Jana Straková is a researcher at the Institute of Formal and Applied Linguistics (ÚFAL) within the Faculty of Mathematics and Physics at Charles University in Prague. Her work focuses on advancing natural language processing techniques for multilingual applications, particularly in deep learning architectures for linguistic analysis. Her research interests span Natural Language Processing , Deep Learning , and Multilingual Text Processing with specialization in named entity recognition, dependency parsing, and morphological analysis. She has pioneered neural network approaches for tasks like diacritics restoration and grammar error correction, with particular emphasis on Czech language processing while extending methodologies to Latin and other languages. Analysis of her publication record from 2019-2025 reveals consistent innovation in NLP tool development, particularly the NameTag and UDPipe systems. Her work demonstrates strong integration of contextual embeddings with linguistic resources, showing increasing focus on cross-lingual transfer and historical language processing in recent years. As part of the ÚFAL research group, she collaborates extensively with Milan Straka and Jan Hajič on developing open-source NLP pipelines. Her projects include creating large-scale datasets like CWRCzech for web relevance ranking and advancing ontology engineering through LLM-augmented approaches.
Mathieu Delalandre is an Associate Professor at LIFAT Laboratory, University of Tours, France, specializing in image processing and document/video analysis since 2009. His research focuses on local approaches including local detectors, template matching, and transform domain processing, with applications in video copy detection, scene text detection, and document image networking. He received his PhD in Computer Science from Rouen University in 2005, followed by research fellowships at SCSIT (Nottingham, UK), L3i (La Rochelle, France), and CVC (Barcelona, Spain) from 2006-2009. His educational background established his foundation in document analysis and pattern recognition. Delalandre's research spans multiple domains of computer vision with emphasis on practical applications. His work bridges theoretical image processing techniques with real-world problems in document analysis and video content monitoring. Key contributions include robust symbol localization, performance evaluation frameworks for symbol recognition systems, and real-time text detection algorithms. His approach often combines structural and statistical methods for enhanced accuracy. His publication record demonstrates consistent focus on video and document analysis, with recent work emphasizing partial video copy detection (2021-2023), real-time text detection (2019-2021), and JPEG document processing (2015-2017). The trajectory shows evolution from fundamental symbol recognition research to applied multimedia analysis systems addressing contemporary challenges in copyright protection and content monitoring. As head of the StationTV project, Delalandre leads research on real-time processing of multi-channel television content. He has participated in numerous national and international research projects, contributing to advancements in document image analysis and video processing. His work has been supported through various research grants focusing on practical applications of computer vision. Delalandre is an active member of the RFAI research group at LIFAT Laboratory, where he contributes to the development of innovative image processing techniques. His team collaborates across European institutions, maintaining strong connections with previous research sites in the UK, Spain, and other French laboratories. Current work focuses on multimedia fact-checking systems and real-time video analysis solutions.
Dr. Alice Towler is a Casual Academic at the School of Psychology within the University of New South Wales (UNSW). Her research focuses on improving face identification accuracy and efficiency in forensic settings, including the study of super-recognition abilities, development of evidence-based facial comparison methods, and optimization of human-software collaboration in identity verification. Her primary research interests include: Forensic face identification techniques and error reduction Predictors of superior face recognition ability (super-recognition) Development of evidence-based training protocols for facial image comparison Integration of human expertise with facial recognition software systems Identity fraud detection in critical applications like passport verification Dr. Towler collaborates extensively with the UNSW Forensic Psychology Lab under Dr. David White and Prof. Richard Kemp. She maintains an active partnership with the Australian Passport Office to enhance identity fraud detection capabilities. As a member of the Evidence-Based Forensics Initiative, she advocates for scientifically validated practices in forensic sciences through interdisciplinary collaboration between legal professionals, forensic scientists, and cognitive researchers. Her recent publications demonstrate consistent focus on face recognition mechanisms, perceptual expertise development, and forensic applications. Research themes include optimization of training protocols, individual differences in recognition abilities, validation of forensic methodologies, and real-world implementation challenges.
Wesley Klewerton Guez Assuncao serves as an Associate Professor in the Department of Computer Science within the College of Engineering at North Carolina State University. Previously, he held positions as a University Assistant/Senior Researcher at Johannes Kepler University Linz in Austria, Postdoctoral Researcher at Pontifical Catholic University of Rio de Janeiro in Brazil, and Assistant/Associate Professor at Federal University of Technology - Paraná in Brazil. His academic journey includes a Ph.D. in Computer Science from the Federal University of Paraná with a visiting period at Johannes Kepler University. Dr. Assuncao's research spans several critical areas in modern software engineering. His primary interests include Software Modernization (reverse engineering, re-engineering, and migration), Variability Management (variability mechanisms, software customization, and software reuse), and Software Quality (technical debt, code smells, and software refactoring). He also investigates Model-Driven Engineering (model inconsistency detection, repair generation, and change propagation), Collaboration in Systems Engineering (change synchronization, tool flexibility, and conflict awareness), Software Testing (regression testing, integration testing, and test case selection/prioritization), and AI4SE (Generative AI, Machine Learning, and Evolutionary Algorithms for Software Engineering). His recent publications reveal a strong focus on software modernization challenges, particularly regarding legacy systems transformation, microservices architecture, and the application of AI techniques in software engineering. The research shows increasing integration of Large Language Models in addressing software evolution problems, with emphasis on empirical validation through industrial collaborations. His work consistently bridges theoretical foundations with practical applications, as evidenced by multiple industry partnerships and real-world case studies. Dr. Assuncao has received numerous prestigious awards including Distinguished Reviewer Awards from FSE and SANER conferences, a Young Researcher Award from Johannes Kepler University, and multiple Best Paper Awards from top software engineering conferences. His research has been recognized with ACM SIGSOFT Distinguished Paper Awards and IEEE Computer Society TCSE Distinguished Paper Awards. In terms of academic service, he serves as Co-editor of the In Practice track at the Journal of Systems and Software and has held various leadership roles in major conferences including ICSE, SANER, MSR, and SPLC. He has successfully secured substantial research funding totaling approximately USD 1.91 million from sources including the Austrian Science Fund, Brazilian National Council for Scientific and Technological Development, and state-level Brazilian research foundations. Dr. Assuncao leads the Wolfpack Innovations in Software Engineering Research (WISER) Lab at NC State University, where he supervises graduate students working on cutting-edge software engineering research problems. His lab maintains strong collaborations with international institutions and industry partners including Dynatrace and ITPRO Consulting & Software GmbH.
Stergios Christodoulidis is a researcher specializing in artificial intelligence and its applications in medical imaging and earth sciences. His work spans deep learning methodologies for image analysis, including multiple instance learning, dosiomics, and registration techniques. He contributes to interdisciplinary research in computational biology, digital pathology, and radiomics. Core research in AI-driven medical image analysis Contributions to earth sciences through remote sensing and satellite imagery Focus on low-data regimes and uncertainty quantification in AI models His recent publications highlight innovative approaches in: Visually grounded bias discovery in clinical AI Conformal prediction adaptation for vision-language models Self-supervised learning for histopathology and radiology applications Integration of biological constraints in machine learning workflows
Alberto Tellaeche Iglesias is a Lecturer at the University of Deusto's College of Engineering, Department of Computing, Electronics and Communication Technologies. He holds a Ph.D. in Systems Engineering and Automation (UNED, 2008) and an M.Sc. in Automation and Electronics Engineering (University of Deusto, 2001). With over 17 years in industrial R&D, he has worked at Tekniker Foundation (2007–2019) and led research projects under European FP7/H2020 and national programs. Ph.D., Systems Engineering and Automation, UNED (2008) M.Sc., Automation and Electronics Engineering, University of Deusto (2001) His research focuses on Machine Learning , Robotics , and Computer Vision , particularly for industrial automation and precision agriculture. He has published 15 recent papers on topics like 3D vision systems, federated learning for defect detection, and hybrid classifiers for agricultural monitoring. His work bridges Pattern Recognition and Industrial Automation , with applications in quality control, human-robot interaction, and smart cities. Grants and projects include EU-funded AI-Driven Cognitive Robotic Platforms (2021–2024), HAZITEK (2019–2022), and national initiatives like Edge Technologies for Industrial Distributed AI (2021–2024). He has supervised the thesis 'Progress in Industrial Photogrammetry' (2019) and serves on doctoral committees. He previously worked at Tekniker Foundation's Smart and Autonomous Systems unit and is part of interdisciplinary teams integrating 3D Vision and Deep Learning for industrial applications.
Prof. Adrian Evans serves as Deputy Head of Department in the Department of Electronic & Electrical Engineering at the University of Bath, where he leads research within the Electronics Materials, Circuits & Systems Research Unit (EMaCS) and The Foundry: Centre for Digital, Manufacturing & Design. His academic profile demonstrates active engagement in doctoral supervision and cutting-edge research across multiple domains of image processing and biometrics. His primary research focuses on biometrics—particularly 3D face recognition techniques resilient to facial expressions—and advanced colour/multispectral image processing. He has pioneered methods for colour edge detection, nonlinear filtering, and scale-space sieve-based segmentation. His motion estimation work specializes in analyzing non-rigid bodies like clouds and glaciers, while his nonlinear image processing research centers on mathematical morphological sieves and granulometric texture analysis. Recent publication trends (2021-2025) reveal a strategic shift toward computer vision applications in transportation (multi-camera vehicle tracking systems) and healthcare (non-contact cardiorespiratory monitoring). His work increasingly integrates radar technology with computer vision for vital sign detection while maintaining foundational contributions to mathematical morphology and image enhancement techniques. No major scientific awards are documented in the provided materials. His supervisory record includes 11 doctoral students, with current openness to new PhD candidates. Research funding spans UK government and industry collaborations including EPSRC projects like High Speed 4K Video Transmission (2016-2018) and multiple KTP partnerships with Seiche Measurements Limited and Navtech Radar Limited. He operates within Bath's EMaCS research unit and The Foundry center, leveraging these interdisciplinary environments for digital manufacturing and design innovation. His work directly supports UN Sustainable Development Goals through applications in smart transportation systems and healthcare technology development.
Ioannis Pratikakis serves as a Professor in the Department of Electrical and Computer Engineering at Democritus University of Thrace (DUTH), where he has held a faculty position since 2010. Previously, he worked as a collaborating researcher at the Institute of Informatics and Telecommunications of NCSR 'Demokritos' (2003-2010) and completed postdoctoral research at IRISA/INRIA in Rennes, France until 2000. His academic background includes a PhD in digital 3D image processing and analysis from the Vrije Universiteit Brussel (Belgium), awarded in January 1999. Pratikakis's research spans computer vision, 3D image processing, and document analysis, with core expertise in 2D/3D image sequence analysis, pattern recognition, and multimedia information retrieval. His work bridges theoretical computer vision with practical applications in cultural heritage preservation, medical imaging, and human-computer interaction. Analysis of his publication trends reveals sustained innovation in 3D vision systems, particularly in human action recognition from mesh sequences, facial expression analysis, and unsupervised methods for historical document processing. Recent work increasingly integrates deep learning for medical diagnostics while maintaining strong contributions to cultural heritage digitization. He actively leads research through major projects including μtS (historical manuscript transcription), READ (archival document enrichment), and TRANSCRIPTORIUM (handwritten document recognition), alongside European initiatives like H2020-EINFRA-9-2015 and FP7-ICT frameworks. Pratikakis directs research activities within DUTH's Electrical Circuits, Signal and Image Processing Laboratory, mentoring students through undergraduate courses (Computer Vision, Computer Graphics) and postgraduate instruction (Digital Video Editing, 3D Graphics). His professional engagement includes senior IEEE membership, Technical Chamber of Greece affiliation, and leadership in the Hellenic Society for Artificial Intelligence (2010-2014).
Dolores Messer is a Research Fellow at the Department of Computer Science, University of Copenhagen, affiliated with the Faculty of Science. She contributes to the Machine Learning section and the SCIENCE AI Centre, focusing on interdisciplinary research that bridges theoretical foundations with diverse applications. Her research spans Quantum computing and machine learning integration AI ethics and environmental sustainability Medical data analysis and clinical informatics Neuroscience and brain-computer interfaces Remote sensing applications Recommender systems domains. Publications highlight her work in quantum-inspired algorithms, explainable AI techniques, and cross-cultural applications. She collaborates with the TreeSense center for global tree resource monitoring and contributes to AI hardware development for optical neural networks.
Stefan Horst Sommer is a Professor at the Department of Computer Science (DIKU), University of Copenhagen . He leads the Pioneer AI (P1AI) section, serves as Head of Studies for Machine Learning and Data Science, and co-founded the Center for Computational Evolutionary Morphometry (CCEM) with Rasmus Nielsen. His work bridges stochastic processes , geometric statistics , and machine learning with applications in computational anatomy and diffusion modeling . Key Roles : Head of Pioneer AI, Head of MLDS Studies, CCEM PI Labs : Applied Geometry Lab, CCEM Research focuses on Riemannian geometry , anisotropic diffusion , and stochastic shape analysis . Current projects include geometric machine learning for aerodynamic modeling and probabilistic image registration with applications in medical imaging and evolutionary biology . His 96+ publications emphasize manifold-valued processes and geometric deep learning . Collaborations span computational anatomy , stochastic mechanics , and AI-driven scientific computing . He co-organizes international workshops on geometric statistics and maintains active GitHub repositories for open-source research tools.
Allan Carlé serves as a Clinical Associate Professor at Aalborg University's Faculty of Health Sciences, where he also works as a Senior Physician and Specialist at Aalborg University Hospital in the Medical Department serving Farsø, Hobro, and Thisted regions in Denmark. His academic and clinical work focuses on endocrinology with particular emphasis on thyroid disorders. Dr. Carlé's research interests prominently feature thyroid disease , with special attention to hypothyroidism (100% fingerprint match), iodine fortification (86% match), hyperthyroidism (48% match), and Graves' disease (39% match). His work spans clinical practice, epidemiological studies, and therapeutic approaches to thyroid disorders. He has examined thyroid conditions across various populations, with particular focus on pregnancy and the impact of iodine nutrition on thyroid health. His publication record shows consistent research activity from 2005 through 2025, with 115 publications including journal articles, conference abstracts, and research datasets. Recent work (2024-2025) focuses on thyroid dysfunction in pregnancy, immune checkpoint inhibitor therapy complications, and LT4/LT3 combination treatment outcomes. His research demonstrates strong collaboration with colleagues across Denmark and internationally, particularly in European thyroid research networks. European Thyroid Association participation (2012, 2013) Danish Endocrinological Society involvement (2013) Thyroidea Landsforeningen educational activities (2017) Dr. Carlé's work has gained media attention, including coverage of his 2019 study on iodine in household salt. His research datasets include important contributions to understanding levothyroxine formulations and hypothyroidism treatment outcomes.
Dr. Mohamed Al-Hussein is a Professor and NSERC Industrial Research Chair in the Industrialization of Building Construction at the University of Alberta’s Department of Civil and Environmental Engineering. His work focuses on advancing modular and offsite construction technologies through automation, lean principles, and Building Information Modelling (BIM). PhD, Construction Engineering & Management, Concordia University (1999) MASc, Construction Engineering and Management, Concordia University (1995) MSc, Civil Engineering, University of Architecture & Civil Engineering, Bulgaria (1988) BSc, Civil Engineering, University of Architecture & Civil Engineering, Bulgaria (1983) Dr. Al-Hussein’s research spans five key domains: Modular Construction: Pioneering high-efficiency offsite building systems, including rapid assembly of student dorms and mid-rise residential buildings. BIM & Digitalization: Developing 3D/4D modeling frameworks, automated design systems, and digital twin applications for construction optimization. Environmental Sustainability: Quantifying CO2 emissions, exploring nano energy storage, and advancing solar PV integration in residential construction. Urban Planning: Specializing in age-restricted community design, municipal infrastructure maintenance, and housing affordability analysis related to paving standards. Construction Safety: Applying ergonomic risk assessment tools and virtual reality to enhance worker safety and reduce construction-related hazards. His 400+ peer-reviewed publications reflect cutting-edge applications of AI, deep learning, and simulation across construction processes. Recent work explores metaverse integration, blockchain collaboration tools, and advanced crane operation optimization using reinforcement learning. As Editor-in-Chief of the International Journal of Industrialized Construction , Dr. Al-Hussein remains a global authority in this field. He has developed industry-transforming technologies like the Quikmod-2 modular lift frame and PCL lift frame project , with real-world implementations ranging from Shell Scotford complex equipment replacement to CBC News and Forbes featured projects.
Sanguthevar Rajasekaran is the Board of Trustees Distinguished Professor and Pratt & Whitney Chair Professor of Computer Science and Engineering at the University of Connecticut. He serves as Director of the School of Computing, leading interdisciplinary research initiatives. His academic roles include overseeing academic programs, research labs, and strategic partnerships in computing and information technology. His research focuses on algorithms, big data analytics, AI/ML, quantum computing, bioinformatics, and high-performance computing. Notable contributions include advancements in record linkage algorithms, IoT time series analysis, quantum algorithms for N-body simulations, and energy-efficient solar PV systems. He has organized major conferences like ICCABS and secured grants such as the NSF Student Travel Grant for computational bio-medical sciences. Rajasekaran has authored over 250 publications across journals and conferences, with recent work emphasizing quantum computing applications, medical data analytics, and cybersecurity in IoT systems. He leads collaborative projects with industry partners and federal agencies, fostering innovation in computational science and engineering.
AnHai Doan is the Vilas Distinguished Achievement Professor and Gurindar S. Sohi Professor in the Department of Computer Sciences at the University of Wisconsin-Madison. He leads research in data integration, entity matching, and data science, focusing on end-to-end systems like Magellan. His work integrates machine learning, scalable data management, and human-in-the-loop approaches. Education details are not explicitly provided in the text but his academic roles suggest advanced degrees in computer science. Research interests include data cleaning, entity matching, and cloud/crowd services. He co-founded GreenBay Technologies (acquired by Informatica) and contributed to the School of Computer, Data, and Information Sciences at UW-Madison. Key awards include the ACM Doctoral Dissertation Award (2003) and NSF CAREER Award (2004). He teaches data science courses (CS 638 DS, CS 774) and served on strategic initiatives for UW-Madison’s computing growth. His service includes roles on SIGMOD’s advisory board and co-chairing SIGMOD-2020.
Dr. Mohamed Khabou serves as a Professor in the Department of Electrical and Computer Engineering and Dean of the Hal Marcus College of Science and Engineering at the University of West Florida. He holds a Ph.D., M.S., and B.S. in Electrical Engineering from the University of Missouri. His research spans multi-component image analysis, gender equity in engineering, historical artifact digitization, and distance learning impact analysis. He has authored over 30 publications in IEEE Transactions and other journals, and his work includes systems for mosaic image indexing, actigraphy signal analysis, and land mine detection. Dr. Khabou has mentored over 60 engineering capstone projects, many of which achieved regional/national recognition. He teaches Microprocessor Applications, Digital Logic, and Pattern Recognition courses. His 2013 Faculty Excellence in Teaching Award reflects his dedication to pedagogy. Collaborative research areas include underrepresentation of women in engineering and interactive distance-learning laboratory development. Key research themes include spectral geometry for shape recognition, fuzzy logic applications in cultural heritage preservation, and adaptive machine learning systems. His work bridges theoretical foundations (eigenvalue analysis, Laplacian operators) with practical applications in education technology and biomedical signal processing.
