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Prof. Dr. Valentina Dagienė serves as a Professor and Senior Researcher at the Educational Systems Group within the Institute of Data Science and Digital Technologies at Vilnius University, Lithuania. Her academic career spans several decades with significant contributions to informatics education globally, particularly through her leadership in the international Bebras contest initiative. Her research focuses on computational thinking education through constructionist learning approaches, with particular emphasis on task design that promotes deep conceptual understanding in K-12 settings. Prof. Dagienė has pioneered methods for integrating computational thinking into primary education curricula while addressing cultural differences in learning approaches. Her work bridges theoretical frameworks with practical classroom implementations, making complex informatics concepts accessible to young learners through engaging short tasks. Analysis of her recent publications reveals a clear progression toward interdisciplinary integration of computational thinking with STEAM education and digital competence frameworks. Her research increasingly addresses assessment methodologies for computational thinking skills and explores the connections between computational and algebraic thinking. The Bebras contest serves as both a research platform and practical implementation vehicle for her educational theories. Prof. Dagienė has established herself as a key figure in international informatics education through her editorial work, conference organization, and cross-national collaborations. She has fostered partnerships between educators and researchers across Europe and beyond, creating sustainable communities around computational thinking education. Her leadership in the Bebras International Contest has engaged millions of students worldwide in computational problem-solving activities. Through her work with the Educational Systems Group, Prof. Dagienė has developed comprehensive teacher training programs that support educators in implementing computational thinking concepts in diverse classroom settings. Her research on student approaches to problem-solving has informed the design of learning environments that accommodate different learning styles and cultural backgrounds.

• Computational Thinking Education
• Informatics Education
• Educational Technology
• +3 more

Dr. Olga Kurasova is a Professor and Senior Researcher at Vilnius University's Institute of Data Science and Digital Technologies, where she leads research in the Cognitive Computing Group. Her work focuses on developing advanced computational methods for real-world applications. Her primary research explores machine learning paradigms including deep learning for cybersecurity (keystroke dynamics, adversarial attacks), medical image analysis (pancreatic cancer detection), industrial monitoring, and explainable AI. She maintains strong collaborations across disciplines, particularly in healthcare and security domains. Analysis of her recent publications (2023-2025) reveals three dominant themes: (1) Advanced biometric authentication systems using behavioral analysis and deep learning, (2) Medical AI applications focusing on pancreatic cancer detection through CT image analysis, and (3) Theoretical advancements in explainable AI methodologies for high-stakes domains. Significant scientific recognition includes: 2021 Lithuanian Science Prize for the cycle 'From Data Science to Artificial Intelligence Technologies' 2024 Vilnius University Rector's Science Prize She has led multiple national research projects, including a 2024-2027 LMT-funded initiative on 'adversarial machine learning for cybersecurity' and coordinated interdisciplinary teams for projects on cognitive computing capabilities and optimal data mining solutions. She directs research within the Cognitive Computing Group, focusing on developing intelligent systems for data analysis, visualization, and decision support across healthcare, cybersecurity, and industrial applications.

• Machine Learning
• Data Visualization
• Cybersecurity
• +3 more

Darius Plikynas is a Senior Researcher at the Smart Technologies Research Group within the Institute of Data Science and Digital Technologies at Vilnius University . His research integrates computational intelligence methods with agent-based simulation to model cognitive and social processes. Position: Senior Researcher, Chief Researcher in the Project Address: Akademijos St. 4, room 224, Vilnius Contact: +370 5 210 9333, +370 620 95101 Email: darius.plikynas@mif.vu.lt Personal page: http://www.dariusplikynas.eu Dr. Plikynas' research spans interdisciplinary domains including neuroscience, physics methods, complexity theory, and distributed cognitive systems. He led the 2017–2019 project "Development of a metric, conceptual and simulation model of the social impact of cultural processes" under the LMT Research Group Funding Program. His recent publications (2016–2025) reflect trends in combining machine learning with social science questions (fake news analysis, propaganda detection) and agent-based modeling of cultural/social capital dynamics. Key collaborations include Leonidas Sakalauskas, Rimvydas Laužikas, and Arunas Miliauskas. Scientific supervision includes doctoral students: Andrius Budrionis (University of Tromsø, Norway) Ieva Rizgelienė (PhD topic: "Propaganda detection and classification in social media using hybrid deep learning") He also serves as an expert at Vilnius University and has contributed to projects involving: 2D financial market visualization Neural oscillation-based cognitive modeling Indoor navigation for blind individuals Cultural participation impact on social capital

• Computational Intelligence
• Agent-Based Simulation Systems
• Neuroscience
• +4 more

Professor Hone-Jay Chu is a faculty member at the Department of Geomatics, National Cheng Kung University, where he has been employed since 2011. He earned his PhD from National Chiao Tung University in 2008, focusing on artificial intelligence and optimization for water resource management, followed by postdoctoral work (2008–2011) with Professor YP Lin on spatial environmental monitoring and land use change models. Education : PhD in Environmental Engineering (2008) from National Chiao Tung University His research integrates geospatial technologies with machine learning to address complex environmental challenges. Key domains include groundwater dynamics , land use/cover change , landslide detection , and air pollution analysis , leveraging big data , remote sensing , and environmental resilience frameworks. Recent publications highlight applications of deep learning (e.g., LSTM networks) and random forest models in predicting meteorological droughts, groundwater changes, and climate impacts. The work spans interdisciplinary collaboration, with co-authors from institutions in Bangladesh, Austria, and Turkey.

• Groundwater
• Land cover and land use change
• Spatial Data
• +6 more

Valiulis Gediminas serves as an Associate Professor at Vilnius University's Šiauliai Academy within the Department of Informatics Engineering. His academic career spans both engineering fundamentals and advanced environmental applications, with a pronounced shift toward bioaerosol monitoring and computer vision systems since 2018. His research interests focus on Environmental Informatics and Automatic Bioaerosol Detection , particularly developing computer vision models for pollen classification using light scattering techniques. This work bridges Environmental Science , Computer Vision , and Public Health through real-time pollen monitoring systems that serve allergy sufferers. His methodology combines fluorescent particle analysis , clustering algorithms , and sensor calibration to advance aerobiological research. Analysis of his publication trends reveals a strategic evolution: early work (2004-2012) centered on control systems engineering (granulation processes, microinverters), while recent output (2018-2024) demonstrates deep specialization in bioaerosol informatics . Key contributions include the Rapid-E particle counter validation, ozone-pollen interaction studies, and real-time pollen forecasting systems for clinical applications. His interdisciplinary approach connects atmospheric science with machine learning to solve environmental monitoring challenges. Automatic pollen recognition systems development Fluorescent bioaerosol characterization Real-time environmental data applications Computer vision for ecological monitoring While no formal advising relationships or scientific awards are documented in the source material, his collaborative research consistently involves multi-institutional teams across Lithuania and Europe. Current projects focus on enhancing bioaerosol sensor accuracy and developing clinical decision-support tools for allergy management through real-time environmental data integration.

• Environmental Informatics
• Bioaerosol Monitoring
• Computer Vision Applications
• +3 more

Viktor Medvedev is an Associate Professor and Senior Researcher at Vilnius University's Institute of Data Science and Digital Technologies, where he serves as Project Lead Researcher in the Blockchain and Quantum Technologies Group. His work focuses on the intersection of machine learning, data visualization, and cybersecurity applications. Dr. Medvedev earned his Doctor of Science in Computer Science Engineering in 2007 from Vilnius Gediminas Technical University and the Institute of Mathematics and Informatics. His doctoral research centered on 'Research on the application of feedforward neural networks for multidimensional data visualization,' establishing the foundation for his continued work in data analysis and visualization techniques. His research interests span machine learning , deep learning , data visualization , and cybersecurity applications . Specifically, he has made significant contributions to keystroke dynamics authentication , behavioral biometrics , dimensionality reduction techniques , and medical data analysis . His work often bridges theoretical computer science with practical applications in security and healthcare domains. Dr. Medvedev's publication record demonstrates a consistent trajectory of research excellence, with recent work focusing on advanced authentication systems using deep learning, pancreatic cancer detection through machine learning, and innovative approaches to data visualization. His research shows a clear evolution from foundational work in neural networks for data visualization to contemporary applications in cybersecurity and medical diagnostics. ICAISC'06 - Best Presentation Award (The 8th International Conference on Artificial Intelligence and Soft Computing) ICANNGA 2007 - Best Young Researcher Paper Award in Neural Networks DAMSS 2021, 2022, 2023 - Best Poster Awards As a Project Lead Researcher in the Blockchain and Quantum Technologies Group, Dr. Medvedev oversees research initiatives that combine cutting-edge technologies with practical applications. His work in cybersecurity has particular relevance to critical infrastructure protection, where insider threat detection using behavioral biometrics represents a significant contribution to the field.

• Machine Learning
• Deep Learning
• Neural Networks
• +8 more

Prof. Dr. (HP) Saulius Gudas is an Affiliated Professor in the Cybersocial Systems Engineering Group of the Institute of Data Science and Digital Technologies at Vilnius University . His office is located at Akademijos St. 4, room 603, Vilnius, Lithuania. Education & Background: While explicit degrees are not listed in the provided source, his long-standing professorship and extensive publication record spanning over a decade indicate advanced doctoral training and senior academic status. Research Interests: Prof. Gudas focuses on the intersection of software engineering, enterprise modelling, and knowledge-based systems . Core themes include: Causal knowledge modelling to enhance agile enterprise application development. Model-driven approaches for ensuring business-IT alignment. Deep knowledge-based evaluation and interoperability of enterprise applications. Quality modelling of web services and financial process mining. Publication Trends: Across the most recent works (2017–2024), a clear trajectory emerges from foundational enterprise modelling and interoperability assessment toward leveraging causal models and data analytics to improve agile development processes, service quality, and financial anomaly detection. Doctoral Supervision & Training: Prof. Gudas has successfully supervised the following PhD projects: Andrius Valatavičius – “Assessment of application program interoperability using autonomous computing technologies” (defended 2019). Mindaugas Jusis – “Research on synchronization methods for autonomous port loading processes” (2016–2020). Karolis Noreika – “Evaluation of the application development process using an improved Agile project management method” (2019–2023). Laboratory & Team Involvement: As a senior member of the Cybersocial Systems Engineering Group, he contributes to interdisciplinary projects that integrate software engineering methodologies with socio-technical system analysis within the larger Institute of Data Science and Digital Technologies.

• Software Engineering
• Enterprise Modelling
• Information Systems Engineering
• +5 more

Dr. Carlos Raúl Sánchez holds dual roles as a Visiting Lecturer at ISM and Associate Professor of Entrepreneurship and Marketing at Montpellier Business School. He earned a PhD in Management and Economics and has over 21 years of experience as a professor, researcher, and consultant across Spain, Mexico, France, Australia, and other regions. His expertise spans social innovation, frugal innovation, and customer-centric strategies, with a focus on bridging academic research and practical business applications. He has extensive consulting experience with global brands, offering deep insights into business cultures in Latin America, Australia, France, and Spain. His pedagogical innovations include certifications in Lego® Serious Play®, Design Thinking, and Gamification, emphasizing experiential learning. His research outputs prioritize entrepreneurship education, social enterprise models, and cross-cultural consumer behavior analysis. Key research themes include social innovation frameworks, frugal innovation in education, and typologies of consumer behavior in global markets. His work often intersects with microfinance, gender empowerment (as seen in his 2004 Mexican case study), and healthcare service optimization. Despite no listed awards or grants, his prolific publications reflect sustained academic contribution across multiple continents.

• Social Innovation
• Frugal Innovation
• Social Entrepreneurship
• +4 more

Dr. Šarūnas Mikolaitis is an Associate Professor and Senior Researcher at Vilnius University's Institute of Theoretical Physics and Astronomy (ITPA). His research focuses on stellar evolution, galaxy chemodynamics, and chemical abundances in stars, with a particular emphasis on understanding the chemical composition of planetary host stars and Galactic structure using data from the Gaia-ESO Survey and other international collaborations. Institution: Institute of Theoretical Physics and Astronomy (ITPA), Vilnius University Research Area: Stellar evolution, chemodynamics of the Galaxy Key Collaborations: Gaia-ESO Survey, AMBRE project, PLATO mission Research Trends: Analysis of elemental abundances (C, N, O, Fe, neutron-capture elements) in open clusters and field stars, using high-resolution spectroscopy and machine learning techniques to trace Galactic chemical evolution, age-abundance relations, and star-planet connections. Publication Highlights: Contributions to the Gaia-ESO Survey data releases, studies on neutron-capture elements in planetary hosts, and investigations of mixing processes in RS CVn and hot subdwarf stars. His work often involves international consortia and large-scale spectroscopic datasets.

• Stellar evolution
• Galaxy chemodynamics
• Chemical abundances
• +2 more

Joana Katina is an Assistant Professor at the Faculty of Mathematics and Informatics, Vilnius University, Lithuania, teaching Internet Technologies and Algorithms and Data Structures continuously from 2016/2017 through 2025/2026 academic terms across multiple course formats including lectures, exercises, and subgroup sessions. Her research bridges computer science with energy systems and financial engineering, focusing on household energy behavior in Lithuania/Morocco, cryptocurrency forecasting using neural networks, Industrial Control Systems cybersecurity, and queueing theory. She integrates computational modeling with socio-technical analysis to address sustainability challenges and market prediction problems. Analysis of her 2023-2025 publications reveals a dominant trend toward renewable energy transitions (particularly waste-to-energy systems and cross-regional green strategies) combined with machine learning applications in finance. This evolution from earlier virtual stock exchange simulations demonstrates increasing interdisciplinary scope while maintaining core expertise in stochastic modeling and predictive analytics.

• Renewable Energy
• Financial Engineering
• Computer Science
• +4 more

Viktoras Bulavas is an Adjunct Professor at Vilnius University's Faculty of Physics and a PhD student in the Cognitive Computing Group at the Institute of Data Science and Digital Technologies. He works at Saulėtekio al. 9, ITTC (JK2) room 207, Vilnius, with contact number +370 5 236 6202. His educational background includes a Master's degree in Physics from Vilnius University (1991) and a Master's degree in Public Administration from BI Norwegian School of Management (1997). Since 2004, he has been teaching Introduction to Project Management to 4th-year undergraduate students at the Faculty of Physics. Bulavas specializes in cyber and information security, with research focusing on applying machine learning methods for early detection of cyber incidents. His work spans information systems audit, information technology project management, and management of scientific research projects. His doctoral research, supervised by Prof. Habil. Dr. Gintautas Dzemyda with consultant Prof. Dr. Virginijus Marcinkevičius, runs from October 1, 2017 to September 30, 2026. His publication record demonstrates consistent contributions to network security and intrusion detection, with a focus on machine learning applications. His work shows progression from exploring data visualization methods (2018) to investigating fractal dimensionality (2019) and ultimately optimizing machine learning model performance (2021). Professional certifications include: ECPC-B DPO Professional from the European Center on Privacy and Cybersecurity (2019) Certified Scrum Master from Scrum Alliance (2011) CRISC certification from ISACA (2011) CGEIT certification from ISACA (2009) CISA certification from ISACA (2006) Bulavas has participated in significant academic activities including an Erasmus+ Doctoral Internship at Gdańsk University of Technology (2020-2021) and multiple International Summer Schools on Deep Learning at the same institution (2018, 2019).

• Cyber and information security
• Application of machine learning methods
• Information systems audit
• +4 more

Arnold Budžys is a Junior Researcher at the Vilnius University Institute of Data Science and Digital Technologies , focusing on cybersecurity, machine learning, and behavioral biometrics. His work addresses critical infrastructure security, insider threat detection, and keystroke dynamics-based authentication. Research Highlights : Keystroke biometrics, anomaly detection, deep learning, data fusion, and red team tactics. Technical Focus : Behavioral analysis, neural networks, security frameworks, and adversarial attack modeling. His publications reveal a trend toward integrating advanced AI techniques with cybersecurity protocols to enhance authentication accuracy and threat prevention in sensitive systems.

• Cybersecurity
• Machine Learning
• Keystroke Dynamics
• +6 more

Jonas Dagys serves as an Associate Professor in the Department of History of Philosophy and Analytical Philosophy at Vilnius University, where his research centers on contemporary analytic philosophy, logic, and the historical development of analytic thought. His scholarly profile reflects deep engagement with methodological implications of psychophysical reductionism and antireductionism, as well as critical examinations of temporal metaphysics through comparative studies of philosophers like McTaggart and Mellor. His research trajectory demonstrates evolving specialization: early work (2006-2012) established foundations in consciousness studies and mind-body problems, while recent publications (2019-2022) reveal intensified focus on medieval logic—particularly Buridan's modal propositions—and intersections between analytic philosophy and early Christian thought. Additional interests include Fregean semantics, university management during crises, and Wittgensteinian philosophy, indicating both historical depth and contemporary relevance in his analytical approach. Analysis of his 15 most recent publications shows a clear methodological pattern: rigorous logical formalization of historical philosophical concepts combined with empirical considerations in identity studies. The 2020-2022 output particularly highlights adaptation to contemporary academic challenges, including pandemic-era educational management alongside sustained exploration of foundational philosophical questions through medieval and modern lenses. No scientific awards were documented in the provided sources. Information regarding student supervision, research grants, laboratory affiliations, or collaborative research teams was not present in the available materials, suggesting either omission in the source texts or primary focus on independent scholarly publication rather than team-based research activities.

• contemporary analytic philosophy
• logic
• history of analytic philosophy

Roles & Affiliation: Professor at the Regional Development Institute of Šiauliai Academy (Vilnius University). Previously served as Professor and Head of the Department of Electronics at Šiauliai University. Specializes in Computer Science, Environmental Science, and Biometrics, with a focus on airborne pollen recognition, deep neural networks, and bioaerosol monitoring. Doctorate in Electrical and Electronic Engineering from Kaunas University of Technology (1999). Research Interests: Combines computer vision and machine learning to advance automated pollen classification, allergen forecasting, and health impact analysis. Explores applications of AI in environmental monitoring, biometrics, and medical diagnostics. Publications: Over 50 peer-reviewed articles, with recent work emphasizing real-time pollen detection systems, health correlations of airborne allergens, and automated bioaerosol monitoring. Key projects include EU-funded initiatives on synergistic effects of pollen and air quality, and real-time pollen forecasting. Grants & Projects: Led EU FP6 projects (COGAIN, MICOLE) on eye-tracking technology for disabled users. Active in Lithuania’s Research Council and EU Structural Funds projects focusing on pollen and environmental health. Labs & Teams: Collaborates with interdisciplinary teams in Šiauliai Academy’s Department of Electronics and bioaerosol research groups. Part of international networks like ISCA and IEEE, contributing to standards in eye-tracking and environmental monitoring.

• Computer Science
• Environmental Science
• Biometrics
• +7 more

Linas Petkevičius serves as an Associate Professor at Vilnius University's Faculty of Mathematics and Informatics, actively teaching courses including Introduction to Quantum Computing across 10 consecutive academic years from 2016/2017 through 2025/2026 as evidenced by institutional schedules. His research demonstrates remarkable interdisciplinary breadth spanning quantum computing algorithm optimization, medical diagnostics through digital pathology analysis, and satellite-based environmental monitoring. He develops machine learning solutions for breast cancer prognosis using Ki67 heterogeneity metrics, creates quantum circuit schemes adapted to hardware constraints, and implements deep learning models for algal bloom detection in Baltic waters using Sentinel-2 data. His work consistently bridges theoretical computer science with practical healthcare and environmental applications. Analysis of his 15 most recent publications (2023-2025) reveals three dominant research thrusts: 1) Quantum computing optimization for NISQ devices, 2) Medical image analysis focusing on spatial tumor microenvironment characterization in breast cancer, and 3) Remote sensing applications using transformer models and few-shot learning for satellite change detection. His publications show increasing specialization in combining deep learning architectures with domain-specific constraints across these fields. Scientific Awards: No scientific awards were mentioned in the provided materials. Advising and Grants: The provided texts contain no information regarding student advisement, research grants, or funded projects.

• Quantum Computing
• Machine Learning
• Medical Image Analysis
• +3 more