• +5 more

Professor Klaus Berberich is a faculty member at htw saar (Saarland University of Applied Sciences), where he serves as Professor in the Databases & Information Systems department within the Faculty of Engineering. He is the Laboratory manager of the software laboratory (SWL) and Chairman of the examination boards for Practical Computer Science, Communication Informatics and Production Informatics. His research focuses on Information Retrieval, Machine Learning, Data Mining, and Web Archives, with significant contributions to knowledge graphs, temporal information retrieval, and neural information retrieval models. Professor Berberich has developed innovative approaches for quantity extraction from web tables, structuring text into tables, and knowledge graph querying. His publication record shows a consistent trend toward increasingly sophisticated neural approaches to information retrieval, evolving from traditional temporal search techniques to modern deep learning models. Recent work demonstrates strong integration of knowledge graphs with neural information retrieval systems, particularly in handling quantities and temporal aspects of information. Professor Berberich has received numerous prestigious awards throughout his career: 2020: Test of Time Award, ECIR 2020 2018: Honorable Mention for Best Poster Award, WWW 2018 2017: Prominent Paper Award, Artificial Intelligence Journal 2014: Highly Commended Poster Presentation Award, IIiX 2014 2013: Honorable Mention for Best Paper Award, CIKM 2013 2011: Best Demo Award, WWW 2011 2009: Best Late-Breaking Result Award, WSDM 2009 As an active researcher and educator, Professor Berberich serves on numerous program committees for major conferences including WSDM, CIKM, SIGIR, and ICTIR. He has been a consistent reviewer for prestigious journals in the field and is a member of the executive committee of the Information Retrieval specialist group of the German Informatics Society. His teaching portfolio includes courses in Databases, Information Retrieval, Data Science, Machine Learning, and Deep Learning. Professor Berberich leads the software laboratory (SWL) at htw saar and has been instrumental in developing research infrastructure for knowledge-centric tasks, including the GYANI indexing infrastructure. His research group has made significant contributions to temporal information retrieval, particularly in the context of web archives and news archives.

Michael DohertyView profile

Associate Professor

• Database Applications
• Knowledge Management Systems
• Computer Graphics
• +3 more

Michael Doherty is an Associate Professor in the Department of Computer Science at Pacific University's School of Engineering and Computer Science. His research and teaching focus on Database Applications , Knowledge Management Systems , Computer Graphics , Simulation , Video Game Development , and Software Engineering . Ph.D. in Computer Science from University of Colorado at Boulder (1998) M.S. in Computer Science from University of Rhode Island (1992) B.S. in Electrical Engineering from University of Florida (1983) His publications center on database systems , knowledge management , and software engineering applications in technology transfer , telerobotics , and telecommunications . Key topics include ontologies , graphical transformation languages , and object-oriented database updates .

• Communication Design
• Education Design
• Multimedia Design
• +4 more

António Carvalho Maneira is a Professor at Universidade Europeia , Universidade Lusíada , and previously taught at Universidade da Beira Interior (UBI) . His expertise lies in Digital Media Literacy , Educational Technology , and Communication Design . He holds a PhD in Digital Media (2014) and a Master’s in Communication Sciences from Universidade Nova de Lisboa (UNL) . Affiliations: UNIDCOM, Khan Academy, Plano Nacional de Leitura Projects: Learning and Teaching Mathematics with Khan Academy , PICCLE (Plano de Intervenção Cidadãos Competentes em Leitura e Escrita) Research focuses on Interactive Videos , Inclusive Design , and E-Learning Platforms . He has supervised multiple Master’s students and is currently co-supervising a PhD candidate on pandemic-era communication design. His collaborative projects with Educom , Fundação Altice , and Direção-Geral de Educação have impacted over 12,000 students.

Pradip K SrimaniView profile

Professor

• Parallel and Distributed Computing
• Interconnection Networks
• Self-Stabilizing Systems
• +2 more

Pradip K Srimani is a Professor at the School of Computing , Clemson University , with research focus on Parallel and Distributed Computing , Self-Stabilizing Systems , and Graph Theory Applications . He is an IEEE Life Fellow and ACM Distinguished Scientist with over 250 publications. Research Interests Self-stabilizing algorithms for network graphs Ontology-based biomedical information retrieval High-performance computing resource management Biologically inspired distributed algorithms Network topology computation Recent publications demonstrate expertise in token circulation protocols, genome assembly frameworks, and semantic similarity measurement. He has served on program committees for APDCM , GPC-2018 , and IEEE BigMM conferences, while maintaining editorial roles at International Journal of High Performance Computing and Journal of Big Data . Scientific Awards IEEE Life Fellow ACM Distinguished Scientist

Li QinView profile

Professor

• Social Media and Commerce
• Adoption of Information Technology
• Information Security and Privacy
• +3 more

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

• Machine Learning
• Quantum Computing
• Natural Language Processing
• +3 more

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.

• Information Retrieval
• Machine Learning
• Evaluation
• +4 more

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 GrossView profile

Professor

• Artificial Intelligence
• Machine Learning
• Computer Graphics
• +8 more

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 TorlakovaView profile

Associate Professor

• Arabic Linguistics
• Phraseology
• Idioms Analysis
• +3 more

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. BjålieView profile

Professor

• Neuroinformatics
• Brain Architecture
• Digital Brain Atlases
• +7 more

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.

Giovanni SilenoView profile

Lecturer

• Artificial Intelligence
• Logic Programming
• Knowledge Representation
• +5 more

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.

• Statistical Cybermetrics
• Operational Research
• Mathematical Modeling
• +5 more

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.

• Natural Language Processing
• Information Extraction
• Social Media Analysis
• +4 more

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 GruningerView profile

Professor

• Ontologies
• Semantic Integration
• Process Modelling
• +6 more

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.