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.
Dr. Fabio Sgarbossa is a Full Professor of Industrial Logistics at NTNU's Department of Mechanical and Industrial Engineering (MTP), leading the Production Management Research Group and overseeing the Logistics 4.0 Lab. He holds a PhD in Industrial Engineering from the University of Padova (2010) and previously served as an Associate Professor there. His research focuses on industrial logistics design, supply chain management, digitalization in logistics, and human factors in production systems, with contributions to over 150 international publications. He is an Associate Editor for the International Journal of Production Research and actively involved in organizing international conferences. His work integrates advanced technologies like Industry 4.0, additive manufacturing, and AI to address challenges in supply chain resilience, sustainable production, and ergonomic system design. Notable projects include optimizing warehouse systems, evaluating assistive technologies for worker well-being, and developing decision frameworks for spare parts management. His research bridges theoretical models with practical applications, emphasizing human-centered design and circular economy principles. Dr. Sgarbossa’s contributions extend to strategic frameworks for disassembly systems, collaborative logistics in rural contexts, and the mitigation of Lean implementation barriers through digitalization. His Logistics 4.0 Lab serves as a platform for experimental research and education, fostering innovation in smart manufacturing and logistics solutions. He collaborates extensively on EU-funded projects and guides industry partnerships to translate academic insights into actionable strategies.
Tadeusz Trzaskalik is a Professor at the University of Economics in Katowice, specializing in the Department of Operations Research. His work focuses on multiobjective dynamic programming, decision support systems, and stochastic optimization. Research Interests: Development of bipolar methods for multistage decision processes Multiobjective dynamic programming applications in project portfolio selection Stochastic programming for resource allocation and reliability analysis Interactive decision-making procedures with hierarchical criteria Preference modeling in complex economic systems Operations research in business and industrial applications Academic Contributions: Published extensively in journals like Annals of Operations Research, Central European Journal of Operations Research, and Croatian Operational Research Review Authored textbooks on linear programming, integer programming, and decision support systems Developed interactive procedures for multiobjective allocation and scheduling problems
Prof. habil. dr. Leonidas Sakalauskas is an Affiliated Scientist at the Smart Technologies Research Group within the Institute of Data Science and Digital Technologies at Vilnius University. His extensive research career spans mathematical modeling, stochastic programming, and statistical analysis with significant contributions across multiple disciplines. He maintains an active research profile with recent publications extending into 2025. Professor Sakalauskas' research focuses on stochastic programming, mathematical modeling, financial mathematics, statistical analysis, optimization, and queueing theory. His work demonstrates a consistent pattern of interdisciplinary research, bridging theoretical mathematics with practical applications in finance, social sciences, and computer science. He has developed innovative approaches to agent-based modeling, fractal analysis, and Monte Carlo methods, contributing significantly to both theoretical frameworks and practical implementations. His recent publications reveal a continuing evolution of research interests, with increasing emphasis on data science applications, computational social science, and advanced statistical modeling techniques. The articles demonstrate sophisticated methodological approaches to complex problems across diverse domains including financial modeling, social cohesion analysis, and network performance optimization. Throughout his career, Professor Sakalauskas has maintained a strong publication record in high-impact journals and conference proceedings, reflecting his ongoing engagement with the international research community. His work shows consistent methodological rigor combined with practical relevance across multiple application domains. Professor Sakalauskas has collaborated extensively with researchers across Lithuania and internationally, demonstrating strong teamwork and interdisciplinary engagement. His research has practical applications in financial modeling, social science analysis, and computational systems optimization, contributing to both theoretical advances and real-world implementations.
Associate Professor Vaida Bartkutė-Norkūnienė of Utena College is an active researcher in mathematics education, digital transformation, and mentoring communication. Her work bridges technical disciplines with practical applications in education and sustainability. Her primary research interests include: Mathematics education methodologies and computerized learning systems Digital CO2 footprint analysis for websites and e-commerce Cross-national mentoring communication frameworks Labor market policy efficiency in EU contexts Digital innovation adoption in tourism sectors Recent publications demonstrate a clear trajectory toward sustainable digital practices, with significant focus on quantifying environmental impacts of web technologies while advancing educational models through AI integration. Her cross-cultural mentoring studies reveal critical communication patterns between Lithuanian and Latvian academic systems. Dr. Bartkutė-Norkūnienė manages multiple high-impact projects including the National Education Agency's 'Digital Transformation of Education (EdTech)', NORDPLUS 'Network Water Quality and Food Safety', and Erasmus+ 'DICCMEM' communication competence initiatives. She secured Ministry of Education funding for youth programs 'Start a career with an idea' (2021) and 'Code - Keys to Success' (2020). She actively contributes to academic governance as editorial board member of 'Įžvalgos' journal, program committee member for ISC SAI 2022, and Scientific Activities Subgroup member developing ethical AI guidelines for science. Her professional affiliations include the Lithuanian College Mathematics Teachers Association, National Distance Learning Association, and European Working Group on Stochastic Programming.
