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.
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.
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.
Benjamin Emmich is a Visiting Lecturer at the International School of Management (ISM) and holds the L. Charles Hilton Jr. Fellowship. He is a Ph.D. candidate in Economics at Florida State University, where he focuses on applied microeconomics with emphasis on Law and Economics and Public Finance. His research employs causal inference methods to analyze criminal justice system impacts, particularly the roles of attorneys and judges in criminal outcomes. Education: Ph.D. Candidate in Economics, Florida State University Master of Science in Economics, Florida State University Bachelor of Science in Physics and Mathematics (Minors: Economics, Statistics), Mississippi State University Teaching areas include Econometrics, Quantitative Methods, and Microeconomics. He instructs courses such as Mathematical Finance (GRAE017) and Introduction to Public Choice Theory (POL149). His research explores intersections between legal institutions and economic outcomes, contributing to public policy analysis. Awards: L. Charles Hilton Jr. Fellowship No advising or grant information is provided. Current lab/ team affiliations are unspecified.
Dr. Quentin Gallea is a Visiting Lecturer at the International School of Management (ISM) and holds roles as a Senior Researcher and Lecturer at the University of Lausanne and École Polytechnique Fédérale de Lausanne (EPFL). His expertise centers on statistics, causal inference, and econometrics, with a focus on developing tools to enhance critical thinking around data interpretation. He advocates for a “Causal Mindset” methodology, detailed on his platform The Causal Mindset . His teaching emphasizes defining, measuring, and interpreting key performance indicators (KPIs), having instructed over 10,000 students across economics, management, and in vivo medical research fields. His research addresses societal challenges, such as the impact of weapon exports on African conflict risks and the effects of COVID-19 lockdowns. Publications appear in journals like Proceedings of the National Academy of Sciences (PNAS) , Management Science , and Environmental Research Letters . He actively shares insights via LinkedIn, TedX talks ( How to Question Numbers and Prevent Manipulation ), and articles on platforms like Towards Data Science. Key contributions include bridging statistical theory with practical applications, promoting data literacy, and critiquing misinformation in quantitative analyses.
Dr. Andrius Čiginas is a Senior Researcher at the Interdisciplinary Statistical Research Group , part of the Institute of Data Science and Digital Technologies (VU DMSTI) at Vilnius University . His work focuses on advancing survey statistics, small area estimation, and the integration of non-probability samples into official statistics. Research Focus Dr. Čiginas specializes in developing statistical methodologies for improving the accuracy and efficiency of population estimates, especially in small domains. His research includes: Composite estimation techniques Non-probability sample integration Real-time prediction using social media and administrative data Bootstrap and Edgeworth approximations for finite populations Doctoral Supervision He currently supervises two doctoral students: Akvilė Vitkauskaitė – Informatics (2024–2028): Parameter estimation and forecasting in population domains using non-probability samples. Ieva Burakauskaitė – Mathematics (2022–2026): Use of additional information in estimating parameters in population domains. Scientific Contributions Dr. Čiginas has published extensively in top-tier journals such as Journal of Official Statistics , Statistics , and Nonlinear Analysis: Modelling and Control . His recent work explores the integration of non-probability data sources into official statistics, consumer confidence estimation, and small area estimation techniques.
