Vijolė Satkauskienė is a Lecturer and CEO of UAB „Verslo barometras“. She specializes in applied research and experimental development activities conducted between 2020–2023, with a focus on science popularization. Conducted 8-hour accounting and tax training for non-profit accountants (Contract No. ST-6 2020-12-02) Active member of Utena City Board Local Action Group, Lithuanian Engineers Union (Utena Civil Engineers Club), and Chairperson of Utena Accountants Club Her research interests emphasize practical applications in financial governance and regulatory compliance for non-profit organizations.
ISM University of Management and EconomicsLithuania
Prof. Jannis Angelis is a Visiting Professor at ISM and a Docent/Reader in Operations Strategy at Indek. Previously, he held roles at Cambridge Judge Business School, Warwick, and Stockholm, and served as a country champion for teaching excellence at Oxford University’s education society. His academic journey includes a PhD in operations management from Cambridge, MPhil in political economy (Cambridge), MA in China studies from SOAS, and an MSc in international relations from Stockholm. His research focuses on the intersection of technology and management, exploring areas such as blockchain applications in supply chains, circular economy strategies for EV batteries, and digital transformation in manufacturing. Notable contributions include studies on blockchain in food supply chains, DLT ecosystems, and lean operational excellence, as evidenced by his receipt of the Shingo Prize and Skinner/Voss best paper awards. Angelis has supervised 9 PhDs and 400 student projects, secured €1.4m in grants, and led major research initiatives. His professional experience spans roles at the ILO, venture capital firms, and international consultancies like ITC (WTO/UNCTAD). He currently leads research at the Gunilla Bradley Centre and IFN Research Institute, focusing on digitalization and operational excellence. Teaching Awards: Four teaching awards, including recognition for pedagogical innovation. Research Grants: Over €1.4m secured, with leadership in three major projects. Labs/Teams: Active in the Gunilla Bradley Centre (Digitalisation) and IFN Research Institute (Industrial Economics).
Professor Yuosre F. Badir is a distinguished academic and administrator at the Asian Institute of Technology, serving as both Dean and Professor of Technology and Innovation Management at the School of Management (SOM). With a Ph.D. in Management of Technology from EPFL Lausanne and advanced degrees in logistics, project management, and civil engineering, he brings over two decades of expertise in innovation management and digital transformation across global institutions. Education : Ph.D. in Management of Technology (EPFL), Master in Logistics (EPFL), MSc in Project Management (University of Putra Malaysia), B.Sc. in Civil Engineering (University of Benghazi) Research Interests : Managing technological innovation, digital transformation, green innovation, and innovation dynamics in SMEs International Experience : Research Fellow at Tokyo Institute of Technology, Visiting Scholar at NYU, and various roles at EPFL and European University in Geneva His publications demonstrate a consistent focus on innovation management in emerging markets, with particular emphasis on green innovation , open innovation strategies , and digital transformation in SMEs. Over 15 recent publications reveal patterns in sustainable business practices , technology adoption , and organizational innovation across multiple disciplines. Key Awards and Recognitions : Best paper award in Strategic Marketing Track at ANZMAC 2017 Best paper award in Entrepreneurship and Innovation Track at ANZMAC 2016 Best article award at International Conference on Emerging Trends in Business and Economy 2014 Selected among top ten engineering 'works of value' by IEEE USA Today's Engineer 2003 Professor Badir has advised numerous publications through collaborative research while teaching advanced courses in Technology-based M&A , Corporate Technology Strategy , and Qualitative Research Methods . His work has been published in top journals including Research Policy , Journal of Product Innovation Management , and IEEE Transactions on Engineering Management .
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.
Daiva Makutėnienė is an Associate Professor at the Faculty of Bioeconomy Development within Vytautas Magnus University , affiliated with the Department of Applied Economics, Finance and Accounting . She is also associated with the Bioeconomy Research Institute and Agriculture Academy . Doctor of Social Sciences (2000) Associate Professor (2005) Her research focuses on sustainable bioeconomy , agricultural and food economics , and rural development . Recent projects analyze biomass flows, competency development in agricultural sectors, and strategic policy frameworks for sustainable production. Key trends in her work include economic modeling , resource efficiency , and policy analysis for bioeconomic systems. She specializes in value addition in agriculture , stakeholder engagement , and international collaboration in education reform. Her affiliations and projects span national initiatives like the Lithuanian Rural Development Program 2014-2020 and international programs such as Erasmus+ . She has contributed to curriculum development in Uzbekistan and competency training for agronomic professionals.
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.
Prof. Kęstutis Kubilius serves as an Adjunct Professor within the Interdisciplinary Statistical Research Group at Vilnius University's Institute of Data Science and Digital Technologies. His research office is located at Akademijos Street 4, room 208 in Vilnius, with contact numbers +370 5 210 9731 and +370 618 06064. His research program centers on advanced stochastic processes, specializing in fractional Brownian motion and fractional stochastic differential equations (FSDEs). Key contributions include developing existence-uniqueness frameworks for FSDEs with stochastic forcing, creating numerical approximation schemes like the implicit Euler method, and advancing parameter estimation techniques for Hurst and Orey indices. His work bridges theoretical probability with applications in financial mathematics and statistical modeling of long-range dependent phenomena. Analysis of his 15 most recent publications reveals consistent focus on fractional calculus applications, with increasing emphasis on numerical methods since 2020. The research demonstrates methodological evolution from theoretical foundations toward practical implementation, particularly in volatility modeling and boundary-conditioned SDEs. Recent collaborative works (notably with Medžiūnas and Mishura) highlight interdisciplinary approaches combining statistical inference with computational mathematics. Prof. Kubilius actively supervises doctoral candidates, having guided Aidas Medžiūnas through completion of the 2018-2022 dissertation 'Parameter evaluation for mixed SDLs'. His research group maintains strong connections with international probability theory communities, evidenced by co-authorships with leading scholars in fractional calculus. As a core member of the Interdisciplinary Statistical Research Group, he contributes to Vilnius University's strategic focus on advanced statistical methodologies for complex data systems, with particular relevance to financial engineering and time-series analysis applications.
Rimantas Rudzkis is an Affiliated Professor at Vilnius University's Institute of Mathematics and Informatics, specifically within the Interdisciplinary Statistical Research Group. He serves as the Head of the Applied Statistics Department and has been working at the Institute since 1978. His academic credentials include a Doctor of Mathematics from Vilnius University (1978), a Habilitation Doctor from the Institute of Mathematics and Informatics (1993), and Professor title from Vytautas Magnus University (1996). Dr. Rudzkis has extensive educational background, having graduated from Kaunas Polytechnic Institute in 1973 with a specialization in computing technology and engineering mathematics. His professional journey began as an assistant at KPI (1973-1974), followed by postgraduate studies at the Institute of Mathematics and Informatics (1974-1977). His research primarily focuses on probability theory , mathematical statistics , and econometric modeling . He has developed methods for data clustering, nonparametric density estimation, and created mathematical models of Lithuanian macroeconomic indicators using VAR methodology. His work spans theoretical developments in statistical decision algorithms and practical applications in economic forecasting. Analysis of his recent publications reveals a consistent focus on statistical methodology development, particularly in goodness-of-fit testing, multivariate analysis, and applications to economic and financial data. His research shows a clear progression from theoretical probability work toward increasingly applied econometric modeling, with significant emphasis on Baltic region economic analysis in recent years. Dr. Rudzkis maintains significant professional engagement through multiple editorial roles, including membership on the editorial boards of 'Lithuanian Mathematical Collection' (since 1999), 'Lithuanian Statistical Works' (since 2000), and 'Money Studies' (since 2000). He has also served as Editor for proceedings of the '8th International Conference on Probability Theory and Mathematical Statistics'. His academic service includes leadership as Head of the Applied Statistics Department Seminar, membership on the program committee for international conferences, and serving as an Expert member of the Lithuanian Academy of Sciences since 1994. He has also been actively involved with professional societies, serving on the boards of both the Lithuanian Mathematical Society and Lithuanian Statistical Union since 1998. Dr. Rudzkis teaches probability theory, mathematical statistics, and specialized courses including multivariate statistics, time series analysis, correlation-regression analysis, and econometrics at multiple universities including VDU, KTU, and VGTU.
Dalia Breskuvienė is a PhD student and junior researcher at the Cognitive Computing Group of Vilnius University’s Institute of Data Science and Digital Technologies. Her research spans Machine Learning, Data Mining, and Fraud Detection , focusing on optimizing classifier training for highly imbalanced data in financial contexts. Doctoral Studies : Enrolled from 2021 to 2025 under supervisor Prof. Gintautas Dzemyda. Research Focus : Addresses challenges in fraud detection by innovating feature selection, encoding, and training strategies for imbalanced datasets. Publications : Explores autoencoders, clustering techniques, and concept drift in machine learning models. Conferences : Presented her work at annual Computer Science Engineering PhD Student Conferences in Vilnius (2022–2023).
Dr. George Dounias is a researcher at the Department of Financial and Management Engineering, University of the Aegean. His work spans computational intelligence, machine learning, and soft computing with applications in medical diagnosis, industrial systems, and disaster preparedness. Research Keywords: Inductive machine learning Artificial intelligence in engineering management Fault diagnosis in complex systems Medical decision making with AI Recent publications highlight his focus on: Hybrid ML for evacuation behavior analysis Neural networks in production line throughput estimation Computational intelligence for polyneuropathy and breast lesion detection
Bengisu Yilmaz is a Researcher at the Ultrasound Research Institute of Kaunas University of Technology (KTU), actively pursuing her PhD while leading the Marie Curie-funded "NDT on Air" project focused on non-destructive evaluation of aircraft bonding structures. Her work aims to reduce aircraft weight through improved adhesive testing methodologies, contributing to environmentally sustainable aviation solutions. Yilmaz holds a Mechatronics Engineering degree from an Istanbul private university where she served on the gender awareness committee, implementing anti-stereotype training for high school educators. Her academic journey reflects a deliberate shift from initial interests in political studies and medicine toward engineering, driven by her passion for tangible creation and handicraft. Her research integrates aerospace engineering, materials science, and electronics within interdisciplinary teams of physicists and chemists, emphasizing the critical role of visual communication in science. She pioneered approaches linking art education to technical presentation, arguing that effective image-based communication enhances research dissemination in non-destructive testing applications. As a Marie Curie Fellow, Yilmaz advocates for expanded diversity beyond gender to include LGBT communities, disabled researchers, and international perspectives, believing multidimensional viewpoints drive technological innovation. She challenges the notion that women must outperform men in STEM, instead promoting self-improvement over comparison while noting improving gender ratios from undergraduate (30% women) to PhD levels (50% women) though senior roles remain male-dominated. Yilmaz manages significant project responsibilities including international travel for the "NDT on Air" initiative while navigating financial constraints common to non-wealthy PhD candidates. Her future plans remain fluid between academia and industry due to her aversion to teaching, with current residence in Lithuania complicated by unfavorable research conditions in her native Turkey and workplace sexism regarding appearance-based judgments.
Prof. habil. dr. Gintautas Dzemyda is a leading Lithuanian computer scientist, Professor and Senior Researcher at Vilnius University Institute of Data Science and Digital Technologies (VU DMSTI), and Head of the Cognitive Computing Group . He is simultaneously affiliated with the Institute of Mathematics and Informatics (MII) in Vilnius, where he has built an internationally recognized scientific school in visual data analysis. Education & Qualifications 1984 – Candidate of Technical Sciences (PhD equivalent), thesis on “Problem Structure Analysis – a Tool for More Effective Optimization”. 1997 – Habilitation Doctor of Technical Sciences, dissertation on “Isolation of Necessary Knowledge to Improve Optimization Efficiency”. 1992 – Associate Professor, Institute of Mathematics and Informatics, Vilnius. 1998 – Professor, Kaunas University of Technology. Research Interests Prof. Dzemyda’s research integrates data science, artificial intelligence, optimization, and cognitive computing . Core topics include dimensionality reduction, multidimensional data visualization, neural-network–based analytics, parallel and distributed computing, multi-criteria decision support, and advanced AI applications in medicine (ophthalmology, cardiology, oncology). His work has pioneered Lithuanian capabilities in visual analytics and large-scale data exploration. Publication Trends Across 270+ refereed works and 2 Springer monographs (2013, 2023), recent outputs (2021–2025) emphasize geometric multidimensional scaling for big-data visualization, deep learning for pancreatic-cancer detection on CT images, reinforcement learning for autonomous navigation, and fraud-detection techniques for highly imbalanced financial datasets. These contributions appear in Springer LNCS/LNNS, Informatica, Journal of Global Optimization, Engineering Applications of Artificial Intelligence , and other top venues. Awards & Recognition Lithuanian State Science Prize (2001 & 2021) Honorary Doctor of the University of Latvia (2019) Knight's Cross of the Order “For Merit to Lithuania” (2007) Doctoral Supervision & Committees He has mentored 28 doctoral graduates (15 direct, 13 through academic descendants) and currently supervises: Dalia Breskuvienė – Classifier training-set optimization Modestas Motiejauskas – Emotion recognition in photographs Victor Bulava – Machine-learning methods for cyber-incident early detection He also chairs or serves on doctoral and habilitation committees at VU, KTU, VGTU, VMU and MII. Laboratory & Projects As Head of the Cognitive Computing Group , Prof. Dzemyda coordinates several national and EU projects, including the current Lithuanian Research Council grant “Geometric Method for Multidimensional Scaling” (S-MIP-20-19, 2020-2022) and the SMART programme project “CognitiveSTATS” (2021-2023) focused on combating misinformation during pandemics. His team develops open-access tools for large-scale data visualization and contributes to the MIDAS national research-data archive.
Prof. Dr. Remigijus Paulavičius is a Senior Researcher, Professor, and Group Leader at the Blockchain and Quantum Technologies Group of Vilnius University. He earned his Doctor of Science degree in Computer Science in 2010 from Vilnius University's Institute of Mathematics and Informatics, focusing on global optimization with simplex subdomains under the supervision of Dr. J. Žilinskas. Research Interests : His work bridges global optimization methods with blockchain and quantum computing. Key areas include non-convex bilevel programming, derivative-free optimization algorithms (e.g., DIRECT), and applications in blockchain scalability, consensus protocols, and quantum circuit design. Scientific Awards : Best Paper Award, Journal of Global Optimization (2014) Funding from Lithuanian State Science and Studies Foundation for doctoral students (2007–2009) Member, Young Academy of the Lithuanian Academy of Sciences (since 2019) Projects & Leadership : Leads research on blockchain simulators, quantum machine learning frameworks, and optimization toolboxes (e.g., DGO, DIRECTGO). Involved in international collaborations and conference committees, including AIChE and EUROPT. Publications : Over 70 works on global optimization algorithms, blockchain applications, and quantum computing, with recent focus on generative AI, energy consumption in blockchains, and reproducibility in machine learning.
Dr. Ernestas Filatovas is a Senior Researcher and Chief Researcher in the Project at Vilnius University's Institute of Data Science and Digital Technologies (formerly Institute of Mathematics and Informatics), where he has been affiliated since 2013. He leads the Blockchain and Quantum Technologies Group, focusing on cutting-edge research at the intersection of quantum computing, blockchain, and artificial intelligence. Previously, he served as an Associate Professor and Lecturer at Vilnius Gediminas Technical University's Faculty of Fundamental Sciences from 2013 to 2019. Dr. Filatovas earned his Doctor of Technology in Computer Science Engineering from Vilnius University Institute of Mathematics and Informatics in 2012. His dissertation, supervised by Prof. Dr. Olga Kurasova, focused on the interactive solution of multi-criteria optimization problems. His research spans multiple high-impact domains, with particular expertise in blockchain technologies, quantum computing, artificial intelligence, and machine learning. He has pioneered work in quantum blockchain implementations, reproducibility of AI research through blockchain verification, and quantum machine learning applications. His research bridges theoretical computer science with practical applications in financial markets, healthcare, and distributed systems. His extensive publication record—over 50 scientific papers, with more than 25 in Clarivate Analytics-indexed journals—demonstrates consistent productivity and international collaboration. Recent work shows a clear trajectory toward quantum-enhanced AI systems, blockchain-based research verification frameworks, and quantum algorithms for practical problems. Laureate of the 4th LMA Young Scientists' Conference (2014) INFOBALT scholarship 2nd place winner (2014) Lithuanian State Science and Studies Foundation funding recipient (2009, 2010) Recognized as one of Lithuania's most active doctoral students Master's degree with honors (2006) Dr. Filatovas leads multiple significant research projects, including the 2021-2024 project 'Solving the problems of reproducibility of scientific research in the field of artificial intelligence using blockchain technologies' as team leader, and the 2023-2027 project 'Development and validation of quantum machine learning methods using prepared datasets' as Chief Researcher. He has also contributed to international collaborations such as the Spanish-funded 'High Performance Solutions for Modern Scientific Computing Challenges' (2019-2021). His popular science contributions, including the VU news portal article 'Quantum Computing: Who and Why?', demonstrate his commitment to science communication. As a key member of Vilnius University's Blockchain and Quantum Technologies Group, Dr. Filatovas contributes to Lithuania's growing reputation in quantum computing research and blockchain innovation, working closely with international collaborators across Europe.
Professor Matviychuk Andriy Viktorovych is a leading academic and practitioner at Vadym Hetman Kyiv National Economic University, where he serves as Professor at the Department of Mathematical Modeling and Statistics. He holds multiple concurrent roles including General Director of the KNEU Science Park and Director of the Institute of Modeling and Information Technologies in Economics. He has 24 years of scientific and pedagogical experience. His research focuses on artificial intelligence, complex system modeling, financial time series forecasting, and economic risk modeling using neuro-fuzzy technologies. His recent publications demonstrate expertise in environmental modeling, human capital analysis, and financial market dynamics. 2024 - Editor-in-Chief of "Neuro-fuzzy modeling technologies in economics" (Scopus-indexed) 2024 - Completed advanced training in Data Science at KNEU 2024 - International scientific and pedagogical internships Scientific awards include: Honorary sign 'For Scientific and Educational Achievements' (2018) Honorary sign 'Outstanding Educator of Ukraine' (2015) President of Ukraine grants for young scientists (2014, 2012) National Academy of Sciences of Ukraine Prize (2006) M.Fedorenko International Scientific Fund grants (2009, 2004) He serves as head of the Specialized Academic Council for doctoral dissertation defenses and participates in multiple editorial boards of Ukrainian academic journals. His language proficiency includes English, and his personal interests span history, philosophy of religion, and skiing.
