Martin Giese is affiliated with the University of Oslo (Department of Informatics) and the University Clinic Tübingen (Department of Cognitive Neurology). He is a researcher with a focus on semantic technologies, ontology-based data access, and visual query systems. Research Themes : Semantic Web, Ontology Engineering, Knowledge Graphs, Geological Informatics, Probabilistic Logic, Automated Reasoning Key Collaborations : Siemens, Statoil, Norwegian Petroleum Directorate, and various European research institutions Technical Contributions : Developed visual query systems (OptiqueVQS), ontology-driven geological modeling (GeoFault), and semantic data integration frameworks for industrial applications. His work spans both theoretical logic and practical implementations in big data environments. Publications : Recent articles focus on fault ontologies, process representation, and semantic embeddings. Earlier work includes foundational research in automated theorem proving and UML formalization.
Sarah D Castle serves as Assistant Professor of Mathematics Education at the University of Idaho with dual appointments in the Department of Mathematical and Statistical Sciences (College of Science) and Curriculum and Instruction Department (College of Education, Health and Human Sciences). Her research bridges computational methods and mathematics education to advance equity and foster creativity in undergraduate STEM contexts. Her educational background includes: B.A. in Mathematics, Whitworth University (2012-2016) B.S. in Engineering Physics, Whitworth University (2012-2016) M.S. in Mathematics, Michigan State University (2019-2022) Ph.D. in Mathematics Education, Michigan State University (2018-2023) Castle's research centers on computational approaches to uncover systemic inequities in STEM education while creating environments that nurture mathematical creativity, particularly for marginalized students. She investigates how computational modeling—such as Jupyter Notebook implementations in linear algebra—can transform mathematical engagement and understanding. Her work critically examines power structures in educational spaces and develops pedagogical frameworks that leverage coding to deepen conceptual learning and promote equitable participation. Analysis of her 13 publications (2020-2024) reveals three dominant trends: (1) computational tools as catalysts for mathematical creativity and conjecture, (2) quantitative critical analyses of systemic advantages in STEM courses, and (3) investigations of student agency development in proof-based mathematics. These works span ACM computer science education conferences, mathematics education research forums, and interdisciplinary learning sciences venues. Castle actively contributes to major research initiatives: SEISMIC Project: Multi-institutional analysis of equity in introductory STEM courses Transition to Proof Project: Studying sense-making in advanced mathematics Computational Education Research Lab: Developing computational modules for mathematics learning Her teaching portfolio includes Secondary Mathematics Methods (EDCI 434), Proof and Viable Argumentation (MTHE 410), and Calculus I (MTH 132), reflecting her dual expertise. She has extensive experience as instructor of record, teaching assistant, and developer of STEM curricula including high-altitude balloon launch modules for K-12 students.
Dr Nicole Vickery is a Lecturer in Visual Communication at the School of Design, Faculty of Creative Industries, Education & Social Justice, Queensland University of Technology (QUT). Her work focuses on designing playful experiences to enhance health and wellbeing, particularly through tangible technologies for children and intergenerational interaction. She received her PhD from QUT in 2019, examining how videogames facilitate cognitive flow, and subsequently worked as a Post-Doctoral Research Fellow on the project ‘Enabling Children’s Active Play using Novel Technology’. Her research explores the intersection of playful design, child-computer interaction, and cultural studies of play. Key interests include: Developing tangible interfaces for active play in children Designing intergenerational digital experiences Using games to foster empathy and social connection Ecological dimensions of play through ‘more-than-human’ frameworks Vickery's publications (2019-2025) demonstrate a consistent focus on embodied interaction, game design, and child development. Recent work emphasizes tangible technologies for nature play, neurodiversity-inclusive games, and pandemic-era digital play. Methodologically, she employs scoping reviews, case studies, and experimental prototypes to advance design justice and participatory approaches. She leads significant grants including the Australian Research Council Discovery Project DP240102717 (‘Designing Distanced Intergenerational Interaction with Tangible Technology’) and contributes to the 2024 Alastair Swayn Design Strategy Grant (‘Making Memories Visible: Design for Meaningful Engagement in Residential Aged Care’).
Supratim Biswas is a Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Bombay, where he has served since 1995. His academic career spans over four decades, beginning as a Lecturer in the Computer Center in 1980, progressing to Assistant Professor in 1985, Associate Professor in 1990, and achieving full Professorship in 1995. He has held significant administrative roles including Dean of Academic Programs (2007-2010), Head of CSE Department (2000-2003), and Director of IITB-Monash Academy (2009-2010). His research interests focus on Programming Languages, Compiler Optimization, Parallelizing Compilers, Parallel and Distributed computing, and Combinatorial Optimization . Professor Biswas has made substantial contributions to compiler technology, particularly in parallelization techniques for modern architectures. His work bridges theoretical compiler design with practical applications in high-performance computing and CAD systems, demonstrating how compiler optimizations can significantly enhance computational efficiency in real-world applications. The publication record shows a consistent research trajectory spanning nearly four decades, with recent work (2012-2015) focusing on GPU-based parallel algorithms, loop parallelization techniques for non-uniform data dependencies, and mesh processing for CAD applications. His research demonstrates evolution from foundational compiler theory to contemporary parallel architectures, maintaining relevance through practical applications in computational geometry, CAD systems, and high-performance computing. Excellence in Teaching Award (2000) Professor Biswas has supervised over 60 doctoral and master's students, establishing himself as a dedicated mentor in systems software education. His sponsored research portfolio includes significant projects with CDAC (350 lacs), MIT (133 lacs), TCS (81.3 lacs), and Intel Corporation (10 lacs), demonstrating strong industry-academic collaboration. His teaching portfolio spans both undergraduate and postgraduate levels, including foundational courses like Discrete Structures and advanced topics like Parallelizing Compilers, reflecting his commitment to curriculum development across multiple generations of computer science education. His laboratory work has supported students across B.Tech, M.Tech, and Ph.D. programs, with particular emphasis on compiler construction and operating systems. Through the Continuing Education Program, he has extended his expertise to industry professionals, conducting numerous specialized courses for organizations including VSNL, TCS, DRDO, and Reliance.
Prof. Dr. Christina Drüke-Noe holds a W3 professorship in mathematics education at the University of Education Weingarten since 2018, where she also leads the Internship Office and serves as a contact for secondary school matters. Previously, she was a Junior Professor (2014-2018) at the same institution and worked as a scientific collaborator at the University of Kassel. Her current consultation hours are scheduled for summer semester 2025. Her academic journey includes: Abitur at Oberstufengymnasium Oberzwehren (1986) Surveying technician training (1986-1989) Mathematics and English studies at University of Kassel (1st State Examination, 1989-1996) Studies at University of Surrey, UK (1990-1991) Foreign Language Assistant in UK (1993-1994) 2nd State Examination at Christian-Rauch-Schule (1997-1999) Dr. rer. nat. in Mathematics and Natural Sciences (University of Kassel, 2013) Her research focuses on mathematics education innovation, specializing in: Competency-based task design and analysis Development of standardized assessments and educational standards Teacher professionalization in mathematics pedagogy Cross-national examination systems Curriculum development for secondary mathematics Implementation of spatial reasoning in geometry education Her publications demonstrate consistent focus on mathematics assessment reform, showing strong emphasis on: competency-based evaluation frameworks, international comparative studies of examination systems, teacher training methodologies, and three-dimensional geometry pedagogy. Recent works increasingly address cognitive dimensions of task design and systemic implementation of educational standards. She leads significant projects including: Analysis of European exit exam tasks (DFG-funded) Development of teacher competencies in task analysis Mathematical reasoning research (MaBeLL) Diagnostic support for vocational mathematics She coordinates nationwide assessment development for the Institute for Educational Quality Improvement (IQB). As Head of Internship Office, she oversees practical teacher training programs and advises on secondary mathematics curriculum implementation.
Diarmuid O'Donoghue is an Assistant Professor in the Faculty of Science & Engineering at Maynooth University, specializing in Computational Creativity and Analogical Reasoning . His research explores topological similarities between text and source code to develop cognitively inspired systems for problem-solving and bias detection. Co-PI of the Modelling implicit bias project (€21/FFP-P/10118) Senior Scientific Coordinator for the €2.6M EU-funded Dr Inventor project Key research areas include: Latent homomorphism detection in lexical data Comparative analysis of LLMs and analogical systems Formal specification generation from code/text His work has shaped undergraduate project frameworks with ethical GenAI integration . Publications span ICCC , GECCO , and journals like Artificial Intelligence Review .
Rolf Plötzner serves as Professor for Media Didactics within the Department of Learning with Media at Freiburg University of Education's Institute of Psychology. He maintains active consultation hours for Summer Semester 25 via Zoom, demonstrating ongoing academic engagement in teacher education programs across primary, secondary, and vocational disciplines. His research program investigates cognitive mechanisms in multimedia learning, with concentrated expertise in instructional animations , interactive videos , and 3D visualizations for understanding technical system changes. Key theoretical frameworks include cognitive load theory and visuospatial working memory models, examining how interactivity features influence learning outcomes when mastering dynamic processes. Recent publications (2020-2025) reveal consistent experimental and meta-analytic approaches comparing animation efficacy against static media. Research trends emphasize change-specific learning demands , interaction feature optimization , and cognitive predictor variables , with emerging exploration of AI applications in educational media design. Professor Plötzner supervises Bachelor's and Master's theses requiring rigorous theoretical grounding in educational psychology. His supervision spans analytical literature reviews, empirical classroom studies, and development-oriented media projects across teacher education programs. Students must submit preliminary research questions with theoretical frameworks and methodological plans, focusing on digital media applications for programming education, cooperative learning, and technical system comprehension.
Prof. Dr. Eva-Kristina Franz serves as Professor of Primary School Research and Primary Education at the University of Trier since September 2021. She is affiliated with the Department of Educational Sciences IV within the Faculty of Humanities. Currently on sabbatical during the summer semester, she maintains her research and academic activities through Stud.IP office hours. Her academic journey includes: Undergraduate studies for teaching at special schools at Heidelberg University of Education (1999-2003) First State Examination for Teaching at Special Schools (2003) Second State Examination for Teaching at Special Schools (2005) Doctorate (Dr. phil.) from Karlsruhe University of Education (2011) Professor Franz's research centers on teacher education and adaptive teaching in heterogeneous primary school settings. Her work bridges theoretical frameworks with practical classroom applications, particularly examining how teachers develop competencies to address diverse learning needs. She investigates content and linguistic adaptability in social science education, with growing emphasis on historical consciousness, democracy education, and sustainability. Her approach integrates learning workshop methodologies as spaces for developing adaptive teaching skills. Her recent publications reveal evolving research trajectories toward historical thinking assessment, climate education through picture books, and democratic participation in learning environments. This reflects a strategic expansion from foundational work on inclusive teaching to address contemporary educational challenges including sustainability and democratic citizenship. Professor Franz actively supervises research projects including HiPepro (Professionalizing teacher training students for historical perspective in general education) and has secured funding for collaborative international learning workshop initiatives. She has organized significant conferences including the 16th International Conference of University Learning Workshops focused on Democracy and Participation. As director of learning workshop activities at University of Trier, she leads innovative approaches connecting university teacher education with school practice through the "Lebendige Moselweinberge" (Living Moselle Vineyards) project and other community-based learning initiatives that serve as laboratories for developing adaptive teaching competencies.
Professor Klaus-Dieter Althoff is a faculty member at the University of Hildesheim, working within the Intelligent Information Systems Division of the Mathematics, Natural Sciences, Economics & Computer Science school. His research focuses on applying artificial intelligence techniques, particularly case-based reasoning (CBR), to architectural design support systems and knowledge management applications. His research interests center on Case-Based Reasoning methodologies , AI-assisted architectural design , semantic building information modeling , and knowledge representation systems . Althoff's work bridges computer science with practical applications in architecture, developing systems that automate and enhance early-stage design processes through machine learning and case-based approaches. Analysis of his recent publications (2021-2024) reveals a strong trend toward integrating deep learning with traditional case-based reasoning for architectural applications. His research increasingly focuses on autocompletion of architectural spatial configurations , BIM (Building Information Modeling) enhancement , and explainable AI for design support . The work spans theoretical CBR methodology development and practical implementations in architectural design tools. Althoff maintains active research collaborations across disciplines, with publications spanning architecture, computer science, and cybersecurity applications of case-based reasoning. His work demonstrates consistent methodological development in CBR frameworks like FLEA and SEASALT, while expanding into new application domains including network security and fitness training systems.
Dr. Taotao Cai serves as an Honorary Lecturer and Postdoctoral Research Fellow in the School of Computing at Macquarie University, bringing expertise in graph analytics and machine learning. Previously, they held an Associate Research Fellow position at Deakin University's School of Information Technology. Their academic foundation includes a Ph.D. from Deakin University and a Master's in Computer Science from Shenzhen University, China. Education: Ph.D. in Computer Science, Deakin University, Australia Master of Computer Science, Shenzhen University, P.R. China Research spans graph data processing, social network analytics, and data mining with significant contributions to causal graph neural networks, influence maximization, and privacy-preserving machine learning. Recent work demonstrates strong interdisciplinary applications in remote sensing, healthcare decision systems, and battery management, while maintaining core focus on dynamic network analysis. The research portfolio reveals increasing sophistication in causal reasoning within graph structures and practical implementations for real-world systems. Publication trends from 2023-2025 show concentrated activity in graph neural networks (40%), privacy/security (25%), and remote sensing applications (20%), with emerging work in causal classification and medical AI. This evolution reflects both theoretical advancement and practical problem-solving across diverse domains. No formal scientific awards are documented in available sources. While no formal student advising is recorded, collaborative patterns indicate active mentorship within research teams. Grant activity remains unspecified, but extensive cross-institutional collaborations (particularly with Deakin University and international partners) suggest participation in multiple funded projects. Current trajectories point toward causal graph neural networks for explainable AI and privacy-enhanced federated learning systems as primary research frontiers. Active participation in international research networks is evident through co-authorship spanning Australia, China, and global institutions, with particular strength in graph algorithm development and social network analysis communities.
Jason Armitage is a Researcher in the Department of Computational Linguistics at the University of Zurich, specializing in multimodal machine learning and embodied AI within the Language, Technology, and Accessibility team. His work bridges virtual environments, accessibility technologies, and cross-modal reasoning. His academic background includes: Cognition and Computation from University of London, Birkbeck Data Science from University of Stirling Machine Learning studies at UC San Diego Armitage's research centers on aligning visual and linguistic inputs for embodied AI systems, with current projects enhancing scientific data accessibility through cross-modal formats and applying nonlinear time series modeling to 3D scene editing. His methodology integrates multimodal fusion techniques with embodied cognition principles to develop more intuitive human-AI interactions. Publications from 2020-2025 reveal consistent advancement in multimodal learning frameworks, particularly in vision-language navigation systems and multilingual multimodal benchmarks. Key innovations include trajectory planning algorithms using feature-location cues and novel architectures for multitask learning across diverse language-modalities. Professional experience spans research at the University of Bonn and industry roles at BBC and Disney, where he developed digital video services, games, and mobile applications. He actively contributes to the Language, Technology, and Accessibility team's mission of creating inclusive technological solutions through computational linguistics.
Dr. Ismail Ilkan Ceylan is an Associate Member at the University of Oxford's Department of Computer Science, specializing in artificial intelligence and machine learning with a focus on graph-based approaches. His research spans multiple disciplines within computer science, with particular emphasis on developing methods that integrate machine learning, knowledge representation, and theoretical computer science. Dr. Ceylan's research interests center on graph machine learning, which involves developing methods to learn from relational patterns and reason over them. He explores how techniques from deep learning, graph representation learning, probabilistic methods, logical reasoning, and complexity theory can be combined to address challenging problems in relational structures. His work has applications across diverse domains including life sciences, chemical and biological systems, and social networks. His publication record shows a strong trend toward foundational work in graph neural networks, with recent papers examining theoretical properties, representation capacity, and practical applications. The research spans theoretical computer science aspects like homomorphism counts and zero-one laws, while also addressing practical challenges in knowledge graph completion, link prediction, and representation learning for specialized graph structures. Best Paper Prize for A Dichotomy for Homomorphism-Closed Queries on Probabilistic Graphs Sister Conference Best Paper Track for Open-World Probabilistic Databases: An Abridged Report Marco Cadoli-Best Student Paper Award for Open-World Probabilistic Databases Dr. Ceylan actively supervises both current and past graduate students, indicating a strong commitment to academic mentorship. His research group includes students working on various aspects of graph machine learning, knowledge representation, and probabilistic reasoning. While specific grant information isn't detailed in the provided text, his extensive publication record in top-tier conferences suggests successful funding of research activities. His work appears to be centered around advancing the theoretical foundations of graph machine learning while simultaneously developing practical applications in knowledge representation and reasoning. The collaborative nature of his publications, often with multiple co-authors from different institutions, suggests participation in a broader research network focused on the intersection of AI, machine learning, and knowledge representation.
Dr. Leendert van Maanen is an Associate Professor at the Department of Experimental Psychology, Utrecht University, where he studies cognitive mechanisms underlying information processing and decision-making. His research focuses on developing and validating computational cognitive models, with applications in human-machine interaction and cognitive neuroscience data analysis. Research Themes: Cognitive Modeling, Decision-Making, Human-Centered Artificial Intelligence, Applied Data Science Labs: Leads the COBRA (Cognitive and Behavioral Research in Artificial Intelligence) lab His work spans three levels of cognitive integration in AI: model development, human-machine deployment, and neural data analysis. Recent projects include cognitive strategy detection, digital twin user modeling, and neural event detection using multivariate time series. For collaborations and publications, see leendertvanmaanen.com or Utrecht University Research Portal.
Sören Frappart is a Lecturer at the Cognition, Languages, Language, Ergonomics (CLLE) laboratory, affiliated with the University of Toulouse - Jean Jaurès. His research focuses on cognitive development, science education, and the intersection of cultural anthropology with developmental psychology. Department: CLLE (Cognition, Languages, Language, Ergonomics) Academic Rank: Lecturer Email: soren.frappart@univ-tlse2.fr Frappart investigates how children develop understanding of counterintuitive scientific concepts like Earth's shape and gravity, while also exploring the influence of ontological plurality on scientific practice. His recent work examines lived experience in research methodology and its reflexive implications. Key article trends include analyzing children's conceptual development through interdisciplinary lenses (2024), philosophical discussions' impact on self-awareness (2023), and cross-cultural astronomy education challenges (2019). He employs comparative methods and enactive theory frameworks. Administrative roles include serving as delegate at CLLE and contributing to the CRCT (Centre de Recherche sur les Cultures et Textes) during 2023-2024. Collaborations span institutions like Atécopol (Political Ecology Workshop) and EPHE (École Pratique des Hautes Études). His 2024 work on lived experience demonstrates how researchers' embodied perspectives shape scientific activity. Earlier studies (2008-2017) focused on children's ontological reasoning, while 2023 publications address nature conceptualization in educational contexts.
Ersen Yazıcı is a Professor in the Department of Mathematics and Science Education at Aydın Adnan Menderes University's Faculty of Education. His career spans over two decades in mathematics education research, with a focus on teacher training, pedagogical strategies, and cognitive aspects of mathematical learning. Education: Bachelor's in Primary School Mathematics Teaching (2002), Selçuk University Master's in Primary School Mathematics Teaching (2004), Selçuk University PhD in Mathematics Education (2009), Selçuk University Yazıcı's research focuses on mathematics education , STEM integration , and pedagogical innovation . He has published extensively on flipped classroom models , problem-solving strategies , and cognitive development in pre-service teachers. Recent publications include studies on pattern generalization , modelling-based instruction , and gamification in mathematics education. His 2020 work on STEM product analysis and teaching strategies for intellectual disabilities demonstrates interdisciplinary and inclusive approaches. Yazıcı has participated in multiple academic activities including YÖKDİL (2017) and UDS (2009) language proficiency certifications, indicating international academic engagement.
