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Saulius GudasView profile

Professor

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

Vaidotas MarozasView profile

Professor

• Digital Signal Processing
• Machine Learning Algorithms
• Wearable Medical Devices
• +2 more

Prof. Dr. Vaidotas Marozas is affiliated with the Kaunas University of Technology (KTU) , serving as a Professor in the Faculty of Electrical and Electronics Engineering , Department of Electronic Engineering. He is currently the Director of the Institute of Biomedical Engineering and leads the Biosignal Analytics Laboratory . His research focuses on digital signal processing, machine learning applications, wearable medical devices, and cardiovascular disease monitoring technologies. Institute of Biomedical Engineering - Director Biosignal Analytics Laboratory - Head Projects: SV/115, SV/116, SV3/116 - Chief Researcher He can be contacted at vaidotas.marozas@ktu.lt . Professional profiles: Google Scholar , ORCID , Scopus ID 8319694900, ResearcherID T-4499-2017.

• Educational Technology
• Information and Communication Technology in Education
• Cybersecurity
• +3 more

Lazar Stosic is a Professor at the College of Professional Studies Educators, where he contributes to academic development and research in the intersection of technology and education. His work bridges technical disciplines with pedagogical innovation, particularly in digital learning environments. Research Interests: His primary areas include Educational Technology, Cybersecurity, Information and Communication Technology (ICT) in Education, and Computer Science Education. His research emphasizes improving educational quality through technological integration and securing digital learning platforms. The recent publications reflect a strong trend toward the application of computer science in educational contexts, especially focusing on cybersecurity awareness, AI ethics in assessment, and digital transformation challenges in academic institutions. These works span interdisciplinary domains combining technical rigor with psychological and organizational insights. Scientific Awards: No awards mentioned in the provided text. Advising and Grants: There is no explicit information regarding student advising, research grants, or funded projects. However, his editorial role in the journal "Psychology and Law" indicates scholarly leadership and peer recognition. Labs and Research Teams: No specific labs, research groups, or collaborative teams are mentioned in the available data.

• Generalized Nets
• Intuitionistic Fuzzy Sets
• Decision Making Under Uncertainty
• +3 more

Assoc. Prof. Vassia Atanassova, PhD is an Associate Professor in the Bioinformatics and Mathematical Modelling Department at the Institute of Biophysics and Biomedical Engineering , Bulgarian Academy of Sciences. She holds a PhD in Informatics and Computer Sciences (2013) and has been actively contributing to generalized nets, intuitionistic fuzzy sets, and decision-making under uncertainty since 2002. Education : PhD in Informatics and Computer Sciences (2013), Institute of Information and Communication Technologies – BAS Master in Marketing (2009), University of National and World Economy Bachelor in Informatics (2004), Faculty of Mathematics and Informatics, Sofia University Her research focuses on intuitionistic fuzzy logic applications, generalized net modeling , and Wiki technologies for knowledge transfer . She has developed novel intuitionistic fuzzy operators and conducted extensive work on intercriteria analysis for complex systems. Recent publications emphasize fuzzy decision-making frameworks (2015-2019), with applications in Algorithm optimization Economic modeling Bioinformatics Medical data analysis Scientific Awards : Youngest Researcher Award 'Ivan Evstratiev Geshov' (2011) 2nd Award, VIII Youth Session of Federation of Scientific-Technical Unions (2010) She serves as Guest Editor for MDPI Mathematics and technical editor for international journals. Her teaching includes PhD-level courses on Wikipedia-based knowledge transfer and fuzzy set theory at Bulgarian universities.

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

• Cybersecurity
• Machine Learning
• Network Analysis
• +5 more

Linas Bukauskas is a Professor at Vilnius University's Department of Informatics under the Faculty of Mathematics and Informatics. Since 2005, he has supervised academic projects and taught courses in cybersecurity, with a focus on practical skill assessment, network analysis, and human factors in security systems. Research Interests: Cybersecurity, Machine Learning, E-Vehicle Infrastructure, Cyber Defense Training Key Contributions: Development of cybersecurity training platforms, behavioral analysis in cyber exercises, and novel network flow modeling techniques Recent Work: Focus on maritime cybersecurity resilience, individualized risk assessment frameworks, and AI integration in smart learning environments

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

• Cyber and information security
• Application of machine learning methods
• Information systems audit
• +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).

• Blockchain Technology
• Quantum Computing
• Machine Learning
• +3 more

Marco Marcozzi is a researcher at the Vilnius University Blockchain and Quantum Technologies Group. His work focuses on blockchain systems, quantum computing, and performability analysis of fault-tolerant protocols. Research Interests : Blockchain technology, quantum computing, Byzantine fault tolerance, consensus protocols, and data-driven system performance modeling. Key Contributions : Led studies on quantum-enhanced machine learning, performability analysis of PBFT systems, and classification of DLT consensus protocols using clustering algorithms. Publications Trend : Recent works emphasize quantum computing applications in blockchain systems, performability modeling for fault-tolerant architectures, and machine learning approaches to consensus protocol analysis across disciplines like computer science and system reliability engineering. Current Affiliation : Blockchain and Quantum Technologies Group, Vilnius University, Akademijos St. 4, Vilnius.

• Reinforcement Learning
• Artificial Intelligence
• Machine Learning
• +4 more

Oskaras Klimašauskas is a Researcher affiliated with the Cognitive Computing Group at Vilnius University's Institute of Data Science and Digital Technologies. His work focuses on reinforcement learning applications in autonomous vehicle navigation and route optimization. His research explores Artificial Intelligence , Machine Learning , and Visual Environment Development . Recent publications (2024-2025) demonstrate his expertise in applying reinforcement learning to autonomous driving scenarios, including route navigation, track driving, and interactive route computing environments. The articles reflect interdisciplinary work at the intersection of Computer Science , Data Science , and Software Engineering . Key sub-fields include simulation, optimization, intelligent navigation systems, and vehicle control algorithms. Though no formal awards are listed, his publications indicate active research contributions.

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

Saulius PreidysView profile

Assistant Professor

• Big Data Processing
• E-learning Methodology
• Business Intelligence
• +3 more

Roles & Affiliations: Assistant Professor at the Department of Organizational Information and Communication Research, Faculty of Communication, Vilnius University. Active in research projects, academic conferences, and editorial roles. Education: PhD in Mathematics and Informatics (2012), Vilnius University, Thesis: 'The applications of datamining methods to personalized learning environments.' MA in Informatics (1995), Vilnius Pedagogical University. Research Interests: Focus on e-learning methodologies, big data processing, educational technology, and business intelligence. Explores applications of machine learning in finance and personalized learning systems. Publications & Projects: Over 15 articles in peer-reviewed journals and conferences (e.g., Applied Sciences, Mykolas Romeris University Proceedings). Led the EU-funded project 'Development and Introduction of Multilingual Teacher Education Programmes in Georgia and Ukraine (DIMTEGU)' (2012–2016). Active in conference organizing and editorial work. Grants & Memberships: Participated in EU-funded projects. Member of the National Association of Distance Education (NADE), Lithuanian Association of Distance and e-Learning (LieDM), and Lithuanian Computer Society (LIKS). Labs & Teams: Involved in research groups focused on educational technology, data analytics, and digital transformation initiatives within the Faculty of Communication.

Goda KlumbytėView profile

Lecturer

• Critical Algorithm Studies
• Science and Technology Studies
• Human-Computer Interaction Design
• +7 more

Goda Klumbytė is a Lecturer and post-doctoral researcher at the University of Kassel, working within the Participatory IT Design department in the Faculty of Electrical Engineering and Computer Science. She is an interdisciplinary scholar bridging informatics with humanities and social sciences, with particular expertise in feminist new materialism, posthumanism, and human-computer interaction. Her research focuses on creating more contextualized and accountable machine learning systems through feminist and critical theoretical approaches. Her research interests include Critical Algorithm Studies, Science and Technology Studies, Human-Computer Interaction Design, Feminist Epistemology, Critical Theory, Posthumanism, and New Materialism. She approaches the study of algorithmic systems from a feminist perspective that emphasizes situated knowledges, intersectionality, and material-semiotic approaches to technology design. Her recent publications demonstrate a clear trend toward developing feminist frameworks for explainable AI and accountable machine learning systems. Her work examines how critical theoretical concepts from social sciences and humanities can be integrated into computing practices, with particular attention to how power relations and structural inequalities manifest in algorithmic systems. She has been particularly active in exploring how feminist intersectional perspectives can reshape approaches to AI explainability and accountability. Co-edited 'More Posthuman Glossary' with R. Braidotti and E. Jones (Bloomsbury, 2022) Published in 'Posthuman Glossary' (Braidotti & Hlavajova, 2018) Contributed to 'Everyday Feminist Research Praxis' (Leurs & Olivieri, 2015) Published in journals including Online Information Review, Digital Creativity and ASAP Presented at major informatics conferences including ACM's CHI, nordiCHI and FAccT Dr. Klumbytė leads significant research projects including 'AI Forensics: Accountability through Interpretability in Visual AI Systems' (funded by Volkswagen Foundation, 2022-2025) and 'CF+: Reconfiguring Computing Through Cyberfeminism and New Materialism' (University of Kassel, 2018-2019). She also serves as one of the editors of the critical computing blog 'engines of difference,' which provides a platform for critical perspectives on computing and technology.

• Renewable Energy
• Automation
• Computer Vision
• +4 more

Dr. Dervinis Donatas is an Assistant Professor at the Department of Electrical and Electronic Engineering, VU Šiauliai Academy. His research focuses on Renewable Energy, Automation, Computer Vision, and Cybersecurity, with a strong emphasis on practical applications in industrial systems and educational technologies. He holds a Doctor of Science in Electrical and Electronics Engineering and has over 22 months of academic experience. His educational background includes advanced studies in electrical engineering, complemented by a prolific career in research. Key research areas include solar energy systems, image processing, and automated systems for transportation and security. He contributed to the development of a textbook on Image Processing (2012) and has been affiliated with the eLABa research group. Donatas has authored or co-authored over 15 peer-reviewed publications, exploring topics such as energy-efficient cloud computing, password security, and real-time video analysis for human recognition. His work bridges theoretical advancements with practical implementations in fields like industrial automation and biomedical engineering. While no specific awards are listed, his publications appear in reputable journals like Applied Scientific Research and Professional Studies: Theory and Practice . His academic advising and grant activities are not explicitly detailed, though his research often involves collaborative projects with institutions like Tallinn University of Applied Sciences and Šiauliai State College. Donatas is actively involved in the eLABa research team, focusing on innovative solutions in electrical engineering and technology education. His future work continues to emphasize sustainable energy systems and advanced computer vision applications.

Daunys GintautasView profile

Professor

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

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.