Katarzyna Rycerz is an academic researcher specializing in quantum computing, high-performance computing (HPC), and optimization algorithms. Her work spans hybrid quantum-classical systems, complex network analysis, and multiscale simulations. Collaborating with institutions such as AGH University of Science and Technology (inferred via co-authors like Marian Bubak), her research focuses on advancing quantum algorithms for optimization problems, quantum annealing applications, and software frameworks for distributed computing environments. Her notable contributions include developing libraries like QHyper for hybrid quantum-classical optimization, analyzing quantum walk-based image segmentation, and exploring the application of quantum computing to classical problems like the Traveling Salesman Problem. She has also contributed to foundational studies in quantum game theory and functional programming paradigms in HPC. Rycerz's publications reflect a sustained focus on interdisciplinary research, bridging quantum mechanics, computer science, and applied mathematics. Her work often addresses practical challenges in computational efficiency, algorithm design, and scalable simulation frameworks for complex systems.
Michael Koch is a Professor of Human-Computer Interaction (HCI) at the University of Bundeswehr Munich in Germany. He holds a doctorate (PhD) and Habilitation in Informatics from TU München. His research focuses on collaboration technologies, socio-technical systems design, and integrating user interface innovations for team and community support. He leads the Cooperation Systems Center Munich (CSCM) and chairs the HCI Special Interest Group in the German Computing Society (GI). He has held industry roles at Xerox Research Centre Europe and academic positions at TU München, University of Dortmund, and University of Bremen. Education: PhD in Informatics, TU München (1997) Habilitation in Informatics, TU München (2003) Research Interests: Designing usable systems for work and leisure contexts Computer-Supported Cooperative Work (CSCW) Socio-technical system integration for team collaboration Human-Computer Interaction innovations Professional Roles: Fellow of the German Computing Society (GI) Member of ACM SIGCHI Chapter and EUSSET boards Editorial roles in i-com Journal and Lecture Notes in Informatics Advising & Collaboration: Guided over 10 PhD/Master’s students Active in European cyber defense research through Forschungszentrum Cyper Operations His research bridges technical innovations with human-centric design principles, emphasizing real-world applicability in enterprise and community settings.
Ran Wei is a researcher at the University of Science and Technology of China, Department of Chemistry, with extensive contributions across interdisciplinary domains. His work bridges Computer Science , Biomedical Imaging , and Transportation Systems , focusing on active inference models, digital twins, and multimodal learning. Academic Affiliation : University of Science and Technology of China, Department of Chemistry Research Themes : Ran Wei's research explores active inference for adaptive systems, multimodal fusion in computer vision, and domain-specific knowledge graphs for engineering applications. He also investigates federated learning on heterogeneous networks and automated safety analysis for cyber-physical systems. Recent Article Trends : His publications from 2025–2021 span AI-driven engineering (e.g., construction project management), biomedical imaging (lesion detection, radiomics), and transportation systems (driver behavior modeling). Methodologically, he integrates transformers , Bayesian inference , and active learning frameworks. Scientific Collaborations : Ran Wei collaborates with experts in autonomous vehicles (Alfredo García, Anthony D. McDonald), biomedical engineering (Qiaolin Ye, Yifan Cai), and safety-critical systems (Tim Kelly, Simon Foster).
Dr. Stephan Thober is a Group Leader of the HydroScientific Software Development (HSD) group at the Helmholtz Centre for Environmental Research - UFZ in Leipzig. He holds a PhD in Geosciences from Friedrich Schiller University of Jena and a Diploma in Mathematics from the University of Greifswald. His work focuses on advancing computational hydrosystems, particularly through the development and maintenance of the mesoscale Hydrologic Model (mHM). He is also a visiting scientist at the European Centre for Medium-Range Weather Forecasts (ECMWF) and leads the DestinE Climate Adaptation Digital Twin project. His research interests include hydrologic process representation, multiscale parameter regionalization, and climate change impacts on water resources. Thober has led projects such as HOKLIM, investigating high-resolution climate projections for Europe, and contributed to the Ulysses project for global hydrological forecasting. His expertise spans statistical downscaling, land-surface modeling, and drought analysis. He has secured over €4 million in funding for initiatives like calibration of ECLand models and hydrological seasonal prediction services. His work emphasizes interdisciplinary applications, integrating environmental informatics and climate adaptation strategies. Key achievements include developing drought indices for future climates, improving reservoir modeling techniques, and advancing metadata practices for simulation workflows. His team’s contributions to tools like HydroRiver and the mHM model have enhanced global hydrological understanding and climate resilience planning.
Jean-Marc Jézéquel is a prominent professor and researcher at IRISA (Institut de Recherche en Informatique et Systèmes Aléatoires) in Rennes, France, a joint research unit of INRIA, CNRS, and the University of Rennes. His extensive publication record spanning over two decades demonstrates his significant contributions to software engineering, particularly in model-driven engineering and software product lines. His research expertise encompasses several critical areas in contemporary software engineering. Jézéquel has pioneered work on managing uncertainty in model-based development, developing frameworks like DataTime for temporal reasoning in models. He has made substantial contributions to deep variability management across multiple layers of software systems, investigating how compile-time and run-time configuration options interact. His recent work increasingly integrates machine learning techniques with traditional software engineering approaches, creating innovative hybrid methodologies for software development and analysis. Jézéquel's publication trends reveal a clear evolution in his research focus, moving from foundational model-driven engineering concepts toward addressing uncertainty, incorporating temporal aspects into models, and leveraging machine learning for software analysis and development. His work spans both theoretical foundations and practical industrial applications, with notable collaborations including projects with Airbus on specialized software product line engineering. The interdisciplinary nature of his research connects software engineering with artificial intelligence, data science, and systems engineering. His significant contributions to the field include foundational work on model transformation, software language engineering, and variability management. Jézéquel has helped advance model-driven engineering from what he describes as 'craft' to a more systematic engineering discipline, as evidenced in his publications spanning from early 2000s to the present. As an academic leader, Jézéquel has supervised numerous PhD students and maintained extensive international collaborations. His work demonstrates a consistent commitment to bridging the gap between academic research and industrial software development practices, with applications ranging from aerospace systems to urban transportation solutions.
Dr. Sujan Koirala is a Researcher at the Max-Planck-Institute for Biogeochemistry, Department of Biogeochemical Integration (BGI). His work focuses on Model-Data Integration of carbon and water cycles, developing process-based models to quantify uncertainties in structural and observational data. Key research interests include global water and carbon budget dynamics, groundwater's role in ecosystems, and the quantification of land-atmosphere CO2 exchange. He contributes to frameworks like the SINDBAD model and ESMValTool for climate and biogeochemical modeling. His recent studies address interannual variability in ecosystem productivity, machine learning applications in hydrology, and the impacts of climate change on water limitations. Education details are not explicitly provided, but his research spans biogeochemistry, hydrology, and climate science. Collaborations involve global hydrological models and satellite data integration. He is affiliated with the Model-Data Integration research group and has published extensively on carbon cycle modeling, groundwater dynamics, and disturbance regimes in forest ecosystems.
Francisco García-Sánchez is a Professor at the University of Murcia, Faculty of Computer Science, with 20+ years of expertise in Semantic Web , Natural Language Processing , and Recommender Systems . He collaborates with institutions like Simón Bolívar University and the University of Guadalajara, focusing on ontology-driven information systems, semantic analysis of political ideology, and financial data integration. His research bridges Artificial Intelligence and Information Retrieval , particularly in Spanish-language domains. Key projects include: PolticES: Political Ideology Detection FinancES: Financial Sentiment Analysis AllergyLESS: Smart City Health Systems His recent work (2023-2024) evaluates Transformer models for disinformation detection, emotion recognition, and multilingual hope speech analysis, emphasizing linguistic features and knowledge-based systems . He contributes to platforms like UMUTeam and FD, integrating semantic technologies with social networks and financial data.
Jorge Eduardo Ibarra Esquer is an Associate Professor in the Department of Computer Science at the School of Engineering, Universidad de Sonora, Mexico. With a publication record spanning nearly two decades from 2006 to 2023, he has established himself as an active researcher in multiple domains of computer science. His work demonstrates consistent collaboration with colleagues Brenda Leticia Flores Ríos, María Angélica Astorga Vargas, and Félix Fernando González-Navarro across numerous publications. His research interests focus on the Internet of Things , Software Engineering , Machine Learning , and Educational Technology . Recent work has explored IoT object categorization, user engagement analysis on social media for scientific dissemination, and software development education during the pandemic. Earlier research examined biosensor modeling, game playing preferences, and driving safety through gamification. His publications appear in reputable venues including IEEE Access, Sensors, and the Colombian Journal of Computing, as well as at national conferences like ENC (Encuentro Nacional de Computación). Analysis of his recent publications (2020-2023) reveals a strategic focus on applying machine learning to practical problems in IoT, educational technology, and social media analytics. His work bridges theoretical computer science with real-world applications, particularly in the Mexican higher education context as evidenced by studies on Facebook engagement at a higher education institution. The interdisciplinary nature of his research spans from healthcare applications (biosensors, physiological monitoring) to educational technology and software engineering practices. While no specific scientific awards are documented in the available publication records, his consistent publication output across multiple high-quality venues demonstrates recognition within his academic community. His research has practical implications for software development education, IoT applications, and human-computer interaction design. Dr. Ibarra Esquer's work shows strong collaborative patterns, particularly with researchers from his home institution. His recent publications suggest active involvement in research teams focusing on educational technology applications during the pandemic, social media analytics for scientific communication, and IoT applications. The continuity of his research program since 2006 indicates sustained scholarly productivity and relevance in evolving technological domains.
Akito Monden is a prolific Japanese software-engineering scholar with 171 publications recorded in dblp during 1995-2025. His recent work centres on defect prediction, online learning, bandit-based tool selection, and empirical studies of software quality and human factors. Research interests span software defect prediction, mining software repositories, effort estimation, clone detection, code generation, and the application of machine-learning techniques (notably bandit algorithms and ensemble methods) to practical software-engineering tasks. He also investigates requirements ambiguity, security-bug identification with large language models, and gaze-based human-computer interaction in programming education. Across 2022-25 articles Monden explores online learning to cope with concept drift in defect datasets, multi-armed bandit algorithms for dynamic selection of clone detectors, fault-localisation techniques and code generators, and LLM-based security-bug detection. These themes reflect a sustained focus on data-driven, adaptive approaches that improve software quality assurance processes.
Shawkat K. Guirguis is an academic researcher with a focus on cybersecurity, machine learning, and IoT technologies. His work spans across intrusion detection systems, botnet prevention, and adaptive algorithms for network security. He has contributed to advancements in deep learning applications for social media analysis and real-time trajectory compression. His research often intersects with practical implementations in smart cities and healthcare authentication systems. Key areas of exploration include the use of boosting algorithms, tree-based models, and blockchain integration to enhance IoT security. His publications highlight contributions to wireless sensor networks and stock prediction models. Despite extensive research output, affiliations such as university or department remain unspecified in available records.
Dr. Hussein Mohammed is a Researcher and Head of the Visual Manuscript Analysis Lab at the University of Hamburg's Centre for the Study of Manuscript Cultures (CSMC). He leads the Cluster of Excellence 'Understanding Written Artefacts' (UWA) and the Visual Manuscript Analysis (VMA) Lab. His roles include Principal Investigator and Project Lead for initiatives like 'Similarity Measurement of Visual Patterns in Written Artefacts' and 'Pattern Recognition in 2D Data from Digitized Images.' He holds a doctoral degree (Dr. rer. nat.) in computer science from Hamburg University, focusing on computational analysis of handwriting styles. His research emphasizes pattern recognition, machine learning, and computer vision applied to historical manuscripts. Key projects include developing tools like the Pattern Analysis Software Tools (PAST), AFAT, and HAT to analyze visual features and aid cultural heritage preservation. His work bridges computer science and humanities, with contributions to palimpsest deciphering via generative AI, artifact feature analysis, and multimodal data integration. Notable awards include the Erasmus Mundus Scholarship (2012). He also contributes to academic outreach through lectures on computational paleography and manuscript digitization.
Alexander Nutz is a Researcher at the University of Freiburg's Department of Computer Science, affiliated with the Software Modeling and Verification Group. His primary research focuses on software model checking, satisfiability modulo theories (SMT), and Craig interpolation. He contributes to the development of verification tools such as SMTInterpol and the Ultimate Program Analysis Framework. Nutz has held teaching roles in courses like Automata Theory, Decision Procedures, and Program Analysis since 2012, collaborating extensively with colleagues on seminar leadership and course assistance. Education: PhD in Computer Science from the University of Freiburg (2019), focusing on 'Data Flow in Program Verification.' Professional activities include jury membership in the SV-COMP competition (2014-2016, 2018) and contributions to the AVACS research project. His work emphasizes program analysis, verification frameworks, and automated reasoning techniques. Research interests span formal methods, program analysis, and the application of SMT solving to real-world systems like smart contracts. Nutz's projects include enhancing verification tools for memory safety checks and integrating data flow graphs into verification processes. Key contributions: Development of Ultimate Kojak and Automizer tools, exploration of map abstraction techniques, and advancements in interpolation-based verification methods. His publications address challenges in non-linear arithmetic verification and automated reasoning for complex software systems.
Heinrich Hußmann is a Professor at Ludwig Maximilian University of Munich with an extensive research career spanning from 1985 to present. His work primarily focuses on Human-Computer Interaction, with significant contributions to Virtual Reality, Tangible User Interfaces, and intelligent systems. Over his career, he has published nearly 250 papers in top-tier conferences and journals. His research interests center around understanding and improving human interaction with emerging technologies. Key focus areas include cinematic virtual reality experiences, tangible interfaces for education and collaboration, privacy in mobile contexts, and user understanding of intelligent systems. His work often bridges theoretical HCI principles with practical applications that address real-world user needs. Analysis of his recent publications (2020-2023) reveals a strong emphasis on tangible interaction for educational contexts, particularly for children's learning. He has also made significant contributions to understanding social aspects of cinematic virtual reality and developing frameworks for user understanding of AI systems. His work consistently demonstrates a user-centered approach that considers psychological, social, and technical dimensions of interaction. Professor Hußmann has mentored numerous students who have become active researchers in the HCI field, including Sarah Theres Völkel, Malin Eiband, and Daniel Buschek, among others. His collaborative network spans multiple institutions and disciplines, reflecting the interdisciplinary nature of modern HCI research. His research has practical implications for the design of more intuitive, privacy-respecting, and educationally effective interactive systems. Current projects suggest continued exploration of how physical and digital elements can be integrated to create meaningful user experiences that support learning, collaboration, and mindful technology use.
J. Edward Swan II is a Professor in the Department of Computer Science and Engineering at Mississippi State University. His research focuses on Augmented Reality (AR), Virtual Reality (VR), and Human-Computer Interaction (HCI), with an emphasis on perceptual aspects of AR/VR systems, calibration techniques, and user experience optimization. He has contributed extensively to advancing AR/VR technologies through studies on depth perception, text legibility, and system accuracy. Key roles: IEEE VR Steering Committee member, conference program chair, and guest editor for top journals. Research areas include optical see-through AR systems, user performance evaluation, and spatial perception in virtual environments. His work spans over 120 publications since 1995, with recent contributions addressing font optimization for AR displays, eye-tracking-based depth measurement, and X-ray vision systems for situational awareness. Notable recognition includes the VGTC Virtual Reality Service Award (2023).
Florian Nouviale is a researcher specializing in virtual reality (VR), human-computer interaction, and collaborative systems. His work spans applications in medical training, historical reconstruction, and industrial design. He has collaborated extensively with institutions and researchers in France, focusing on projects such as the development of wheelchair simulators for rehabilitation and frameworks for no-code interactive applications. Key contributions include EvoluSon (an interactive music history platform) and Xareus (a no-code VR framework). His research emphasizes usability, user-centered design, and interdisciplinary collaboration. Research Interests: Virtual Reality Applications in Medicine and Rehabilitation Collaborative Systems and Frameworks for Interactive Environments Historical and Cultural Heritage Visualization Non-Verbal Communication and Emotional Expression in VR Publications: Focus on VR frameworks, medical simulators, and interactive systems, with a trend toward user-centered design and accessibility. Recent work explores emotional expression through body postures and no-code development tools.
