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viktor medvedev

Assoc. Prof. Dr. Viktor Medvedev

Department: Blockchain and Quantum Technologies Group
Position: Senior Researcher, Project Principal Researcher, Associate Professor

Address: Akademijos g. 4, room 613, Vilnius
Tel: +370 5 210 9310
E-mail:   

Social profiles: linkedin 24x24  google 24x24  orcid 24x24  clarivate 24x24

 

Academic Qualifications

Education

  • 2008 – PhD in Informatics Engineering (Technological Sciences, 07T). Dissertation: “Research into the Use of Feedforward Neural Networks for Multidimensional Data Visualisation”.
  • 2003–2007 – doctoral studies at Vilnius Gediminas Technical University and the Institute of Mathematics and Informatics, Lithuania.

Honours and Awards

Honours and Awards

  • ICAISC 2006 – Best Presentation Award, 8th International Conference on Artificial Intelligence and Soft Computing.
  • ICANNGA 2007 – Best Young Researcher Paper Award in the area of Neural Networks, 8th International Conference on Adaptive and Natural Computing Algorithms.
  • DAMSS 2021, 2022 and 2023 – Best Poster Awards.

Research Interests

Research Interests

  • Data mining.
  • Artificial intelligence.
  • Machine learning and deep learning.
  • Adversarial machine learning.
  • Artificial neural networks.
  • Multidimensional data visualisation and dimensionality reduction.
  • Medical image and biomedical signal analysis.
  • Cybersecurity.
  • Detection and prevention of malicious activity.
  • Healthcare data analysis.

Academic Service

Conference Organisation and Programme Committees

  • Member of the Organising Committee of the International Conference on Data Analysis Methods for Software Systems (DAMSS).
  • Member of the Technical Program Committee of the International Conference on Advances in Artificial Intelligence and Machine Learning (AAIML), 2026 and 2027.
  • Co-organiser of the national master's student research conference held at the Lithuanian Academy of Sciences, 2024–2026.
  • Member of the Programme Committee of the World Conference on Information Systems and Technologies (WorldCIST), 2024–2025.
  • Member of the Programme Committee of the International Workshop on Secure Mobile Cloud Computing (IWoSeMC), 2020–2024.
  • Member of the Scientific Committee of the 2021 International Conference on Information Technology & Systems.
  • Organisation of the final Management Committee meeting of COST Action IC1406 cHiPSet, Vilnius, Lithuania, 28–29 March 2019.
  • Member of the Programme Committee of the International Conference on Advanced Service Computing (SERVICE COMPUTATION), 2014–2016.

Doctoral Examination and University Committees

  • Member of doctoral dissertation defence committees: V. Jusevičius (Vilnius University, 2022), V. Atliha (Vilnius Gediminas Technical University, 2023), B. Čiapas (Vilnius University, 2024), R. Gipiškis, R. Jurkus and S. Virbukaitė (Vilnius University, 2025).
  • Member of the Mathematics of Finance and Insurance Study Programme Committee, Vilnius University.
  • Service on the Faculty Election Committee and the Vilnius University Labour Council Election Committee.

Journal Peer Review

Reviewer for international scholarly journals, including: Knowledge-Based Systems; Information Fusion; Computers & Security; Computers in Biology and Medicine; Computer Standards & Interfaces; Artificial Intelligence in Medicine; Engineering Applications of Artificial Intelligence; Expert Systems with Applications; IEEE Access; IEEE Transactions on Biometrics, Behavior, and Identity Science; Informatica; Mathematical Modelling and Analysis; Journal of Global Optimization; Pattern Recognition Letters; International Journal of Applied Mathematics and Computer Science; Applied Computing and Informatics; Baltic Journal of Modern Computing; The Baltic Journal of Road and Bridge Engineering.

Research Publications and Datasets

Publications affiliated with the Vilnius University Institute of Data Science and Digital Technologies (DMSTI) and the Institute of Mathematics and Informatics (MII)

Articles in Journals Indexed in Web of Science SCIE or SSCI

  • Medvedev, V.; Budžys, A.; Kurasova, O. (2025). A decision-making framework for user authentication using keystroke dynamics. Computers & Security, 155, 104494. DOI: 10.1016/j.cose.2025.104494.
  • Budžys, A.; Kurasova, O.; Medvedev, V. (2025). Integrating deep learning and data fusion for advanced keystroke dynamics authentication. Computer Standards & Interfaces, 92, 103931. DOI: 10.1016/j.csi.2024.103931.
  • Dzemyda, G.; Kurasova, O.; Medvedev, V.; Šubonienė, A.; Gulla, A.; Samuilis, A.; Jagminas, D.; Strupas, K. (2025). Deep learning-based aggregate analysis to identify cut-off points for decision-making in pancreatic cancer detection. Expert Systems, 42(1), e13614. DOI: 10.1111/exsy.13614.
  • Budžys, A.; Kurasova, O.; Medvedev, V. (2024). Deep learning-based authentication for insider threat detection in critical infrastructure. Artificial Intelligence Review, 57(10), 272. DOI: 10.1007/s10462-024-10893-1.
  • Markevičiūtė, J.; Bernatavičienė, J.; Levulienė, R.; Medvedev, V.; Treigys, P.; Venskus, J. (2022). Impact of COVID-19-related lockdown measures on economic and social outcomes in Lithuania. Mathematics, 10(15), 2734. DOI: 10.3390/math10152734.
  • Dzemyda, G.; Sabaliauskas, M.; Medvedev, V. (2022). Geometric MDS performance for large data dimensionality reduction and visualization. Informatica, 33(2), 299–320. DOI: 10.15388/22-INFOR491.
  • Markevičiūtė, J.; Bernatavičienė, J.; Levulienė, R.; Medvedev, V.; Treigys, P.; Venskus, J. (2022). Attention-based and time series models for short-term forecasting of COVID-19 spread. Computers, Materials & Continua, 70(1), 695–714. DOI: 10.32604/cmc.2022.018735.
  • Golubickas, D.; Lukoševičius, S.; Tamakauskas, V.; Dobrovolskienė, L.; Basevičienė, I.; Grib, L.; Ragaišytė, N.; Leonavičius, R.; Medvedev, V.; Veikutis, V. (2021). Image quality in computed tomography coronary angiography and radiation dose reduction. Informatica, 32(4), 741–757. DOI: 10.15388/21-INFOR464.
  • Venskus, J.; Treigys, P.; Bernatavičienė, J.; Tamulevičius, G.; Medvedev, V. (2019). Real-time maritime traffic anomaly detection based on sensors and history data embedding. Sensors, 19(17), 3782. DOI: 10.3390/s19173782.
  • Venskus, J.; Treigys, P.; Bernatavičienė, J.; Medvedev, V.; Voznak, M.; Kurmis, M.; Bulbenkienė, V. (2017). Integration of a self-organizing map and a virtual pheromone for real-time abnormal movement detection in marine traffic. Informatica, 28(2), 359–374. DOI: 10.15388/Informatica.2017.133.
  • Medvedev, V.; Kurasova, O.; Bernatavičienė, J.; Treigys, P.; Marcinkevičius, V.; Dzemyda, G. (2017). A new web-based solution for modelling data mining processes. Simulation Modelling Practice and Theory, 76, 34–46. DOI: 10.1016/j.simpat.2017.03.001.
  • Jucevičius, J.; Treigys, P.; Bernatavičienė, J.; Briedienė, R.; Naruševičiūtė, I.; Dzemyda, G.; Medvedev, V. (2017). Automated 2D segmentation of prostate in T2-weighted MRI scans. International Journal of Computers Communications & Control, 12(1), 53–60.
  • Bernatavičienė, J.; Dzemyda, G.; Bazilevičius, G.; Medvedev, V.; Marcinkevičius, V.; Treigys, P. (2015). Method for visual detection of similarities in medical streaming data. International Journal of Computers Communications & Control, 10(1), 8–21. DOI: 10.15837/ijccc.2015.1.1310.
  • Besson, J. D.; Lupeikienė, A.; Medvedev, V. (2012). Comparing real and intended system usages: a case for web portal. Informatica, 23(2), 191–201. DOI: 10.15388/Informatica.2012.356.
  • Medvedev, V.; Dzemyda, G.; Kurasova, O.; Marcinkevičius, V. (2011). Efficient data projection for visual analysis of large data sets using neural networks. Informatica, 22(4), 507–520.
  • Dzemyda, G.; Marcinkevičius, V.; Medvedev, V. (2011). Web application for large-scale multidimensional data visualization. Mathematical Modelling and Analysis, 16(2), 273–285. DOI: 10.3846/13926292.2011.580381.
  • Medvedev, V.; Dzemyda, G. (2006). Optimization of the local search in the training for SAMANN neural network. Journal of Global Optimization, 35(4), 607–623. DOI: 10.1007/s10898-005-5368-1.

Articles in Journals Indexed in Web of Science AHCI or ESCI

Kurasova, O.; Budžys, A.; Medvedev, V. (2024). Exploring multidimensional embeddings for decision support using advanced visualization techniques. Informatics, 11(1), 11. DOI: 10.3390/informatics11010011.

Other Publications

  • Šimėnas, S.; Klepachevskyi, D.; Medvedev, V.; Treigys, P.; Abramikas, Ž. J.; Bernatavičienė, J. (2027). Inter-patient evaluation of diffusion-based ECG denoising under standardized noise types. Advances of Control and Automation: Proceedings of the 22nd Polish Control Conference, Poznań, Poland, 2026, Volume 2, Lecture Notes in Networks and Systems, 2072, 404–415. DOI: 10.1007/978-3-032-32216-6_33.
  • Dautartas, J.; Čypas, J. R.; Kurasova, O.; Medvedev, V. (2026). Evasion of malware classifiers by injecting category-specific benign features. 2026 International Conference on Advances in Artificial Intelligence and Machine Learning (AAIML), 102–109. DOI: 10.1109/AAIML67890.2026.11498090.
  • Žeromskas, P.; Medvedev, V. (2026). Steganografija dirbtinių neuroninių tinklų parametruose ir jos aptikimas mašininio mokymosi metodais [Steganography in artificial neural network parameters and its detection using machine learning methods]. Lietuvos magistrantų informatikos ir IT tyrimai, Vilnius University Open Series, 336–347. DOI: 10.15388/LMITT.2026.35.
  • Kurasova, O.; Medvedev, V.; Čypas, J. R.; Dautartas, J. (2025). Poster: Targeted evasion of malware detection using adversarial machine learning. 2025 23rd International Symposium on Network Computing and Applications (NCA), 308–309. DOI: 10.1109/NCA67271.2025.00056.
  • Žaliauskas, A.; Medvedev, V. (2025). Multimodalinių modelių taikymas vaizdų antraščių generavimui lietuvių kalba [Applying multimodal models to image caption generation in Lithuanian]. Lietuvos magistrantų informatikos ir IT tyrimai, Vilnius University Open Series, 271–280. DOI: 10.15388/MITT.2025.31.
  • Lukšys, M.; Medvedev, V. (2025). Požymių konvertavimo į vaizdus metodų palyginimas kenkėjiškų programų aptikimo efektyvumui gerinti [Comparing feature-to-image conversion methods for improving malware detection performance]. Lietuvos magistrantų informatikos ir IT tyrimai, Vilnius University Open Series, 142–151. DOI: 10.15388/LMITT.2025.17.
  • Čypas, J. R.; Medvedev, V.; Dautartas, J. (2025). Kenkėjiškų programų aptikimo gerinimas taikant kelių klasių gerybinės programinės įrangos analizę [Improving malware detection through the analysis of multiple benign software classes]. Lietuvos magistrantų informatikos ir IT tyrimai, Vilnius University Open Series, 24–27. DOI: 10.15388/LMITT.2025.3.
  • Dzemyda, G.; Kurasova, O.; Medvedev, V.; Šubonienė, A.; Kielaitė-Gulla, A.; Samuilis, A.; Jagminas, D.; Strupas, K. (2024). Optimal cut-off points for pancreatic cancer detection using deep learning techniques. Information Systems and Technologies (WorldCIST 2023), Vol. 1, Lecture Notes in Networks and Systems, 799, 559–569. DOI: 10.1007/978-3-031-45642-8_54.
  • Budžys, A.; Kurasova, O.; Medvedev, V. (2023). Behavioral biometrics authentication in critical infrastructure using siamese neural networks. HCI for Cybersecurity, Privacy and Trust (HCII 2023), Lecture Notes in Computer Science, 14045, 309–322. DOI: 10.1007/978-3-031-35822-7_21.
  • Medvedev, V.; Budžys, A.; Kurasova, O. (2023). Enhancing keystroke biometric authentication using deep learning techniques. 2023 18th Iberian Conference on Information Systems and Technologies (CISTI), 1–6. DOI: 10.23919/CISTI58278.2023.10211344.
  • Kurasova, O.; Medvedev, V.; Šubonienė, A.; Dzemyda, G.; Kielaitė-Gulla, A.; Samuilis, A.; Jagminas, D.; Strupas, K. (2023). Semi-supervised learning with pseudo-labeling for pancreatic cancer detection on CT scans. 2023 18th Iberian Conference on Information Systems and Technologies (CISTI), 1–6. DOI: 10.23919/CISTI58278.2023.10211356.
  • Dzemyda, G.; Medvedev, V.; Sabaliauskas, M. (2022). Multi-core implementation of geometric multidimensional scaling for large-scale data. Information Systems and Technologies (WorldCIST 2022), Vol. 2, Lecture Notes in Networks and Systems, 469, 74–82. DOI: 10.1007/978-3-031-04819-7_8.
  • Kurasova, O.; Marcinkevičius, V.; Medvedev, V.; Mikulskienė, B. (2021). Early cost estimation in customized furniture manufacturing using machine learning. International Journal of Machine Learning and Computing, 11(1), 28–33. DOI: 10.18178/ijmlc.2021.11.1.1010.
  • Dzemyda, G.; Kurasova, O.; Medvedev, V.; Dzemydaitė, G. (2019). Visualization of data: methods, software, and applications. Advances in Mathematical Methods and High Performance Computing, Advances in Mechanics and Mathematics, 41, 295–307. DOI: 10.1007/978-3-030-02487-1_18.
  • Bernatavičienė, J.; Dzemyda, G.; Kurasova, O.; Marcinkevičius, V.; Medvedev, V.; Treigys, P. (2016). Cloud computing approach for intelligent visualization of multidimensional data. Advances in Stochastic and Deterministic Global Optimization, Springer Optimization and Its Applications, 107, 73–85.
  • Medvedev, V.; Kurasova, O. (2016). Cloud technologies: a new level for Big Data Mining. Resource Management for Big Data Platforms: Algorithms, Modelling, and High-Performance Computing Techniques, 55–68. DOI: 10.1007/978-3-319-44881-7_3.
  • Dzemyda, G.; Medvedev, V.; Lupeikienė, A.; Kurasova, O.; Čaplinskas, A. (2016). Big multidimensional datasets visualization using neural networks - efficient decision support. Complex Systems Informatics and Modeling Quarterly, 6, 1–11. DOI: 10.7250/csimq.2016-6.01.
  • Kurasova, O.; Marcinkevičius, V.; Medvedev, V.; Rapečka, A.; Stefanovič, P. (2014). Strategies for big data clustering. 2014 IEEE 26th International Conference on Tools with Artificial Intelligence (ICTAI), 740–747. DOI: 10.1109/ICTAI.2014.115.
  • Dzemyda, G.; Marcinkevičius, V.; Medvedev, V. (2011). Large-scale multidimensional data visualization: a Web service for data mining. Towards a Service-Based Internet (ServiceWave 2011), Lecture Notes in Computer Science, 6994, 14–25.
  • Dzemyda, G.; Kurasova, O.; Medvedev, V. (2007). Dimension reduction and data visualization using neural networks. Emerging Artificial Intelligence Applications in Computer Engineering, Frontiers in Artificial Intelligence and Applications, 160, 25–49.
  • Ivanikovas, S.; Medvedev, V.; Dzemyda, G. (2007). Parallel realizations of the SAMANN algorithm. Lecture Notes in Computer Science, 4432, 179–188.
  • Bernatavičienė, J.; Dzemyda, G.; Kurasova, O.; Marcinkevičius, V.; Medvedev, V. (2007). The problem of visual analysis of multidimensional medical data. Models and Algorithms for Global Optimization, Springer Optimization and Its Applications, 4, 277–298. DOI: 10.1007/978-0-387-36721-7_17.
  • Medvedev, V.; Dzemyda, G. (2006). Speed up of the SAMANN neural network retraining. Lecture Notes in Artificial Intelligence, 4029, 94–103. DOI: 10.1007/11785231_11.
  • Medvedev, V.; Dzemyda, G. (2006). Retraining the neural network for data visualization. Artificial Intelligence Applications and Innovations (AIAI 2006), IFIP International Federation for Information Processing, 204, 27–34.

Accepted Publications

  • Medvedev, V.; Dautartas, J.; Čypas, J. R.; Kurasova, O. (2026). Poster: Beyond a Single Benign Class: What Functional Categories of Benign Software Reveal About Static Malware Detection. Proceedings of the 2026 ACM SIGSAC Conference on Computer and Communications Security (CCS ’26). DOI: 10.1145/3830454.3846425. Accepted for publication.
  • Rimšelis, J. M.; Medvedev, V.; Treigys, P.; Abramikas, Ž.; Markevičiūtė, J.; Bernatavičienė, J. (2026). Cross-Dataset Component Ablation of a Hybrid Model for Robust Beat-Level ECG Arrhythmia Classification. Position Papers of the 21st Conference on Computer Science and Intelligence Systems (FedCSIS), Annals of Computer Science and Information Systems, 48, 149–155. Accepted for publication.

Research Datasets

  • Dautartas, J.; Kurasova, O.; Čypas, J. R.; Medvedev, V. (2026). WinAPI-AdvMal: A Six-Class Windows API Import Dataset for Adversarial Malware. DOI: 10.5281/zenodo.20208958.
  • Kurasova, O.; Medvedev, V.; Dautartas, J.; Budžys, A.; Jomantas, H.; Čypas, J. R. (2025). WinAPI-4C-AdvMal: Windows API features for adversarial malware. DOI: 10.18279/MIDAS.265677.
  • Bernatavičienė, J.; Štikonienė, O.; Račkauskas, A.; Leipus, R.; Markevičiūtė, J.; Levulienė, R.; Treigys, P.; Medvedev, V. (2021). COVID-19 infekcija Lietuvoje: modeliavimas ir socio-ekonominių padarinių analizė [COVID-19 infection in Lithuania: modelling and analysis of socioeconomic consequences]. DOI: 10.18279/MIDAS.MIFKOR.163019.
  • Medvedev, V.; Dzemyda, G.; Dzemydaitė, G.; Kurasova, O. (2017). Ekonomikos regioninio vystymo duomenys vizualiai analizei [Data for visual analysis of regional economic development]. DOI: 10.18279/MIDAS.DVARED.30016.

Preprints

Dautartas, J.; Kurasova, O.; Čypas, J. R.; Medvedev, V. (2026). Learning to Look Benign: Targeted Evasion of Malware Detectors via API Import Injection. arXiv, arXiv:2605.18624. DOI: 10.48550/arXiv.2605.18624.

Research Projects

Research Projects

  • 2025–2027 – “Denoising Diffusion Probabilistic Models for Enhanced ECG Signal Noise Reduction in Arrhythmia Classification and AFIB Detection”, No. S-ITP-25-9. Funded by the Research Council of Lithuania under the programme “Information Technologies for the Development of Science and the Knowledge Society”. Role: primary project implementer. Project leader: Dr Jolita Bernatavičienė.
  • 2024–2027 – “Adversarial Machine-Learning Enhanced C2 Framework” (AML-C2), No. S-MIP-24-116. Researcher Group Project funded by the Research Council of Lithuania. Role: project team member. Project leader: Prof. Olga Kurasova.
  • 2023–2027 – “Data Centre for Machine Learning and Quantum Computing in Natural and Biomedical Sciences”. Funded under the University Excellence Initiative. Role: research team member.
  • 2020–2022 – “Geometric Method for Solving the Multidimensional Scaling Problem”, No. S-MIP-20-19. Researcher Group Project funded by the Research Council of Lithuania. Project leader: Prof. Gintautas Dzemyda.
  • June–December 2020 – “COVID-19 Infection in Lithuania: Modelling and Analysis of Socioeconomic Consequences”, No. P-COV-20-34. Funded by the Research Council of Lithuania to investigate the consequences of the COVID-19 pandemic. Project leader: Dr Olga Štikonienė.
  • 2018–2020 – participation in “Development of Lithuanian Speech-Controlled Services – LIEPA 2”, No. 02.3.1-CPVA-V-527-01-0001. Funded by EU Structural Funds; the full project period was 2017–2020.
  • 2012–2014 – “Theoretical and Engineering Aspects of e-Service Technology Development and Application in High-Performance Computing Platforms”, No. VP1-3.1-ŠMM-08-K-01-010.
  • 2011–2013 – “Production Effectiveness Navigator” (E!6232 PEN). Eurostars joint programme project, registration No. 31V-134/LSS-58000-1405. International partners from Türkiye, Poland and Hungary.
  • 2009–2010 – “Application of Data Mining Methods to the Investigation of Software Systems”. Research project funded by the Lithuanian State Science and Studies Foundation.
  • 2009–2010 – “Application of Data Mining Methods to the Investigation of Software Systems”. Project under the Lithuanian–French Integrated Action Programme “Gilibert” (“Žiliberas”).
  • 2008 – “Development of Specialised Data Analysis Methods for Investigating the Thermal Anisotropy of Cardiac Tissue”, No. T-106/08. Researcher Group Project funded by the Lithuanian State Science and Studies Foundation.
  • 2007–2009 – “Information Technology Tools for Clinical Decision Support and Public Health Promotion in an e-Health System” (“Info sveikata”). Funded under the High Technology Development Programme of the Lithuanian State Science and Studies Foundation.
  • 2003–2009 – “Information Technologies for Human Health: Clinical Decision Support (e-Health)” (“IT sveikata”). Project under a priority research and experimental development programme funded by the Lithuanian State Science and Studies Foundation.

Contract Research

“Development of Data Analysis Methods and Tools”, under an agreement between Informatikos mokslų centras and Vilnius University dated 12 February 2013.

COST Actions

  • COST Action IC1406, “High-Performance Modelling and Simulation for Big Data Applications” (cHiPSet). Lithuanian representative on the Management Committee. Action period: 2015–2019.
  • COST Action TD1403, “Big Data Era in Sky and Earth Observation” (BIG-SKY-EARTH). Substitute Lithuanian representative on the Management Committee. Action period: 2015–2019.

Conferences, Research Visits and International Cooperation

Scientific Conferences and Seminars

  • 2009–2025 – participation in and contribution to the organisation of various editions of the International Conference on Data Analysis Methods for Software Systems (DAMSS), Druskininkai, Lithuania.
  • 20–23 June 2023 – 18th Iberian Conference on Information Systems and Technologies (CISTI 2023), Aveiro, Portugal.
  • 3–6 July 2022 – 32nd European Conference on Operational Research (EURO 2022), Espoo / Helsinki, Finland.
  • 16–18 May 2018 – 2nd World Intelligence Congress, Tianjin, China.
  • 13–16 June 2017 – 9th China Cloud Computing Conference, Beijing, China.
  • 10–12 November 2014 – IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2014), Limassol, Cyprus.
  • 26–29 May 2010 – 15th International Conference Mathematical Modelling and Analysis, Druskininkai, Lithuania.
  • 2–5 September 2009 – Computer Graphics, Vision and Mathematics (GraVisMa 2009), Plzeň, Czechia.
  • 30 June–3 July 2009 – 13th International Conference on Applied Stochastic Models and Data Analysis (ASMDA 2009), Vilnius, Lithuania.
  • 18–19 June 2009 – 50th Conference of the Lithuanian Mathematical Society, Vilnius, Lithuania.
  • 20–23 May 2008 – Continuous Optimization and Knowledge-Based Technologies (EurOPT 2008), Neringa, Lithuania.
  • 11–14 April 2007 – 8th International Conference on Adaptive and Natural Computing Algorithms (ICANNGA 2007), Warsaw, Poland. Best Young Researcher Paper Award in the area of Neural Networks.
  • 25–29 June 2006 – 8th International Conference on Artificial Intelligence and Soft Computing (ICAISC 2006), Zakopane, Poland. Best Presentation Award.
  • 21–27 January 2006 – 32nd International Conference on Current Trends in Theory and Practice of Computer Science (SOFSEM 2006), Měřín, Czechia.
  • 15–17 September 2005 – “Kompiuterininkų dienos 2005” (Computer Days 2005), Klaipėda, Lithuania.
  • 15–16 June 2005 – 46th Conference of the Lithuanian Mathematical Society, Vilnius, Lithuania.
  • 19–21 May 2005 – Workshop of the European Chapter on Metaheuristics: Metaheuristics and Large Scale Optimization, Vilnius, Lithuania.

COST Meetings and Research Seminars

  • 28–29 March 2019 – final Management Committee meeting of COST Action IC1406 cHiPSet, Vilnius, Lithuania.
  • March 2018 – Management Committee and working-group meetings and a research seminar, COST Action IC1406 cHiPSet, Fontainebleau, France.
  • March 2017 – Management Committee meeting and research seminar, COST Action IC1406 cHiPSet, Palermo, Italy.
  • December 2016 – working-group meetings and research seminar, COST Action IC1406 cHiPSet, Luxembourg.
  • April 2016 – Management Committee and working-group meetings and a research seminar, COST Action IC1406 cHiPSet, Dublin, Ireland.
  • April 2016 – working-group meetings and research seminar, COST Action TD1403 BIG-SKY-EARTH, Brno, Czechia.
  • September 2015 – Management Committee meetings, COST Action IC1406 cHiPSet, Kraków, Poland.
  • March–April 2015 – Management Committee and working-group meetings, COST Action TD1403 BIG-SKY-EARTH, Belgrade, Serbia.

International Cooperation and Expert Meetings

  • May 2018 – Belt and Road Forum and cooperation meetings on big data and cloud computing, Tianjin, China.
  • December 2017 – LiTurCo Lithuanian–Turkish research cooperation event, Eskişehir, Türkiye.
  • November 2017 – European Commission ICT Proposers’ Day, Budapest, Hungary.
  • October 2017 – ScanBalt Forum 2017, Tallinn, Estonia.
  • June 2017 – Belt and Road Forum and cooperation meetings with representatives of the China Association for Science and Technology and the Chinese Institute of Electronics on big data and cloud computing, Beijing, China.
  • September 2016 – European Commission ICT Proposers’ Day, Bratislava, Slovakia.
  • October 2014 – European Commission ICT Proposers’ Day, Florence, Italy.
  • July and September 2013 – meetings of the Information and Communication Technologies Programme Committee of the EU Seventh Framework Programme (FP7), Brussels, Belgium.
  • September 2012 – European Commission ICT Proposers’ Day, Warsaw, Poland.
  • May 2011 – European Commission ICT Proposers’ Day, Budapest, Hungary.
  • December 2010 – research cooperation visit and meetings on cooperation agreements, Geneva, Switzerland.
  • September 2010 – European Commission information and communication technologies event, Brussels, Belgium.

Accepted Forthcoming Presentations

15–19 November 2026 – ACM SIGSAC Conference on Computer and Communications Security (CCS 2026), The Hague, Netherlands. Accepted poster: “Poster: Beyond a Single Benign Class: What Functional Categories of Benign Software Reveal About Static Malware Detection”. Authors: Viktor Medvedev, Juozas Dautartas, Juozapas Rokas Čypas and Olga Kurasova.

Professional Memberships and Expert Evaluation

International Organisations

  • Lithuanian representative, through the Lithuanian Computer Society (LIKS), on Technical Committee 11 (TC11), “Security and Privacy Protection in Information Processing Systems”, of the International Federation for Information Processing (IFIP).
  • Member of the Association for Computing Machinery (ACM) since September 2026.
  • Member of the Institute of Electrical and Electronics Engineers (IEEE).
  • Member of the IEEE Signal Processing Society.
  • Member of the IEEE Computer Society.
  • Participation in the IEEE Computer Society Task Force on Biological, Artificial, and Hybrid Intelligence.
  • IEEE Computer Society technical communities: Big Data; Cyber Security; Cloud Computing; Intelligent Informatics; Security and Privacy; Pattern Analysis and Machine Intelligence.

Lithuanian Organisations

  • Member of the Lithuanian Computer Society (LIKS); Head of its Big Data and Cloud Computing Section.
  • Member of the Lithuanian Mathematical Society.
  • Member of the AI Developers Group of the Lithuanian AI Governance Forum (2025).

Expert Evaluation

  • Expert evaluation for Innovation Agency Lithuania, the Agency for Science, Innovation and Technology (MITA), the Lithuanian Business Support Agency (LVPA), and the Central Project Management Agency (CPVA).
  • Lithuanian expert on the Information and Communication Technologies Programme Committee under the “Cooperation” Specific Programme of the EU Seventh Framework Programme (FP7) (Ministry of Education and Science; MITA), 2013.

Science Communication

Kurasova, O.; Marcinkevičius, V.; Medvedev, V. (2015). DAMIS – nacionalinio mokslo informacijos archyvo MIDAS duomenų analizės įrankis [DAMIS: A data analysis tool for the national research data archive MIDAS]. Mokslas ir technika, No. 3. [In Lithuanian.]

PhD Supervision and Teaching

PhD Supervision

Juozas Dautartas

  • Start of doctoral studies: 1 October 2023. Expected completion: 30 September 2027.
  • Research field: Informatics Engineering (T 007).
  • Dissertation topic: “Machine Learning-Based Methods for Generating Deceptive and Obfuscated Malware to Strengthen Cybersecurity”.
  • Status: ongoing.

Arnoldas Budžys

  • Period of doctoral studies: 1 October 2020–30 September 2024.
  • Research field: Informatics (N 009).
  • Dissertation: “Deep Learning-Based Keystroke Dynamics Authentication for Insider Threat Detection in Critical Infrastructure”.
  • Defended on 1 July 2025.

Teaching and Student Supervision

  • Fundamentals of Artificial Intelligence – bachelor's-level course, taught since 2019.
  • Big Data Analytics – doctoral-level course.
  • Adversarial Machine Learning – doctoral course syllabus prepared in 2026.
  • Doctoral consultations on Deep Neural Networks and Parallel and Distributed Computing.
  • Supervision of bachelor's and master's theses in artificial intelligence and data analysis.
  • Reviewing final theses and serving on thesis defence committees.