جست‌وجوی استاد و پژوهشگر

Julius Žilinskasمشاهده پروفایل

استاد

• Global Optimization
• Multidimensional Scaling
• Parallel Computing
• +۶ مورد دیگر

Professor Julius Žilinskas is a distinguished academic at Vilnius University, serving as Chief Researcher and Group Leader of the Global Optimization Group within the Institute of Data Science and Digital Technologies. He is also a Member of the Senate of Vilnius University and serves on the Expert Committee of Natural and Technical Sciences of the Research Council of Lithuania. His academic career spans multiple institutions including Vilnius Gediminas Technical University and Kaunas University of Technology. Vilnius University, Institute of Data Science and Digital Technologies (Current) Vilnius Gediminas Technical University (Professor appointment, 2012) Kaunas University of Technology (Doctorate awarded, 2002) Professor Žilinskas' research interests focus on global optimization algorithms , multidimensional scaling , and parallel computing for complex optimization problems. His work bridges theoretical computer science with practical applications in facility location, data analysis, and educational assessment. He has made significant contributions to developing and analyzing algorithms for black-box global optimization, particularly in constrained and competitive settings. His recent publications reveal a strong focus on Bayesian optimization techniques , facility location problems , and educational data analysis . The research demonstrates increasing interdisciplinary collaboration, particularly with economists and educational researchers. His work shows a consistent trajectory from theoretical optimization methods to practical applications in diverse fields. Professor Žilinskas has served in numerous leadership roles including: Member of the Senate of Vilnius University Member of the Expert Committee of Natural and Technical Sciences of the Research Council of Lithuania Editor for special issues of the Journal of Global Optimization He leads the Global Optimization Group, which focuses on developing and implementing advanced optimization algorithms for complex real-world problems. The group maintains strong international collaborations and contributes to both theoretical advances and practical applications of optimization techniques.

• Machine Learning
• Medical Imaging
• Computer Science
• +۳ مورد دیگر

Šubonienė Aušra is a junior researcher and PhD student at the Cognitive Computing Group of Vilnius University's Institute of Data Science and Digital Technologies. She works on applying machine learning techniques to medical imaging, particularly for pancreatic cancer detection using CT scans. Field: Computer Science Supervisor: Prof. Dr. Olga Kurasova Study Period: 2023–2027 Research Focus : Her work centers on semi-supervised learning and pseudo-labelling methods for improving diagnostic accuracy in medical imaging. She has contributed to developing deep learning models that identify cut-off points for pancreatic cancer detection, with applications in CT scan analysis. Publication Trends : Recent articles demonstrate expertise in medical image segmentation , neural network optimization , and clinical decision support systems , primarily focused on oncology and radiology.

• Medical Image Analysis
• Machine Learning
• Computer Vision
• +۵ مورد دیگر

Dr. Jolita Bernatavičienė serves as a Senior Researcher at Vilnius University's Institute of Data Science and Digital Technologies within the Image and Signal Analysis Group. With a Doctorate in Technological Sciences (Informatics), she has established herself as a leading researcher in medical image analysis and artificial intelligence applications in healthcare. Her extensive research portfolio spans over 15 years of continuous contributions to the field. Dr. Bernatavičienė's research interests primarily focus on medical image analysis, particularly in ophthalmology and oncology applications. Her work integrates advanced machine learning techniques with medical diagnostics, specializing in eye fundus image analysis for glaucoma detection and prostate MRI analysis for cancer identification. She has made significant contributions to deep learning architectures, signal processing methodologies, and data analysis frameworks applicable to biomedical challenges. Her publication record demonstrates strong trends in applying cutting-edge AI techniques to solve concrete medical problems, with a noticeable shift toward more sophisticated deep learning architectures in recent years. The research spans multiple medical domains including ophthalmology, cardiology, oncology, and renewable energy systems monitoring, reflecting her interdisciplinary approach to data science applications. Leader of International Conference 'Data Analysis Methods for Software Systems (DAMSS)' 2015-2024 Member of IEEE Computer Society section (since 2022) Member of the Council of the Lithuanian Computer Association, Artificial Intelligence Section Member of the Lithuanian Operations Research Society Expert at the Science, Innovation and Technology Agency (MITA) (2020-2022) Dr. Bernatavičienė actively supervises doctoral and master's students, with current doctoral student Roman Surkant working on prostate MRI analysis. She leads multiple significant research projects including 'Developing Talents in Artificial Intelligence to Solve Disruptive Environmental Problems' and serves as scientific leader for the Research Council of Lithuania funded project on cardiac MRI texture analysis. Her work has been supported by various national and international funding mechanisms including COST activities, EuroHPC programs, and Lithuanian national research grants. She is principal investigator for the long-term project developing a database of depersonalized fundus images (2018-2030) and has led numerous projects related to medical image analysis, AI applications in healthcare, and data science methodologies. Her research group maintains strong international collaborations through COST actions and other European research networks.

• Informatics
• Machine Learning
• Computer Networks
• +۳ مورد دیگر

Žydrūnas Vaišnoras is a PhD student, junior assistant, and researcher at Vilnius University's Institute of Data Science and Digital Technologies, working within the Cognitive Computing Group. His research focuses on developing machine learning methods for intrusion detection in computer networks, supervised by Prof. Dr. Olga Kurasova. His work is conducted at Akademijos St. 4, Vilnius, with a study period spanning 2019–2026, including academic vacation periods. PhD Student, Vilnius University JUNIOR ASSISTANT, Cognitive Computing Group RESEARCH INTEREST: Intrusion Detection Systems Vaišnoras has presented his research at multiple doctoral conferences, including the Conference of Doctoral Students in Computer Science (2022) and the Informatics Doctoral Student Conference (2021, 2020). His presentations consistently centered on applying machine learning techniques to enhance network security. Scientific Production: Presentations at 4 doctoral conferences (2020–2022) Thesis topic: "Development of Machine Learning Methods for Detecting Intrusions in Computer Networks"

• 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

• Speech Signal Processing
• Machine Learning
• Natural Language Processing
• +۳ مورد دیگر

Gražina Korvel is a Professor and Senior Researcher at the Image and Signal Analysis Group of Vilnius University's Faculty of Mathematics and Informatics. Her work bridges speech signal processing , machine learning , and natural language processing , with a focus on applications like noise profiling , Lombard effect modeling , and propaganda detection . She leads projects such as the HUMAN-INSPIRED SPEECH ENHANCEMENT (2024–2027) and CLINICAL NLP FOR RECORDS (2024). Education : Doctor of Science in Computer Engineering (2013, Vilnius University), Master’s in Computer Science (2009, Vilnius Pedagogical University), Bachelor’s in Mathematics (2007, Vilnius Pedagogical University). Her research interests include speech enhancement , deep learning for audio analysis , and cross-linguistic emotion recognition . Recent work explores fake news detection , synthetic speech , and Lithuanian language modeling . From 2020–2024, she taught Natural Language Processing at Vilnius University. She has held visiting research roles at Gdańsk University of Technology and International Hellenic University, and serves on editorial boards for Journal of Intelligent Information Systems and Informatica .

• Cognitive Science
• Psycholinguistics
• Speech Processing
• +۴ مورد دیگر

Dr. Gerda Ana Melnik-Leroy serves as Senior Researcher at Vilnius University's Institute of Data Science and Digital Technologies, specializing in the Cognitive Computing Group. Her interdisciplinary work bridges cognitive science, linguistics, and data analytics with significant contributions to understanding human factors in decision-making and language processing. Her academic credentials include: Doctorate in Cognitive Sciences (2019) PhD studies at École Normale Supérieure's Laboratory of Cognitive Science and Psycholinguistics, Paris (2016-2019) Master of Cognitive Science (cognitive psychology major) from École Normale Supérieure, Paris (2014-2016) Melnik-Leroy's research examines cognitive biases in data visualization, second language phonetic acquisition, and synthetic speech perception mechanisms. She investigates how exponential growth misinterpretation affects decision-making and explores perceptual differences between congenitally blind and sighted individuals in speech processing contexts. Her methodological approach combines behavioral experiments with computational modeling to address real-world cognitive challenges. Analysis of her 15 most recent publications (2019-2025) reveals two dominant research trajectories: (1) Cognitive bias mitigation in data visualization, particularly regarding exponential growth misinterpretation and graph design optimization, and (2) Cross-modal speech processing, including L2 phonetic training efficacy and synthetic speech evaluation across diverse user populations. These streams demonstrate consistent integration of cognitive theory with practical data science applications. Within the Cognitive Computing Group, Melnik-Leroy contributes to interdisciplinary collaborations that advance human-centered computing. Her experimental work informs both theoretical models of cognition and applied solutions for educational technology and accessible human-computer interfaces, maintaining active research output through university-affiliated projects at Akademijos St. 4, Vilnius.

• 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

• 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.

• Deep Learning
• Convolutional Neural Networks
• Medical Imaging
• +۴ مورد دیگر

Sandra Virbukaite is a Lecturer at Vilnius University's Department of Lecturers. She is currently pursuing doctoral studies (2020–2024) at Vilnius University Institute of Data Science and Digital Technologies, focusing on deep learning methods for identifying pathological changes in fundus images under Dr. Jolita Bernatavičienė. Master’s degree in Applied Statistics (Vilnius Gediminas Technical University, 2011) Bachelor’s degree in Engineering Informatics (Vilnius Gediminas Technical University, 2009) Recent certificates from Oxford University's Machine Learning Summer Schools (2023–2024) and DeepLearn 2021 Summer Her research centers on medical imaging and artificial intelligence applications for eye disease detection, particularly glaucoma through optic disc and cup segmentation . Analysis of her 15 most recent publications (2020–2024) shows focus areas in convolutional neural networks , image preprocessing , and segmentation accuracy in fundus imaging. Presenter at 8 major conferences including DAMSS (2021–2022), WSCG (2022), and AI Technologies in Medicine (2023–2024) Contributor to 5 peer-reviewed publications in IEEE Access and Nonlinear Analysis: Modelling and Control She teaches Database Query Languages at Vilnius University since 2020, demonstrating both pedagogical and research expertise in applied statistics and deep learning domains.

Gediminas Navickasمشاهده پروفایل

مدرس

• Speech Recognition
• Speech Synthesis
• Signal Processing
• +۵ مورد دیگر

Gediminas Navickas is a Lecturer at Vilnius University's Institute of Data Science and Digital Technologies (DMSTI) within the Faculty of Mathematics and Informatics, specializing in the Image and Signal Analysis Group. His work bridges academic research with practical applications in Lithuanian language technologies. His primary research focuses on automatic Lithuanian speech recognition, speech signal processing, speech synthesis methods and algorithms, and speech synthesis quality assessment. Navickas has made significant contributions to developing speech technologies specifically for the Lithuanian language, addressing the challenges of resource scarcity compared to more widely spoken languages. His work demonstrates strong interdisciplinary connections between computer science, linguistics, and cognitive science. Navickas's publication record reveals a consistent trajectory of innovation in speech technology, with recent work emphasizing cognitive approaches to evaluating synthetic speech, particularly examining differences in perception between blind and sighted users. His research has evolved from foundational work on neural network architectures for speech synthesis to comprehensive studies on speech corpus development and accessibility applications. He has been instrumental in major EU-funded projects including the LIEPA series (2013-present), which have developed comprehensive speech recognition and synthesis systems for Lithuanian. These projects have produced practical applications ranging from educational robots to voice-controlled mobile services and assistive technologies for the visually impaired. Member of Lithuanian Computer Society (LIKS) Council Member of IEEE organization Active participant in COST actions including UniDive and Multi3Generation Navickas regularly engages with the public through media appearances on LRT radio and television, where he explains complex speech technology concepts to general audiences. His science communication efforts demonstrate a commitment to making technical research accessible and relevant to Lithuanian society.

Matviychuk Andriy Viktorovychمشاهده پروفایل

استاد

• Artificial Intelligence
• Mathematical Modeling
• Neuro-Fuzzy Technologies
• +۵ مورد دیگر

Professor Matviychuk Andriy Viktorovych is a leading academic and practitioner at Vadym Hetman Kyiv National Economic University, where he serves as Professor at the Department of Mathematical Modeling and Statistics. He holds multiple concurrent roles including General Director of the KNEU Science Park and Director of the Institute of Modeling and Information Technologies in Economics. He has 24 years of scientific and pedagogical experience. His research focuses on artificial intelligence, complex system modeling, financial time series forecasting, and economic risk modeling using neuro-fuzzy technologies. His recent publications demonstrate expertise in environmental modeling, human capital analysis, and financial market dynamics. 2024 - Editor-in-Chief of "Neuro-fuzzy modeling technologies in economics" (Scopus-indexed) 2024 - Completed advanced training in Data Science at KNEU 2024 - International scientific and pedagogical internships Scientific awards include: Honorary sign 'For Scientific and Educational Achievements' (2018) Honorary sign 'Outstanding Educator of Ukraine' (2015) President of Ukraine grants for young scientists (2014, 2012) National Academy of Sciences of Ukraine Prize (2006) M.Fedorenko International Scientific Fund grants (2009, 2004) He serves as head of the Specialized Academic Council for doctoral dissertation defenses and participates in multiple editorial boards of Ukrainian academic journals. His language proficiency includes English, and his personal interests span history, philosophy of religion, and skiing.

Daivaras Sokasمشاهده پروفایل

پژوهشگر

• Digital Signal Processing
• Machine Learning
• Biomedical Engineering
• +۱ مورد دیگر

Daivaras Sokas is a researcher at the Institute of Biomedical Engineering, Kaunas University of Technology, affiliated with the Biosignal Analytics Laboratory. His primary responsibilities include conducting laboratory work and grading reports for the course 'Digital Signal Processing and Machine Learning.' He holds an ORCID ID (0000-0001-5723-3655). Research Interests: His work focuses on Digital signal processing Machine learning applications in biomedical contexts Electrical and electronic engineering (T001) Laboratory Affiliation: He is actively involved with the Biosignal Analytics Laboratory at KTU.

Andrius Sološenkoمشاهده پروفایل

پژوهشگر

• Machine Learning
• Biosignal Modeling
• Wearable Devices
• +۳ مورد دیگر

Andrius Sološenko is a Senior Researcher at the Institute of Biomedical Engineering at Kaunas University of Technology (KTU), affiliated with the Biosignal Analytics Laboratory . His scientific and practical interests focus on: Development of algorithms for arrhythmia recognition in wearable devices Biosignal modeling, particularly photoplethysmogram (PPG) analysis Application of machine learning techniques, including convolutional neural networks (CNNs) Contact: andrius.solosenko@ktu.lt

• Statistics
• Big data analytics
• Spatial statistics
• +۳ مورد دیگر

Laura Šaltytė-Vaisiauskė is an Associate Professor and currently serves as Vice-Rector for Research and Innovation at Klaipėda University, Lithuania. She is affiliated with the Department of Informatics and Statistics within the Faculty of Marine Technology and Natural Sciences, with additional connections to the Faculty of Social Sciences and Humanities and the Institute of Baltic Region History and Archaeology. Dr. Šaltytė-Vaisiauskė defended her doctoral thesis in Mathematics at Vilnius University in 2013 and has since established herself as a versatile researcher with publications spanning multiple disciplines while maintaining core expertise in statistical methodology. Her research interests center around Statistics, Big data analytics, and Spatial statistics, with significant contributions to Image recognition and Discriminant analysis. Analysis of her publication record reveals a strong focus on developing advanced statistical methods for spatio-temporal data analysis, particularly using Hidden Markov Models with spatial weighting. Her work demonstrates increasing interdisciplinary reach, extending from pure statistical methodology to applications in finance, healthcare, energy systems, and social sciences. Recent publications show her applying statistical techniques to diverse problems including financial volatility modeling, electric vehicle optimization, and even therapeutic interventions for depression. Her scholarly output demonstrates consistent productivity across multiple domains, with publications in reputable journals and conference proceedings. Her work shows particular strength in developing methodological innovations with practical applications, often collaborating across disciplinary boundaries to address complex real-world problems. As Vice-Rector for Research and Innovation, Dr. Šaltytė-Vaisiauskė oversees research strategy and development at Klaipėda University. Her leadership role complements her active research career, allowing her to influence both the direction of her own work and the broader research landscape at her institution. Her recent projects include studies on the impact of COVID-19 restrictions on businesses in the Klaipėda region, demonstrating her commitment to addressing timely societal challenges through rigorous research.