جستجوی استاد راهنمای دانشگاهی

Vygintas Gontisمشاهده پروفایل

استاد پژوهش

• Statistical physics
• Dynamically chaotic systems
• Econophysics
• +۱ مورد دیگر

Vygintas Gontis is a Research Professor at Vilnius University's Faculty of Physics, working at the Experimental nuclear and particle physics center. His research spans statistical physics, dynamically chaotic systems, econophysics, and agent-based stochastic modeling with significant contributions to financial market analysis. Professor Gontis has developed influential theoretical frameworks applying physics principles to financial phenomena, creating stochastic models that explain volatility patterns, return intervals, and long-range memory effects in markets. His interdisciplinary work bridges statistical physics and economic theory, revealing universal patterns in complex market behaviors. Stochastic modeling of financial markets Long-range memory phenomena Agent-based computational finance 1/f noise analysis Complex systems dynamics Econophysics applications His publication record shows consistent scholarly output from 1987 through 2016, with recent work focusing on financial applications while maintaining connections to fundamental physics concepts. The research demonstrates evolution from quantum physics topics toward interdisciplinary financial modeling. Professor Gontis has supervised doctoral student Aleksejus Kononovičius (2011-2015) and postdoctoral researcher Rytis Kazakevičius (2020-2022). He leads the Group of Complex Physical and Social Systems and has held significant international roles including national representative for COST projects on Physics of Risk (2003-2008) and Physics of Competition and Conflicts (2008-2012).

• Wireless networks
• Gravitation
• Computer networks

Dr. Kęstutis Svirskas is a researcher at the Institute of Applied Electrodynamics and Telecommunications (IAET) at Vilnius University. His work bridges wireless network technologies with theoretical physics, particularly gravitation and particle dynamics. Research Interests include wireless networks, gravitation, and computer networks. He has contributed to electromagnetic compatibility studies and channel modeling for next-generation telecommunications. His article trends reveal interdisciplinary work, combining gravitational theory (e.g., Kerr black holes) with practical wireless network challenges (e.g., LTE performance, DVB-T compatibility, LOS/NLOS channel models). Teaching involves courses like Theory of Relativity, Routing in Local Area Networks, and Computer Communication Technologies at the bachelor's level. He also supervised thesis projects on anechoic chamber calibration and LTE network analysis. Scientific activities include translating Stephen Hawking and Leonard Mlodinov's The Grand Project into Lithuanian, contributing to science education through academic projects.

• harmonic analysis
• theory of functions of a complex variable
• Banach algebra
• +۵ مورد دیگر

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.

Povilas Treigysمشاهده پروفایل

استاد

• Digital Signal Processing
• Machine Learning
• Deep Learning
• +۳ مورد دیگر

Professor Povilas Treigys is a Senior Researcher and Group Leader at the Image and Signal Analysis Group within the Institute of Data Science and Digital Technologies at Vilnius University's Faculty of Mathematics and Informatics. With extensive experience in digital signal processing and machine learning applications, he leads research efforts in medical image analysis, speech processing, and maritime traffic modeling. Dr. Treigys earned his Doctor of Science in Computer Science Engineering in 2010 with a dissertation on "Development and application of graphical methods for analyzing ophthalmological and thermovision data." His academic journey has been marked by significant contributions to interdisciplinary research connecting computer science with medical applications. His research primarily focuses on digital signal processing across multiple domains including medical imaging (MRI, eye fundus), audio signals, and maritime traffic data. A key emphasis of his work is the development and application of deep learning methods to solve real-world problems in healthcare diagnostics, retail automation, and transportation safety. His recent work demonstrates a strong trend toward explainable AI in medical applications and sophisticated time series analysis for prediction tasks. Professor Treigys serves in numerous leadership roles including as a EuroHPC JU Board Member representing Lithuania, VU MIF representative on the Lithuanian Quantum Technology Association board, and as a delegate to multiple professional committees. He is an active reviewer for several prestigious journals including Computer Science, Nonlinear Analysis, Baltic Journal of Modern Computing, MDPI Sensors, and MDPI Electronics. His laboratory focuses on bridging theoretical machine learning advancements with practical applications, particularly in medical diagnostics where his team has made significant contributions to prostate cancer detection, arrhythmia classification, and ophthalmological image analysis. The group maintains strong international collaborations and regularly presents findings at major conferences in computer vision, medical imaging, and artificial intelligence.

• Stochastic Programming
• Mathematical Modeling
• Financial Mathematics
• +۷ مورد دیگر

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.

• Wireless Network Technologies
• Microwave Electrodynamics
• Antenna Theory
• +۶ مورد دیگر

Rimvydas Aleksiejūnas is an Assistant Professor at Vilnius University's Institute of Applied Electrodynamics and Telecommunications (IAET) and affiliated with the Department of Radiophysics . His work focuses on Wireless Network Technologies , Microwave Electrodynamics , and Antenna Theory . Research Interests : Radio Wave Propagation, GNSS Systems, MIMO Channel Modeling, Electromagnetic Compatibility, Antenna Design, Radio Interference Analysis Recent Publications : 15+ works on wireless channels, UAV detection systems, and microwave engineering from 2011-2022 Awards : Galileo Masters 2020 prize (with S. Rudys and P. Ragulis) Best lecturer of Physics faculty 2018

• 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

• Machine Learning
• Deep Learning
• Statistical Signal Processing
• +۳ مورد دیگر

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.

• Stochastic Analysis
• Partial Differential Equations
• Mathematical Statistics
• +۲ مورد دیگر

Prof. habil. dr. Remigijus Mikulevičius serves as an Affiliated Scientist at the Interdisciplinary Statistical Research Group within Vilnius University's Institute of Data Science and Digital Technologies. His research focuses on advanced mathematical theories in stochastic processes and differential equations. Dr. Mikulevičius specializes in Stochastic Analysis , Partial Differential Equations , Mathematical Statistics , and Probability Theory . His work particularly examines stochastic differential equations, integro-differential operators, and nonlinear filtering problems, contributing significantly to theoretical foundations in these areas. Analysis of Dr. Mikulevičius's publications reveals a consistent focus on stochastic partial differential equations and related mathematical frameworks spanning from 2000 to 2014. His research trajectory demonstrates deep engagement with theoretical problems in SPDEs, with methodological approaches incorporating Sobolev spaces, Hölder spaces, and Fourier-Hermite expansions. The publications show particular expertise in Cauchy-Dirichlet problems and nonlinear filtering for both continuous and jump processes. Dr. Mikulevičius maintains a significant collaborative relationship with Henrikas Pragarauskas, with whom he has co-authored multiple influential papers. His work appears in prestigious mathematics journals including Journal of Differential Equations and Stochastic Processes and their Applications, reflecting the high quality and impact of his research contributions.