Dr. Vytautas Grivickas is a Senior Researcher at the Institute of Photonics and Nanotechnology (IPN), Vilnius University, specializing in semiconductor physics and materials science. His research focuses on carrier recombination, transport phenomena, and optical properties of wide bandgap semiconductors including Si, SiC, diamond, AlN, and Ga2O3. Expertise in radiation defect evolution in semiconductors Develops contactless characterization techniques for radiation sensors Active in medical dosimetry systems development Research Highlights: His work analyzes free carrier absorption spectra (e.g., in 4H-SiC), investigates carrier dynamics in layered chalcogenides, and explores electron-hole separation mechanisms. Collaborative studies appear in journals like Scientific Reports and Applied Physics Letters . Project Leadership: Contributes to the UWBG-LEAP project (Ultra-Wide Bandgap Laser) under European Regional Development Fund. Scientific Contributions: Demonstrates expertise in semiconductor material characterization and laser-induced modifications, with over 15 recent publications in supercontinuum generation, Bessel beam applications, and femtosecond laser micromachining.
Dr. Vytautas Jukna is a Professor and Senior Researcher at the Laser Research Center (LRC) , Vilnius University. His work focuses on nonlinear optics , laser-matter interaction , and ultrashort pulse propagation . Active in supercontinuum generation , filamentation , and conical wave physics Led 6 projects funded by the Research Council of Lithuania (2010–2022) and the European Regional Development Fund Supervised 12+ students (BSc/MSc/PhD) at Vilnius University and Ecole Polytechnique Research Interests: His work bridges fundamental nonlinear optical phenomena and applied laser processing. Key areas include terahertz structured light , high-precision micromachining , and numerical modeling of filamentation . Publication Trends: Recent articles emphasize nonparaxial beam shaping , supercontinuum in solid-state media , and surface roughness control via femtosecond lasers. Collaborations span materials science, quantum optics, and biomedical applications. Awards: Best PhD Thesis in Physics, Lithuania (2012) Teaching & Leadership: Teaches Laser-matter interaction and Optical information processing . Chairs the Laser Physics and Optical Technology Master's Program Committee and serves on thesis defense panels.
Andrius Poškus is an Associate Professor at the Institute of Chemical Physics , Faculty of Physics, Vilnius University. His research focuses on atomic physics, nuclear physics, and computational physics , particularly in the areas of bremsstrahlung and electron-nuclei interactions. Teaching: Applied Nuclear Physics, Atomic and Elementary Particle Physics, and Educational Laboratory of Atomic and Nuclear Physics. Research: Developing computational models for radiation physics, including bremsstrahlung cross-section calculations and electron transport simulations. His recent publications analyze bremsstrahlung spectra, X-ray detector optimization, and ionization processes in noble gases. While no explicit awards or student supervision details are provided, his work contributes to advancements in radiation detection, nuclear data analysis, and high-energy physics simulations .
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
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.
Prof. Dr. Virginijus Marcinkevičius is a Professor at Vilnius University , serving as the head of the Smart Technologies Research Group and the Artificial Intelligence Laboratory within the Institute of Data Science and Digital Technologies . He is also a Senior Researcher , Project Lead Researcher , and Group Leader . Based in Vilnius, Lithuania, he has been instrumental in advancing research in machine learning , artificial intelligence , cybersecurity , and natural language processing . Research Interests: Machine Learning & AI Cybersecurity & Threat Detection Natural Language Processing Hyperspectral Imaging & Remote Sensing Autonomous Systems & Robotics Big Data & Cloud Computing His work spans both theoretical and applied aspects, including IoT security , visual analytics , and intelligent decision support systems . Recent projects include the development of propaganda detection systems , hyperspectral unmixing algorithms , and autonomous driving agents . Doctoral Supervision: He has supervised 19+ PhD students and 5+ consultants , covering topics from machine learning in cybersecurity to neural machine translation and autonomous UAV navigation . Projects & Grants: He has led or contributed to 15+ national and EU-funded projects , including: CognitiveSTATS – COVID-19 data literacy platform Propaganda and Disinformation Research – ML-based detection DAMIS – Data mining system for national research Raštija 2 – Lithuanian language resource integration Publications: He has authored or co-authored 60+ peer-reviewed publications in journals like IEEE Access , Informatica , Frontiers in Psychology , and Machine Vision and Applications . Professional Memberships: He is a member of the Lithuanian Computer Society , Lithuanian Mathematical Society , and Lithuanian Operations Research Society .
Prof. Dr. Remigijus Paulavičius is a Senior Researcher, Professor, and Group Leader at the Blockchain and Quantum Technologies Group of Vilnius University. He earned his Doctor of Science degree in Computer Science in 2010 from Vilnius University's Institute of Mathematics and Informatics, focusing on global optimization with simplex subdomains under the supervision of Dr. J. Žilinskas. Research Interests : His work bridges global optimization methods with blockchain and quantum computing. Key areas include non-convex bilevel programming, derivative-free optimization algorithms (e.g., DIRECT), and applications in blockchain scalability, consensus protocols, and quantum circuit design. Scientific Awards : Best Paper Award, Journal of Global Optimization (2014) Funding from Lithuanian State Science and Studies Foundation for doctoral students (2007–2009) Member, Young Academy of the Lithuanian Academy of Sciences (since 2019) Projects & Leadership : Leads research on blockchain simulators, quantum machine learning frameworks, and optimization toolboxes (e.g., DGO, DIRECTGO). Involved in international collaborations and conference committees, including AIChE and EUROPT. Publications : Over 70 works on global optimization algorithms, blockchain applications, and quantum computing, with recent focus on generative AI, energy consumption in blockchains, and reproducibility in machine learning.
Dr. Ernestas Filatovas is a Senior Researcher and Chief Researcher in the Project at Vilnius University's Institute of Data Science and Digital Technologies (formerly Institute of Mathematics and Informatics), where he has been affiliated since 2013. He leads the Blockchain and Quantum Technologies Group, focusing on cutting-edge research at the intersection of quantum computing, blockchain, and artificial intelligence. Previously, he served as an Associate Professor and Lecturer at Vilnius Gediminas Technical University's Faculty of Fundamental Sciences from 2013 to 2019. Dr. Filatovas earned his Doctor of Technology in Computer Science Engineering from Vilnius University Institute of Mathematics and Informatics in 2012. His dissertation, supervised by Prof. Dr. Olga Kurasova, focused on the interactive solution of multi-criteria optimization problems. His research spans multiple high-impact domains, with particular expertise in blockchain technologies, quantum computing, artificial intelligence, and machine learning. He has pioneered work in quantum blockchain implementations, reproducibility of AI research through blockchain verification, and quantum machine learning applications. His research bridges theoretical computer science with practical applications in financial markets, healthcare, and distributed systems. His extensive publication record—over 50 scientific papers, with more than 25 in Clarivate Analytics-indexed journals—demonstrates consistent productivity and international collaboration. Recent work shows a clear trajectory toward quantum-enhanced AI systems, blockchain-based research verification frameworks, and quantum algorithms for practical problems. Laureate of the 4th LMA Young Scientists' Conference (2014) INFOBALT scholarship 2nd place winner (2014) Lithuanian State Science and Studies Foundation funding recipient (2009, 2010) Recognized as one of Lithuania's most active doctoral students Master's degree with honors (2006) Dr. Filatovas leads multiple significant research projects, including the 2021-2024 project 'Solving the problems of reproducibility of scientific research in the field of artificial intelligence using blockchain technologies' as team leader, and the 2023-2027 project 'Development and validation of quantum machine learning methods using prepared datasets' as Chief Researcher. He has also contributed to international collaborations such as the Spanish-funded 'High Performance Solutions for Modern Scientific Computing Challenges' (2019-2021). His popular science contributions, including the VU news portal article 'Quantum Computing: Who and Why?', demonstrate his commitment to science communication. As a key member of Vilnius University's Blockchain and Quantum Technologies Group, Dr. Filatovas contributes to Lithuania's growing reputation in quantum computing research and blockchain innovation, working closely with international collaborators across Europe.
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.
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.
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
Assoc. Prof. Dr. Algirdas Lančinskas is a Senior Researcher and Associate Professor at Vilnius University's Faculty of Mathematics and Informatics, working within the Global Optimization Group at the Institute of Mathematics and Informatics. His academic career spans over a decade at Vilnius University, where he has progressed from Junior Researcher to his current position as Associate Professor and Chief Researcher on multiple projects. He maintains an active international research profile with collaborations across Europe. Doctor of Physical Sciences (Informatics, 09P), Vilnius University Institute of Mathematics and Informatics, 2013 Dissertation: Parallelization of random search global optimization algorithms Dissertation supervisor: Prof. Dr. (HP) Julius Žilinskas Dr. Lančinskas specializes in global optimization, particularly competitive facility location problems, discrete optimization, and parallel computing. His research bridges theoretical optimization methods with practical applications in business, spatial economics, and public health. He has made significant contributions to nature-inspired optimization heuristics, developing novel algorithms for solving complex location problems with applications ranging from business expansion strategies to pandemic testing protocols. His work often involves multi-objective optimization approaches and the development of efficient parallel algorithms to handle computationally intensive problems. Analysis of his recent publications reveals a strong focus on competitive facility location modeling, with particular expertise in discrete optimization problems where multiple competitors vie for market share. His research increasingly incorporates robustness considerations under uncertainty in customer behavior, reflecting the growing complexity of real-world location decisions. The integration of ranking-based approaches with traditional optimization techniques represents a distinctive methodological contribution across his work. Dr. Lančinskas actively supervises doctoral students and has served on multiple dissertation defense councils. His research is supported by significant funding from the Research Council of Lithuania and international collaborations through COST actions. He has led multiple research projects focused on optimization algorithm development and their applications. As a member of the Global Optimization Group, he contributes to Vilnius University's strong tradition in optimization research, collaborating closely with Prof. Julius Žilinskas and international partners across Spain, the UK, and other European institutions. His work exemplifies the institute's commitment to both theoretical advances and practical applications of optimization methods.
Dr. Daniele Ettore Otera is a Senior Researcher at the Institute of Data Science and Digital Technologies (DMSTI) and the Faculty of Mathematics and Informatics of Vilnius University , Lithuania. His work is centered on geometric group theory, low-dimensional topology, and group theory, with a focus on asymptotic topology and topological tameness of groups and manifolds. Education: He earned a Mathematics degree from the University of Palermo (1999), a DEA (Master’s) from Université Paris-Sud 11 (2001), and a co-tutored PhD from both University of Palermo and Université Paris-Sud 11 (2006). Research Interests: Geometric group theory: quasi-isometries, ends of groups, lattices in Lie groups Low-dimensional topology: topological tameness, simple connectivity at infinity, geometric simple connectivity Group theory: subgroup permutability, commutativity degrees, probability in group theory Publications: His recent work spans graph theory, spectral invariants, group actions, and geometric topology, reflecting a deep interdisciplinary approach combining algebra, topology, and combinatorics. Labs & Teams: He is affiliated with the Interdisciplinary Statistical Research Group within DMSTI, contributing to collaborative research in mathematical sciences.
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
Rimantas Pupeikis is an Associate Professor and Affiliated Scientist at Vilnius University's Institute of Data Science and Digital Technologies (DMSTI), formerly known as the Institute of Mathematics and Informatics (MII). He is affiliated with the Global Optimization Group within the institute and has maintained a long-standing academic career spanning several decades. Dr. Pupeikis graduated in 1969 from the Faculty of Mechanical Technology at the Vilnius Branch of the Lithuanian State Industrial University, specializing in Complex Mechanization and Automation of Machine Production. He earned his Doctor of Technical Sciences degree in 1979 from the Scientific Council of KPI and was awarded the pedagogical scientific title of Associate Professor in 2000 by the Scientific Council of VGTU (Vilnius Gediminas Technical University). His research focuses on recursive identification of feedback dynamic objects, nonlinear systems, Wiener systems, delay identification, and dynamic object management. His work centers on the development and applications of the theory of identification and control of technological processes. Dr. Pupeikis has published extensively on system identification, signal processing, and control theory, with particular emphasis on adaptive tracking methods, convolution algorithms, and identification of nonlinear systems. His publication record shows a consistent research trajectory with significant contributions in the 2010s and 2020s, particularly in the areas of adaptive joint tracking of system parameters, revised convolution methods, and identification of nonlinear systems with time delays. His work demonstrates a strong mathematical foundation combined with practical applications in signal processing and control systems. Dr. Pupeikis has also been involved in pedagogical activities, teaching Digital Signal Processing and Databases at the VGTU Faculty of Electronics, delivering both lectures and exercises in English and Lithuanian. His professional career includes positions as Junior Researcher, Postgraduate Student, and Researcher at the Institute of Physical and Technical Energy Problems (1969-1979), work at the Department of Technological Process Control at MII starting in 1980, research at the Department of Mathematics, Institute of Mathematics, University of Bayreuth (1996-1998), and research at the Department of Automatic Control and Communication Systems at Linköping University (2002-2003).
