جست‌وجوی استاد و پژوهشگر

Audronė Jakaitienėمشاهده پروفایل

عضو هیئت علمی

• Statistics
• Biostatistics
• Machine Learning
• +۵ مورد دیگر

Prof. Dr. Audronė Jakaitienė is a Senior Researcher and Group Leader at the Interdisciplinary Statistical Research Group of Vilnius University’s Institute of Data Science and Digital Technologies. She supervises doctoral students in Computer Science Engineering (T 007) and Informatics (N 009) , focusing on topics like machine learning in neuro-oncology and genetic variation analysis. Her research spans education systems benchmarking, biomedical statistics, and big data applications in healthcare. Education : Ph.D. in Economics (2010), Vilnius University Teaching : Courses in Nonlinear Statistical Models (Doctoral), Biostatistics (Medicine), and Multivariate Statistics with R (Master’s) Her recent work examines multimorbidity patterns , COVID-19 modeling , and educational effectiveness through international collaborations like the European Joint Program on Rare Diseases and eduBEST project. She employs advanced statistical methods (R packages, DEA, PCA) for analyzing large-scale datasets. Scientific Awards European Social Fund (2018-2022) - EFECTAS project Research Council of Lithuania (2015-2017) - Education System Modeling European Social Fund (2011-2014) - LITGEN project She has advised 4 doctoral students and contributed to 30+ publications, including studies on hypertrophic cardiomyopathy , educational equity , and prediabetes monitoring . Her methodological expertise includes data envelopment analysis and multidimensional scaling.

Viktor Medvedevمشاهده پروفایل

دانشیار

• Machine Learning
• Deep Learning
• Neural Networks
• +۸ مورد دیگر

Viktor Medvedev is an Associate Professor and Senior Researcher at Vilnius University's Institute of Data Science and Digital Technologies, where he serves as Project Lead Researcher in the Blockchain and Quantum Technologies Group. His work focuses on the intersection of machine learning, data visualization, and cybersecurity applications. Dr. Medvedev earned his Doctor of Science in Computer Science Engineering in 2007 from Vilnius Gediminas Technical University and the Institute of Mathematics and Informatics. His doctoral research centered on 'Research on the application of feedforward neural networks for multidimensional data visualization,' establishing the foundation for his continued work in data analysis and visualization techniques. His research interests span machine learning , deep learning , data visualization , and cybersecurity applications . Specifically, he has made significant contributions to keystroke dynamics authentication , behavioral biometrics , dimensionality reduction techniques , and medical data analysis . His work often bridges theoretical computer science with practical applications in security and healthcare domains. Dr. Medvedev's publication record demonstrates a consistent trajectory of research excellence, with recent work focusing on advanced authentication systems using deep learning, pancreatic cancer detection through machine learning, and innovative approaches to data visualization. His research shows a clear evolution from foundational work in neural networks for data visualization to contemporary applications in cybersecurity and medical diagnostics. ICAISC'06 - Best Presentation Award (The 8th International Conference on Artificial Intelligence and Soft Computing) ICANNGA 2007 - Best Young Researcher Paper Award in Neural Networks DAMSS 2021, 2022, 2023 - Best Poster Awards As a Project Lead Researcher in the Blockchain and Quantum Technologies Group, Dr. Medvedev oversees research initiatives that combine cutting-edge technologies with practical applications. His work in cybersecurity has particular relevance to critical infrastructure protection, where insider threat detection using behavioral biometrics represents a significant contribution to the field.

Laura Ringienėمشاهده پروفایل

پژوهشگر

• Educational Data Analysis
• Statistical Modeling
• Multilevel Regression
• +۴ مورد دیگر

Laura Ringienė is a Researcher and Information Technology Administrator at Vilnius University's Institute of Data Science and Digital Technologies, where she works in the Interdisciplinary Statistical Research Group. She holds a PhD in Computer Science Engineering obtained in 2014 from Vilnius University's Institute of Mathematics and Informatics. Her professional activities span research, teaching, and technical administration within the academic environment. Dr. Ringienė earned her educational credentials at Vilnius Pedagogical University, completing her Bachelor's degree in Informatics in 2006 and Master's degree in Informatics in 2008, both from the Faculty of Mathematics and Informatics. Her doctoral dissertation, Hybrid Neural Network for Multidimensional Data Visualization , was defended in 2014 and falls under Technological Sciences, Computer Science Engineering. Her research focuses on statistical analysis of educational data, with particular expertise in multilevel regression, structural equation modeling, and analysis of international large-scale assessment data (PISA, TIMSS, PIRLS). She has developed the R program package EFECTAS specifically designed for analyzing international education research data. Her work examines educational equity, reading achievement gaps across EU countries, socioeconomic factors affecting student performance, and the impact of preschool education on later academic outcomes. Analysis of her publication record from 2020-2025 reveals a strong concentration on educational data science, with recurring themes of statistical modeling of international assessment data, educational equity analysis, and application of advanced statistical techniques to understand educational systems across Europe. Her work frequently employs PISA, TIMSS, and PIRLS datasets to examine cross-national educational patterns and policy implications. Dr. Ringienė has been actively involved in the EFECTAS project (2018-2022), which was financed by the European Social Fund under a grant agreement with the Research Council of Lithuania. This project focused on analyzing the efficiency and productivity of education systems in EU countries using secondary large-scale data. She teaches undergraduate courses at Vilnius University, including Big Data Analytics and Business Process Robotization since 2019. Her professional development includes extensive training in Robotic Process Automation (UIPath), Structural Equation Modeling, Multilevel Linear Modeling, and various Big Data technologies. She is part of the Interdisciplinary Statistical Research Group, which focuses on applying advanced statistical methods to solve complex problems in education and other domains.

• Data Science
• Artificial Intelligence
• Machine Learning
• +۷ مورد دیگر

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.

• Computer Science
• Multidimensional Data Visualization
• Visualization Quality Assessment
• +۴ مورد دیگر

Dr. Rasa Karbauskaitė is a Researcher at the Cognitive Computing Group within Vilnius University's Institute of Data Science and Digital Technologies . She holds a Doctor of Computer Science degree (2010) and specializes in multidimensional data visualization, dimensionality reduction, and intrinsic dimension estimation. Her work combines geometric and statistical methods to analyze high-dimensional datasets. PhD: Computer Science (2010), focusing on local structure preservation in multidimensional data visualization Advanced Training: B2.1 English language course (2016) Research interests include: Fractal dimension analysis for speech emotion classification Manifold learning and topological preservation Optimization of maximum likelihood estimators for dimensionality reduction Geodesic distance applications in data structure analysis Nonlinear data projection algorithms Scientific contributions show a focus on Developing visualization quality assessment frameworks Advancing dimensionality reduction techniques Fractal-based feature selection for emotion recognition Comparative analysis of intrinsic dimension estimation methods Parameter optimization in manifold learning algorithms Awards : Lithuanian Academy of Sciences Young Scientists' Research Prize (2011) Professional Roles : Managing Editor of the Informatica journal Participant in international conferences like Data Analysis Methods for Program Systems (2011-2015) Contributor to IEEE proceedings and specialized workshops

• Multidimensional Scaling
• Geometric Optimization
• Data Visualization
• +۳ مورد دیگر

Martynas Sabaliauskas is an Associate Professor and Researcher at the Cognitive Computing Group , Institute of Data Science and Digital Technologies , Vilnius University , Lithuania. His research focuses on multidimensional scaling, geometric optimization, data visualization, and prime number theory. His research interests include: Multidimensional Scaling (MDS): Developing geometric approaches for efficient data dimensionality reduction and visualization. Prime Number Theory: Investigating properties of the Riemann zeta function, prime sequences, and fractal structures. Computational Mathematics: Designing algorithms for complex mathematical problems with applications in data science. Recent publications reflect a strong focus on geometric MDS techniques, visualization of complex mathematical structures, and computational approaches to number theory. Notable works include studies on the Riemann zeta function's zeros, fractal visualizations, and efficient algorithms for large-scale data analysis. Contact Information: Address: Akademijos St. 4, room 617, Vilnius, Lithuania Phone: +370 5 210 9305