Find Academic Supervisors

Olga KurasovaView profile

Professor

• Machine Learning
• Data Visualization
• Cybersecurity
• +3 more

Dr. Olga Kurasova is a Professor and Senior Researcher at Vilnius University's Institute of Data Science and Digital Technologies, where she leads research in the Cognitive Computing Group. Her work focuses on developing advanced computational methods for real-world applications. Her primary research explores machine learning paradigms including deep learning for cybersecurity (keystroke dynamics, adversarial attacks), medical image analysis (pancreatic cancer detection), industrial monitoring, and explainable AI. She maintains strong collaborations across disciplines, particularly in healthcare and security domains. Analysis of her recent publications (2023-2025) reveals three dominant themes: (1) Advanced biometric authentication systems using behavioral analysis and deep learning, (2) Medical AI applications focusing on pancreatic cancer detection through CT image analysis, and (3) Theoretical advancements in explainable AI methodologies for high-stakes domains. Significant scientific recognition includes: 2021 Lithuanian Science Prize for the cycle 'From Data Science to Artificial Intelligence Technologies' 2024 Vilnius University Rector's Science Prize She has led multiple national research projects, including a 2024-2027 LMT-funded initiative on 'adversarial machine learning for cybersecurity' and coordinated interdisciplinary teams for projects on cognitive computing capabilities and optimal data mining solutions. She directs research within the Cognitive Computing Group, focusing on developing intelligent systems for data analysis, visualization, and decision support across healthcare, cybersecurity, and industrial applications.

Paulius RaubaView profile

Lecturer

• Machine Learning
• Data Science
• Artificial Intelligence
• +3 more

Paulius Rauba is a Visiting Lecturer at ISM University of Management and Economics and a PhD candidate in Machine Learning at the University of Cambridge. He holds a BSc from ISM and an MSc from the University of Oxford, where he is also an Oxford Shirley Scholar. His research focuses on fundamental machine learning, model evaluation, robustness, and quantitative social science. He teaches courses in Econometrics, Data Science, and Elements of Artificial Intelligence. Paulius leads Gauss, a company specializing in deploying machine learning models, and has advised the European Commission on AI systems. His work spans academia, industry, and international organizations, emphasizing data-driven solutions for business and social challenges. Education: BSc, ISM University of Management and Economics MSc, University of Oxford His research explores model robustness, AI applications in healthcare, and conflict analysis through statistical and neural methods. Recent articles address topics like self-healing ML frameworks and context-aware model testing. Paulius is committed to advancing AI ethics and practical deployment. Awards: Oxford Shirley Scholar As a leader in Gauss, he bridges academic innovation with industry needs, focusing on scalable AI solutions. His advisory roles and grants underscore his interdisciplinary impact.

• Environmental Informatics
• Bioaerosol Monitoring
• Computer Vision Applications
• +3 more

Valiulis Gediminas serves as an Associate Professor at Vilnius University's Šiauliai Academy within the Department of Informatics Engineering. His academic career spans both engineering fundamentals and advanced environmental applications, with a pronounced shift toward bioaerosol monitoring and computer vision systems since 2018. His research interests focus on Environmental Informatics and Automatic Bioaerosol Detection , particularly developing computer vision models for pollen classification using light scattering techniques. This work bridges Environmental Science , Computer Vision , and Public Health through real-time pollen monitoring systems that serve allergy sufferers. His methodology combines fluorescent particle analysis , clustering algorithms , and sensor calibration to advance aerobiological research. Analysis of his publication trends reveals a strategic evolution: early work (2004-2012) centered on control systems engineering (granulation processes, microinverters), while recent output (2018-2024) demonstrates deep specialization in bioaerosol informatics . Key contributions include the Rapid-E particle counter validation, ozone-pollen interaction studies, and real-time pollen forecasting systems for clinical applications. His interdisciplinary approach connects atmospheric science with machine learning to solve environmental monitoring challenges. Automatic pollen recognition systems development Fluorescent bioaerosol characterization Real-time environmental data applications Computer vision for ecological monitoring While no formal advising relationships or scientific awards are documented in the source material, his collaborative research consistently involves multi-institutional teams across Lithuania and Europe. Current projects focus on enhancing bioaerosol sensor accuracy and developing clinical decision-support tools for allergy management through real-time environmental data integration.

Viktor MedvedevView profile

Associate Professor

• Machine Learning
• Deep Learning
• Neural Networks
• +8 more

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.

• Renewable Energy
• Automation
• Computer Vision
• +4 more

Dr. Dervinis Donatas is an Assistant Professor at the Department of Electrical and Electronic Engineering, VU Šiauliai Academy. His research focuses on Renewable Energy, Automation, Computer Vision, and Cybersecurity, with a strong emphasis on practical applications in industrial systems and educational technologies. He holds a Doctor of Science in Electrical and Electronics Engineering and has over 22 months of academic experience. His educational background includes advanced studies in electrical engineering, complemented by a prolific career in research. Key research areas include solar energy systems, image processing, and automated systems for transportation and security. He contributed to the development of a textbook on Image Processing (2012) and has been affiliated with the eLABa research group. Donatas has authored or co-authored over 15 peer-reviewed publications, exploring topics such as energy-efficient cloud computing, password security, and real-time video analysis for human recognition. His work bridges theoretical advancements with practical implementations in fields like industrial automation and biomedical engineering. While no specific awards are listed, his publications appear in reputable journals like Applied Scientific Research and Professional Studies: Theory and Practice . His academic advising and grant activities are not explicitly detailed, though his research often involves collaborative projects with institutions like Tallinn University of Applied Sciences and Šiauliai State College. Donatas is actively involved in the eLABa research team, focusing on innovative solutions in electrical engineering and technology education. His future work continues to emphasize sustainable energy systems and advanced computer vision applications.

Daunys GintautasView profile

Professor

• Computer Science
• Environmental Science
• Biometrics
• +7 more

Roles & Affiliation: Professor at the Regional Development Institute of Šiauliai Academy (Vilnius University). Previously served as Professor and Head of the Department of Electronics at Šiauliai University. Specializes in Computer Science, Environmental Science, and Biometrics, with a focus on airborne pollen recognition, deep neural networks, and bioaerosol monitoring. Doctorate in Electrical and Electronic Engineering from Kaunas University of Technology (1999). Research Interests: Combines computer vision and machine learning to advance automated pollen classification, allergen forecasting, and health impact analysis. Explores applications of AI in environmental monitoring, biometrics, and medical diagnostics. Publications: Over 50 peer-reviewed articles, with recent work emphasizing real-time pollen detection systems, health correlations of airborne allergens, and automated bioaerosol monitoring. Key projects include EU-funded initiatives on synergistic effects of pollen and air quality, and real-time pollen forecasting. Grants & Projects: Led EU FP6 projects (COGAIN, MICOLE) on eye-tracking technology for disabled users. Active in Lithuania’s Research Council and EU Structural Funds projects focusing on pollen and environmental health. Labs & Teams: Collaborates with interdisciplinary teams in Šiauliai Academy’s Department of Electronics and bioaerosol research groups. Part of international networks like ISCA and IEEE, contributing to standards in eye-tracking and environmental monitoring.

Šaulienė IngridaView profile

Professor

• Aerobiology
• Environmental Science
• Allergology
• +4 more

Professor Šaulienė Ingrida serves as a Professor and Chief Researcher at Vilnius University Šiauliai Academy, specializing in aerobiology and environmental science. Her work focuses on pollen properties, allergen mitigation, and public health impacts. She has held leadership roles, including Vice-Rector for Science and Art, and directs the University Research Institute. Education: Doctor of Biomedical Sciences (Botany). Research Interests: Aerobiology, pollen dispersion dynamics, allergenic potential of urban flora, and development of monitoring technologies. Her projects include automated pollen detection systems and EU-funded initiatives like EO4EU and REALTIME. Key Projects: Leading initiatives such as AI-augmented Earth Observation (EO4EU), allergen forecasting (PASYFO), and standardized bioaerosol monitoring guidelines. She also chairs COST Action ADOPT and serves in international aerobiology organizations. Awards: Doctor Honoris Causa from Daugavpils University (2016), multiple ministerial commendations, and researcher accolades. Active in editorial roles for journals like Frontiers in Allergy and Zemdirbyste-Agriculture . Grants & Labs: Over 20 funded projects since 2005, focusing on ragweed management, pollen forecasting, and urban green space policies. Collaborates with institutions across Europe on environmental and health initiatives.

• Quantum Computing
• Machine Learning
• Medical Image Analysis
• +3 more

Linas Petkevičius serves as an Associate Professor at Vilnius University's Faculty of Mathematics and Informatics, actively teaching courses including Introduction to Quantum Computing across 10 consecutive academic years from 2016/2017 through 2025/2026 as evidenced by institutional schedules. His research demonstrates remarkable interdisciplinary breadth spanning quantum computing algorithm optimization, medical diagnostics through digital pathology analysis, and satellite-based environmental monitoring. He develops machine learning solutions for breast cancer prognosis using Ki67 heterogeneity metrics, creates quantum circuit schemes adapted to hardware constraints, and implements deep learning models for algal bloom detection in Baltic waters using Sentinel-2 data. His work consistently bridges theoretical computer science with practical healthcare and environmental applications. Analysis of his 15 most recent publications (2023-2025) reveals three dominant research thrusts: 1) Quantum computing optimization for NISQ devices, 2) Medical image analysis focusing on spatial tumor microenvironment characterization in breast cancer, and 3) Remote sensing applications using transformer models and few-shot learning for satellite change detection. His publications show increasing specialization in combining deep learning architectures with domain-specific constraints across these fields. Scientific Awards: No scientific awards were mentioned in the provided materials. Advising and Grants: The provided texts contain no information regarding student advisement, research grants, or funded projects.

Povilas TreigysView profile

Professor

• Digital Signal Processing
• Machine Learning
• Deep Learning
• +3 more

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.

• Analytic Number Theory
• Riemann Zeta Function
• Dirichlet L-functions
• +5 more

Professor Igor Belov is a Senior Researcher and Professor at Vilnius University's Institute of Data Science and Digital Technologies, specializing in analytic number theory, information security, and mathematical modeling. His research spans theoretical mathematics with practical applications in finance and computer science, particularly focusing on zeta functions, prime number theory, and stable distribution models. Education: Doctor of Physical Sciences (Mathematics) from Vilnius University and Vilnius Gediminas Technical University (2004) Master's degree in Statistics from Vilnius University (1999) Bachelor's degree in Mathematics Applications from Vilnius University (1997) Professor Belov's research interests center on analytic number theory with emphasis on Riemann zeta function and Dirichlet L-functions, combinatorial analysis, information security, and mathematical modeling of financial data using stable distributions. His recent work explores fractal structures related to zeta functions, efficient algorithms for zeta function calculations, and applications of machine learning in financial document analysis and securities price forecasting. His publication record demonstrates a strong trajectory from pure mathematical research toward interdisciplinary applications. While maintaining a robust foundation in analytic number theory, his recent work increasingly bridges theoretical mathematics with practical computing applications, particularly in financial modeling and information security. This evolution reflects a strategic expansion of his research impact across multiple domains. Scientific Awards: 1st place at the Vilnius Young Mathematicians' Olympiad (1990) Honorable mention at the Lithuanian Young Mathematicians Olympiad (1991) Letter of Commendation at the Lithuanian Young Mathematicians Olympiad (1992) Professor Belov actively supervises doctoral research and has served on numerous dissertation defense committees. He currently mentors four doctoral students working on zeta function algorithms, financial forecasting, document recognition, and blockchain acceleration. His research has been supported by multiple grants including the LMT project P-SV-23-9 on Mandelbrot bulbs visualization associated with the Riemann zeta function. He has also participated in international research collaborations at CINECA supercomputing center and Karlsruhe Institute of Technology. As a member of the Smart Technologies Research Group, Professor Belov contributes to Vilnius University's leadership in mathematical research and its applications, particularly in the areas of zeta function analysis and computational finance.

• Stochastic Programming
• Mathematical Modeling
• Financial Mathematics
• +7 more

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.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +3 more

Rolandas Gricius is a PhD student and junior assistant at Vilnius University's Institute of Data Science and Digital Technologies, specializing in Computer Science within the Smart Technologies Research Group. His research focuses on content recognition in digitized structured documents under the supervision of Prof. Dr. Igor Belov.

Rokas BendikasView profile

Academic

• Robotics
• Artificial Intelligence
• Machine Learning
• +3 more

Rokas Bendikas is a Visiting Lecturer at ISM and a Doctoral Candidate in Foundational AI at University College London and University of California, San Diego. His research focuses on model-based Reinforcement Learning and 3D scene understanding for robotic control, with a secondary focus on biomedical engineering applications in cardiac electrophysiology modeling. PhD Supervisors: Prof. Danail Stoyanov (UCL) and Prof. Hao Su (UCSD) Education: Master's in Robotics at Imperial College London (Dyson Robotics Lab) Bachelor's in Biomedical Engineering at King's College London Research Interests: Combining spatial understanding and model-based RL for robotic autonomy. Previously applied deep learning to atrial fibrillation mechanisms during his biomedical engineering studies. Specializes in simulation-based approaches for medical device development and surgical planning. Articles Trends: Dominated by robotics control (2023) and cardiac modeling (2020-2021), reflecting dual focus between foundational AI and biomedical applications. Strong emphasis on simulation-driven innovation in both domains. Professional Experience: Software engineering at MathWorks and Qualcomm, research in CEMRG (atrial fibrillation computational modeling). Labs/Teams: Dyson Robotics Lab (Imperial), Cardio-Electro Magnetic Research Group (KCL), and current collaborations at UCL/UCSD AI labs.

John AntonakisView profile

Professor

• Organizational Behavior
• Leadership Development
• Charismatic Leadership
• +4 more

John Antonakis is a Professor of Organizational Behavior at the University of Lausanne’s Faculty of Business and Economics (HEC). He also serves as a Visiting Scholar at ISM. His research focuses on charisma, leadership development, crisis management, and research methodology. Antonakis has secured over $2.45 million in research funding and has been recognized as a Clarivate-Web of Science Highly Cited Researcher (2019) and ranked in the world’s top 2% researchers by PLOS Biology (2020). His work spans prestigious journals like Science, Nature Human Behavior, and Academy of Management Journal. Notable contributions include studies on charismatic leadership’s economic impact, crisis rhetoric effects, and methodological critiques of statistical techniques. Antonakis has held editorial roles including Editor-in-Chief of The Leadership Quarterly and served on top journal boards. Media outlets such as the New York Times and BBC frequently cite his research. Key Research Themes: Charismatic leadership, crisis leadership, leadership measurement, and statistical methodology. Recent Focus: Deep learning applications in leadership detection, gender gaps in venture funding, and pandemic leadership strategies. His publications emphasize rigorous experimental design and transparency in research practices, contributing to methodological advancements in organizational studies.

• Machine Learning
• Multi-class Classification
• Neural Networks
• +3 more

Rimantas Kybartas , Associate Professor at Vilnius University's Faculty of Mathematics and Informatics , specializes in machine learning and software systems architecture . His research focuses on multi-class classification methodologies, including pair-wise classifiers and fuzzy template systems. Current academic affiliation: Vilnius University Key research domains: Neural Networks, Ensemble Learning, Pattern Recognition Teaching focus: Software Systems Architecture and Design His publication record from 2010-2012 demonstrates expertise in solving multi-classification challenges through innovative ensemble architectures and similarity feature engineering. Notably, he has developed frameworks for mineral recognition and generalized multi-category neural network systems. Recent publications reveal emphasis on: Optimizing pair-wise classifier ensembles Addressing complexity in neural network design Domain adaptation techniques for classification tasks Statistical learning in multi-class contexts