Mindaugas Bloznelis is a Professor at Vilnius University's Faculty of Mathematics and Informatics. His academic career spans decades, with active involvement in teaching and research as evidenced by his continuous presence in the university's schedules from 2016/2017 to 2025/2026. Professor Bloznelis specializes in Random Graph Theory , Network Analysis , and Probability Theory . His research focuses on connectivity thresholds, clustering coefficients, and statistical approximations in complex networks. Recent work includes advancements in modeling sparse and clustered dynamic networks, connectivity thresholds for Bernoulli random graph superpositions, and Edgeworth approximations for symmetric statistics. He co-authored the 2025 Modelling and Mining Networks workshop proceedings, highlighting his leadership in network analysis. His publications demonstrate deep expertise in stochastic processes, asymptotic theory, and finite population statistics. While no formal awards or student advisories are listed in the provided texts, his extensive publication record underscores significant contributions to probability and network science.
Prof. habil. dr. Jonas Kazys Sunklodas serves as an Affiliated Professor in the Interdisciplinary Statistical Research Group at Vilnius University's Institute of Data Science and Digital Technologies. His academic career spans decades of rigorous research in probability theory and mathematical statistics, maintaining an active publication record through 2023. His research focuses on asymptotic analysis of stochastic processes , particularly examining normal approximation techniques for various dependent and independent random variable structures. Key contributions include advancements in central limit theorems for φ-mixing processes, m-dependent sequences, and random sums with applications in theoretical statistics. Analysis of his 15 most recent publications reveals consistent specialization in convergence rates , distributional approximations , and limit theorems across diverse stochastic models. His methodological innovations frequently employ characteristic function analysis and L p norm convergence metrics. While no formal awards or student advisement records appear in the provided documentation, his sustained scholarly output demonstrates significant contributions to probability theory. His editorial work on Vytautas Statulevičius' Selected Mathematical Papers (2006) further establishes his standing in Lithuania's mathematical community. Professor Sunklodas maintains institutional affiliation through Vilnius University's Akademijos St. 4 facility (room 220), though no active research grants or laboratory affiliations are documented in the current materials. His textbook Tikimybių teorijos kursas (2003) remains a notable educational contribution to Lithuanian mathematics pedagogy.
Vaidotas Kanišauskas is an Assistant Professor at Vilnius University Šiauliai Academy , where he has served as a Lecturer since January 2, 2021. Previously, he held academic roles at Šiauliai University from 1989 to 2020, including Head of the Department of Mathematics (2002–2008), Associated Professor (2001–2019), and Senior Assistant (1994–1999). His research focuses on the asymptotic theory of estimators for random processes , hypothesis testing, prediction, point processes, and large deviations. He also explores interdisciplinary applications of mathematics in history, archaeology , and internal migration . He teaches courses in Probability theory , Mathematical statistics , Theory of Processes , Reliability analysis , and Optimization methods . He holds a Doctor’s Degree in Physical Sciences (Mathematics) from the Institute of Mathematics and Informatics and Vytautas Magnus University, along with a Master’s and Bachelor’s in History from Šiauliai University.
Dr. Mindaugas Juodis is a Researcher at the Blockchain and Quantum Technologies Group within Vilnius University's Institute of Data Science and Digital Technologies. His work centers on advanced statistical modeling of blockchain systems and decentralized networks. His research spans blockchain, cryptocurrency, quantum computing, and probability theory. Key investigations include Bitcoin price regime shifts using Bayesian methods, Ethereum transactional decentralization metrics, and wealth distribution analysis in blockchain networks. Earlier theoretical work focused on self-normalized sums, central limit theorems, and functional limit theorems for dependent processes. Publications reveal a clear trajectory from theoretical statistics (2004-2007) to applied blockchain analytics (2024-2025). Recent work bridges mathematical rigor with cryptocurrency applications, appearing in Mathematics, ICT Express, and IEEE conferences. His research demonstrates expertise in translating complex statistical frameworks to real-world decentralization problems. As part of the Blockchain and Quantum Technologies Group, he contributes to cutting-edge research in blockchain analytics and quantum computing intersections, focusing on empirical validation of decentralization metrics and network properties.
Dr. Marijus Vaičiulis is a researcher at the Institute of Mathematics and Informatics (Vilnius University) , affiliated with the Interdisciplinary Statistical Research Group . He holds a Doctor of Mathematics degree (awarded in 2004 by Vilnius Gediminas Technical University and MII). Education Bachelor's degree in Mathematics and Computer Science from Šiauliai University (1995) Master's degree in Mathematics and Computer Science from Šiauliai University (1997) Research interests focus on random processes with distant dependence and applied statistics , particularly in extreme value theory, tail index estimation, and network analysis. His work bridges theoretical probability with practical applications in stochastic processes and heavy-tailed data modeling. Publications demonstrate expertise in tail index estimation techniques, evolving random graph analysis, and extreme value statistics. Collaborations with Natalia Markovich and Maxim Ryzhov highlight interdisciplinary approaches to network theory and statistical inference. Pedagogical activities include teaching probability theory , mathematical statistics , and random processes at Šiauliai University and Vilnius University. Current affiliation is with the Interdisciplinary Statistical Research Group at Vilnius University's Institute of Mathematics and Informatics. His work address is Akademijos St. 4, room 218, Vilnius.
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.
Kanišauskienė Karolina is an Assistant and Lecturer at Vilnius University Šiauliai Academy. Her academic career spans roles as an Assistant (2007-2014), Lecturer (2014-2017, 2021-present), and Associate Professor (2017-2020) at Šiauliai University and its successor institution. Doctoral studies in Mathematics at Vilnius University Multiple degrees in Mathematics and Humanities from Šiauliai University Her research focuses on random processes and their prediction , statistical analysis for olympiads , and graphical models with linguistic applications . She teaches courses in Graph Theory , Algorithms , and Statistical Modeling . Key affiliations include: Institute of Education Researchers Researchers' Excellence Network (RENET) Doctoral Studies at Vilnius University
Saulius Norvidas is an Associate Professor and Senior Researcher at Vilnius University's Faculty of Mathematics and Informatics, working within the Interdisciplinary Statistical Research Group at the Institute of Data Science and Digital Technologies. His academic career spans over four decades, with significant contributions to mathematical analysis. His educational background includes: Graduated from the Faculty of Mechanics and Mathematics of Moscow University in 1980 Candidate of Physical and Mathematical Sciences (1986, Moscow University) Doctor of Mathematics (1993, Research Council of Lithuania) Norvidas specializes in harmonic analysis, theory of functions of a complex variable, Banach algebra, and operator theory. His research primarily focuses on characteristic functions, their properties, extensions, and applications in probability theory. He has made significant contributions to understanding the relationship between characteristic functions and their analytic properties, including uniqueness theorems, extrapolation problems, and the behavior of powers of characteristic functions. His work often bridges pure mathematics with applications in signal processing and statistical inference. An analysis of his recent publications reveals a consistent focus on characteristic functions and harmonic analysis, with particular attention to their analytic continuation properties, extension problems, and applications in reconstruction theory. His work demonstrates deep connections between complex analysis, functional analysis, and probability theory, with numerous papers exploring the intricate relationship between the analytic properties of characteristic functions and their underlying probability distributions. Norvidas is an active member of both the Lithuanian and American Mathematical Societies, reflecting his international engagement in the mathematical community. He has contributed to significant research projects, including the Lithuanian State Science and Studies Foundation project "Function and Measure Reconstruction Problems". His academic career progression shows steady advancement from assistant positions at Vilnius University (1980-1997) to Senior Research Fellow at MII (since 1993), culminating in his appointment as Associate Professor at Vilnius University (since 1997). This trajectory demonstrates his sustained contribution to mathematical research and education over several decades. Norvidas works within the Interdisciplinary Statistical Research Group, which suggests collaboration across mathematical disciplines and potential applications of his theoretical work to statistical problems and data science applications.
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.
Mindaugas Stoncelis is an Assistant Professor at Vilnius University specializing in Analytic Number Theory and Zeta Function Theory. He earned his Doctor of Science in Mathematics from Vilnius University in 2019. Prior to his academic role, he has been a University lecturer since 2002 and served as a University IT Project Manager since 2015. His research focuses on universality properties of zeta and L-functions, periodic zeta-functions, Mellin transforms, and Dirichlet series. His work includes groundbreaking studies on weighted universality, discrete universality, and approximation theorems related to zeta functions. Recent contributions (2025-2017) explore Mellin transforms of Riemann zeta functions and Dirichlet series universality. Earlier research (2014-2008) addressed zeta function zeros, numerical approximation methods, and applications in e-commerce analytics. Publications span high-impact journals like Axioms and Mathematical Modelling and Analysis . He actively participates in international conferences such as MMA series and Palanga conferences on analytic number theory.
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.
Tadas Žvirblis serves as an Associate Professor within the Interdisciplinary Statistical Research Group at Vilnius University's Institute of Data Science and Digital Technologies, maintaining his research office at Akademijos St. 4, room 604A in Vilnius. His academic profile bridges theoretical statistics with practical engineering and medical applications through advanced computational methodologies. His research program centers on machine learning and deep learning innovations for complex signal analysis, with dual specializations in biomedical diagnostics (EEG, NIRS, cardiovascular monitoring) and industrial systems (conveyor mechanics, gear fault detection, engine emissions). Key methodological contributions include novel data augmentation techniques for time series, generative modeling of vibration signals, and prognostic frameworks for reliability engineering, demonstrating consistent interdisciplinary collaboration across medical and engineering domains. Analysis of his 13 publications from 2023-2025 reveals a strategic research trajectory applying deep learning to data-scarce scenarios, particularly in biomedical signal interpretation and industrial predictive maintenance. His work shows increasing focus on clinical applications since 2024, including ECMO mortality prediction and aortic morphology studies, while maintaining strong industrial engineering output through IEEE conference publications on conveyor systems and engine diagnostics. Dr. Žvirblis actively supervises doctoral research as Senior Researcher for Gajane Mikalkėnienė's project (2023-2027) developing EEG-based depression diagnosis methods under Informatics field N 009. His grant portfolio includes multiple industry-collaborative projects evidenced by co-authorship with clinical researchers and engineering teams across Lithuania, Poland, and Germany. As a core member of the Interdisciplinary Statistical Research Group, he contributes to the unit's mission of advancing statistical methodologies for real-world data challenges, with particular emphasis on time-series analysis in non-stationary environments. His laboratory work integrates signal processing hardware with deep learning frameworks to address industrial automation and medical monitoring challenges.
