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Prof. Habil. Dr. Gintautas Dzemyda

Department: Cognitive Computing Group
Position: VU Institute of Data Science and Digital Technologies, Director, Principal Researcher, Professor, Head of Group

Address: Akademijos st. 4, room 207, room 615, Vilnius
Tel: (+370 5) 210 9302
E-mail:  

 

 

 

Scientific and Pedagogical Background

Gintautas Dzemyda was born in Vilnius, Lithuania, on 1957. Doctoral studies from 1981 till 1984 at the Institute of Mathematics and Cybernetics, Vilnius, Lithuania. At 1984, he received the doctoral degree in technical sciences (PhD), and in 1997 he received the degree of Doctor Habilius from the Kaunas University of Technology. He was conferred the title of Professor (1998) at the Kaunas University of Technology and the title of Professor (2018) at the Vilnius University. Doctor Honoris Causa of the University of Latvia (2019).

 

Full member of the Lithuanian Academy of Sciences (2011).

Chief of Division of  Technical Sciences of the Lithuanian Acdemy of Sciences.

He was awarded the Lithuanian National Science Award twice (2001 and 2021) and Order for Merit for Lithuania from President of Lithuania (2007). 

 

Since 2005, he is a Director of the Vilnius University Institute of Mathematics and Informatics. Recent employment is at the Vilnius University Institute of Mathematics and Informatics as Director of the Institute, Professor, Principal Researcher, Head of Cognitive Computing Group.

 

Editorial Boards. Editor in Chief of two international journals: - Informatica (www.mii.lt/informatica); - Baltic Journal of Modern Computing (http://www.lu.lv/baltic-journal-of-modern-computing/). Member of Editorial Board of the international journals: - Financial Innovation; - International Journal of Computers, Communications and Control; - Nonlinear Analysis: Modelling and Control; - Informatics in Education; - Journal of Civil Engineering and Management; - Mathematics and Informatics. Journal of the Belarusian State University; - Scientific Proceedings of Riga Technical University. Computer Science, Information Technology and Management Science; - Applied Computer Systems.

 

Expertise in Governmental institutions: - Member of Scientific Council of the Lithuanian National M. Mažvydas Library; - Expert of the Lithuanian Agency for Science, Innovation and Technology

 

Member of research societies and committees: - Lithuanian Computer Society; - Lithuanian Mathematical Society; - IFIP Technical Committee 12 Artificial Intelligence;  - ACM.

 Chairman of the international conferences: - International Workshop “Data Analysis Methods for Software Systems“, every year starting from 2009 (www.mii.lt/damss/); - 10th and 13th International Baltic Conference on Databases and Information Systems (www.mii.lt/BalticDBIS2012/, www.mii.lt/balticDBIS2018/), - World Conference on Information Systems and Technologies, WorldCist, 2021, 2022, 2023.

 

Research Work

Research interests. The main area of ​​scientific interest is the development and application of data science methods and technologies including artificial intelligence ones. Research includes the following main directions: reduction of data dimensionality and visualization; optimization theory and applications; visual multidimensional data analysis, artificial neural networks, parallel computing, multicriteria decision making, artificial intelligence, cognitive computing, image and signal analysis. Medical applications include numerical and visual data of various origins - ophthalmologic, cardiological, physiological, thermovision. Computed tomography images are also being investigated. A lot of software is designed both for general purposes and for applications. Various problems of computer-aided design have been solved, too. The author of more than 270 scientific publications, two monographs, five textbooks.

Internationally recognized research group for visual analysis and dimensionality reduction of multidimensional data has been established and developed in Lithuania. 15 doctoral students successfully defenced their PhD thesis and got doctoral degree under his supervision.

 

Scientific publications

Publications with VU Institute of Data Science and Digital Technologies & Institute of Mathematics and Informatics affiliation

 

The mail publications 2005-2017

 

Monograph

Dzemyda, G., O.Kurasova, J.Žilinskas (2013). Multidimensional Data Visualization: Methods and Applications. Springer Optimization and Its Applications, Vol. 75, Springer, New York, Heidelberg, Dordrecht, London, 250 p. 122 illus., 38 in color. ISSN 1931-6828, ISBN 978-1-4419-0235-1, ISBN 978-1-4419-0236-8 (eBook), DOI 10.1007/978-1-4419-0236-8

 

Research papers

 

  1. Dzemyda, G. (2005). Multidimensional data visualization in the statistical analysis of curricula. Computational Statistics & Data Analysis, 49, 265-281.
  2. Dzemyda, G., O. Kurasova (2006). Heuristic approach for minimizing the projection error in the integrated mapping. European Journal of Operation Research, 171, 859-878.
  3. Bernatavičienė, J., G. Dzemyda, O. Kurasova, V. Marcinkeviius (2006). Optimal decisions in combining the som with nonlinear projection methods. European Journal of Operation Research, 173, 729-745.
  4. Medvedev, V., G.Dzemyda (2006). Optimization of the local search in the training for SAMANN neural network. Journal of Global Optimization, 35, 607-623.
  5. Medvedev, V., G.Dzemyda (2006). Speed up of the SAMANN neural network retraining. Artificial Intelligence and Soft Computing – 8th ICAISC, Lecture Notes in Artificial Intelligence, LNSC 4029. pp. 94-103.
  6. [6]   Dagienė, V., G.Dzemyda, M.Sapagovas (2006). Evolution of the cultural-based paradigm for informatics education in Secondary schools – two decades of Lithuanian experience. Informatics Education – The Bridge between Using and Understanding Computers, Lecture Notes in Artificial Intelligence, LNSC 4226. pp. 1-12.
  7. Paunksnis, A., V. Barzdžiukas, D. Jegelevičius, S. Kurapkienė, G. Dzemyda (2006). The use of information technologies for diagnosis in ophthalmology. Journal of Telemedicine and Telecare, 12, Suppl. 1, 37-40.
  8. Bernatavičienė, J., G.Dzemyda, O.Kurasova, V.Marcinkevičius, V.Medvedev (2007). The problem of visual analysis of multidimensional medical data. In A.Torn, J.Žilinskas (Eds.), Models and Algorithms for Global Optimization. Optimization and Its Applications, Vol. 4. Springer. pp. 277-298.
  9. Dzemyda, G., O. Kurasova, V.Medvedev (2007). Dimension reduction and data visualization using neural networks. In I.Maglogiannis, K.Karpouzis, M.Wallace and J.Soldatos (Eds.), Emerging Artificial Intelligence Applications in Computer Engineering - Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies.  Frontiers in Artificial Intelligence and Applications, Vol. 160. IOS Press. pp. 25-49.
  10. Bernatavičienė, J., G. Dzemyda, V. Marcinkeviius (2007).Conditions for optimal efficiency of relative MDS. Informatica, 18 (2), 187-202.
  11. Dzemyda, G., O. Kurasova (2007). Dimensionality problem in the visualization of correlation-based data. 8th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 200, Lecture Notes in Computer Science, LNCS 4432. pp. 544-553.
  12. Ivanikovas, S., V.Medvedev, G.Dzemyda (2007). Parallel realizations of the SAMANN algorithm. 8th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 200, Lecture Notes in Computer Science, LNCS 4432. pp. 179-188.
  13. Kurasova, O., G.Dzemyda, A.Vainoras (2007). Parameter system for human physiological data representation and analysis. Pattern Recognition and Image Analysis - Third Iberian Conference, IbPRIA 2007, Lecture Notes in Computer Science, LNCS 4477. pp. 209-216.
  14. Treigys, P., G.Dzemyda, V.Barzdžiukas (2008). Automated positioning of overlapping eye fundus images. Computational Science – ICCS 2008. Lecture Notes in Computer Science, LNCS 5101. pp. 770-779.
  15. Ivanikovas, S., G.Dzemyda, V.Medvedev (2008). Large datasets visualization with neural network using clustered training data. Advances in Databases and Information Systems. Lecture Notes in Computer Science, LNCS  5207. pp. 143-152.
  16. Treigys, P., V.Šaltenis, G.Dzemyda, V.Barzdžiukas, A.Paunksnis (2008). Automated optic nerve disc parameterization. Informatica, 19 (3), 403-420.
  17. Dzemyda, G., L. Sakalauskas (2009). Optimization and knowledge-based technologies. Informatica, 20 (3), 165-172.
  18. Ivanikovas S., G.Dzemyda, V.Medvedev (2009) Influence of the neuron activation function on the multidimensional data visualization quality. The XIII International Conference Applied Stochastic Models and Data Analysis ASMDA-2009. Vilnius, Technika. pp. 299-303.
  19. Karbauskaitė, R., G.Dzemyda (2009). Dependence of the Laplacian eigenmaps method and its modification on the parameters. The XIII International Conference Applied Stochastic Models and Data Analysis ASMDA-2009.  Vilnius, Technika, 2009. pp. 263-268.
  20. Lekas, R., P.Jakuška, A.Kriščiukaitis, V.Veikutis, G.Dzemyda, T.Mickevičius, K.Morkūnaitė, A.Vilkė, P.Treigys, G.Civinskienė, J.Andriuškevičius, T.Vanagas, K.Skauminas, J.Bernatonienė (2009). Monitoring changes in heart tissue temperature and evaluation of graft function after coronary artery bypass grafting surgery. Medicina, 45 (3), 221-225.
  21. Dzemyda, G., L. Sakalauskas (2009). Optimization and knowledge-based technologies. Informatica, 20 (3), 165-172.
  22. Karbauskaite, R., G.Dzemyda (2009). Topology preservation measures in the visualization of manifold-type multidimensional data. Informatica, 20 (3), 235-254.
  23. Veikutis, V., P. Jakuska, G. Dzemyda, A. Puodziukynas, A. Siudikas, T. Kazakevicius, V. Sileikis, V. Zabiela, K. Morkunaite, T. Mickevičius, V. Narbutas, V. Jokuzis (2009). Thermovision in cardiac surgery practice: new viewpoints and possibilities. Cardiology, 113, Suppl. 1, 75-76.
  24. Veikutis, V., G. Dzemyda, P. Treigys, K. Morkūnaitė, A. Basevičius, S. Lukoševičius, G. Šakalytė, A. Sederevičius (2010). Analysis of thermovisual data of the radio-frequency impact on the myocardium damage. Informatica, 21 (3), 455-470.
  25. Karbauskaitė, R., G. Dzemyda and V. Marcinkevičius (2010). Dependence of locally linear embedding on the regularization parameter. TOP, An Official Journal of the Spanish Society of Statistics and Operations Research,  18 (2), 354-376.
  26. Dzemyda, G., L. Sakalauskas (2011). Large-scale data analysis using heuristic methods. Informatica, 22 (1), 1-10.
  27. Dzemyda, G., V. Marcinkevičius, V. Medvedev (2011). Web application for large-scale multidimensional data visualization. Mathematical Modelling and Analysis, 16 (2), 273-285.
  28. Kaklauskas,A., E.K.Zavadskas, M.Seniut, G.Dzemyda, V.Stankevic, C.Simkevičius, T.Stankevic, R.Paliskiene, A.Matuliauskaite, S.Kilviene, L.Bartkiene, S.Ivanikovas, V.Gribniak (2011). Web-based biometric computer mouse advisory system to analyze a user’s emotions and work productivity. Engineering Applications of Artificial Intelligence, 24 (6), 928-945.
  29. Kaklauskas, A., E.K. Zavadskas, V. Pruskus, A. Vlasenko, L. Bartkiene, R. Paliskiene, L. Zemeckyte, V. Gerstein, G. Dzemyda, G. Tamulevicius (2011). Recommended biometric stress management system. Expert Systems with Applications, 38 (11), 14011-14025.
  30. Karbauskaitė, R., G.Dzemyda, E.Mazėtis (2011). Geodesic distances in the maximum fikelihood estimator of intrinsic dimensionality. Nonlinear Analysis: Modelling and Control, 16 (4), 387-402.
  31. Medvedev, V., G.Dzemyda, O.Kurasova, V.Marcinkevičius (2011). Efficient data projection for visual analysis of large data sets using neural networks. Informatica, 22 (4), 507-520.
  32. Dzemyda, G., V. Marcinkevičius, V. Medvedev (2011). Large-scale multidimensional data visualization: a web service for data mining. Towards a Service-Based Internet, 4th European Conference, ServiceWave 2011. Lecture Notes in Computer Science, Vol. 6994. pp. 14-25.
  33. Kaklauskas, A., M.Seniut, E.K.Zavadskas, G.Dzemyda, V.Stankevic, C.Simkevicius, S.Ivanikovas, T.Stankevic, A.Matuliauskaite, L.Zemeckyte (2011). Recommender system to analyse students’ learning productivity. Informatics in Control, Automation and Robotics, Vol. 2. Lecture Notes in Electrical Engineering, Vol. 133. pp. 161-164.
  34. Balkys, G., G.Dzemyda (2012). Segmenting the eye fundus images for identification of blood vessels. Mathematical Modelling and Analysis, 17 (1), 21-30.
  35. Pragarauskaitė, J., G.Dzemyda (2012). Visual decisions in the analysis of customers online shopping behavior. Nonlinear Analysis: Modelling and Control, 17 (3), 355-368.
  36. Caplinskas, A., G.Dzemyda, F.Kiss, A.Lupeikiene (2012). Processing of undesirable business events in advanced production planning systems. Informatica, 23 (4), 563-579. 
  37. Pragarauskaitė J., G.Dzemyda (2013). Markov models in the analysis of frequent patterns in financial data. Informatica, 23 (4), 563-579. 
  38. Karbauskaitė, R., G.Dzemyda (2014). Geodesic distances in the intrinsic dimensionality estimation using packing numbers. Nonlinear Analysis: Modelling and Control, 19 (4), 578–591.
  39. Lupeikienė, A., G. Dzemyda, F. Kiss, A. Čaplinskas (2014). Advanced planning and scheduling systems: modelling and implementation challenges. Informatica, 25 (4), 581–616. 
  40. Karbauskaitė, R., G. Dzemyda (2015). Optimization of the maximum likelihood estimator for determining the intrinsic dimensionality of high–dimensionaldata. International Journal of Applied Mathematics and Computer Science, 25 (4), 895-913.
  41. Evora, J., J. J. Hernandez, M. Hernandez, G. Dzemyda, O. Kurasova, E. Kremers (2015). Swarm intelligence for frequency management in smart grids. Informatica, 26 (3), 419-434.
  42. Bernatavičienė, J., G. Dzemyda, G. Bazilevičius, V. Medvedev, V. Marcinkevičius, P. Treigys (2015). Method for visual detection of similarities in medical streaming data. International Journal of Computers Communications & Control, 10 (1), 8-21. 
  43. Bernatavičienė, J., G.Dzemyda, O.Kurasova, V.Marcinkevičius, V.Medvedev, P.Treigys (2016). Cloud computing approach for intelligent visualization of multidimensional data. In P.M.Pardalos, A.Zhigljavsky, J.Žilinskas (Eds.), Advances in Stochastic and Deterministic Global Optimization, Ser.: Springer Optimization and its Applications. Vol. 107. Springer. pp. 73-85.
  44. Bilinskas, M.J., G. Dzemyda, M. Trakymas (2017). Feature-based registration of thorax CT scan slices. Informatica, 28 (3), 439-452. DOI: http://dx.doi.org/10.15388/Informatica.2017.137
  45. Bilinskas, M.J., G. Dzemyda, M. Sabaliauskas (2017). Speeding-up the fitting of the model defining the ribs-bounded contour. Applied Computer Systems, 21 (1), 66-70. DOI: http://dx.doi.org/10.1515/acss-2017-0009
  46. Stabingis, G., J. Bernatavičienė, G. Dzemyda,A. Paunksnis, P. Treigys, R. Vaičaitienė, L. Stabingienė (2017). Automatization of eye fundus vessel width measurements. In J.M..R.S. Tavares, R.M. Natal Jorge (Eds), VipIMAGE 2017: Proceedings of the VI ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing. Lecture Notes in Computational Vision and Biomechanics, Vol. 27. Springer. pp. 787-796. DOI: 10.1007/978-3-319-68195-5.
  47. Tamulevičius, G., R. Karbauskaitė, G.Dzemyda (2017). Selection of fractal dimension features for speech emotion classification. In Proceedings “2017 Open Conference of Electrical, Electronic snd Information Sciences (ESTREAM)”. New York, IEEE. pp. 1-4. DOI: 10.1109/eStream.2017.7950316
  48. Jucevičius, J., P.Treigys, J.Bernatavicienė, R.Briedienė, I.Naruševičiūtė, G.Dzemyda, V.Medvedev (2017). Automated 2D Segmentation of Prostate in T2-weighted MRI Scans. International Journal of Computers Communications & Control, 12 (1), 53-60. DOI: http://dx.doi.org/10.15837/ijccc.2017.1
  49. 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: http://dx.doi.org/10.1016/j.simpat.2017.03.001
  50. Bilinskas, M.J., G. Dzemyda, M. Trakymas (2018). Approximation of the ribs-bounded contour in a Tomography scan slice. International Journal of Information Technology & Decision Making, 17 (1), 83-102. DOI: https://doi.org/10.1142/S0219622017500298
  51. Gėgžna, V., O. Kurasova, G. Dzemyda, R. Kurtinaitienė, I. Čiplys, J. V. Vaitkus, A. Vaitkuvienė (2018). The ROC-based analysis of spectroscopic signals from medical specimens. Nonlinear Analysis: Modelling and Control, 23 (3), 285-302. DOI: https://doi.org/10.15388/NA.2018.3.1
  52. Kaklauskas, A., G. Dzemyda, L. Tupenaite, I. Voitau, O. Kurasova, J. Naimaviciene, Y. Rassokha, L. Kanapeckiene (2018). Artificial neural network-based decision support system for development of an energy-efficient built environment. Energies, 11 (8), 1994. DOI: https://doi.org/10.3390/en11081994
  53. Dzemyda, G. (2018). Data science and advanced digital technologies. In: A. Lupeikienė et al. (Eds.), Databases and Information Systems, 13th International Baltic Conference (DB&IS 2018). Communications in Computer and Information Science, Vol. 838, Springer. pp. 3-7. DOI: https://doi.org/10.1007/978-3-319-97571-9_1.
  54. Kaklauskas A., D. Jokubauskas, J. Cerkauskas, G. Dzemyda, I. Ubarte, D. Skirmantas,A. Podviezko, I. Simkute (2019). Affective analytics of demonstration sites. Engineering Applications of Artificial Intelligence, 81, 346–372.
  55. Kaklauskas, A., Zavadskas, E. K., Bardauskiene, D., Cerkauskas, J., Ubarte, I., Seniuta, M., Dzemyda, G., Kaklauskaite, M., Vinogradova, I., Velykorusova, A. (2019).  An affect-based built environment video analytics. Automation in Construction, Vol. 106, October 2019, 102888.
  56. Tamulevičius, G., Karbauskaitė, R., Dzemyda, G. (2019). Speech emotion classification using fractal dimension-based features. Nonlinear Analysis: Modelling and Control, 24 (5), 679–695. DOI: https://doi.org/10.15388/NA.2019.5.1
  57. Dzemyda, G, Sabaliauskas M. (2019). New method to minimize the stress in multidimensional scaling. In: Filzmoser P., Kharin Yu. (Eds.), Computer Data Analysis and Modeling: Stochastics and Data Science: Proc. of the Twelfth Intern. Conf. BSU, Minsk. pp.  29–31.
  58. Dzemyda, G, Sabaliauskas M. (2020). A novel geometric approach to the problem of multidimensional scaling. In: Sergeyev Y. D., Kvasov D. E. (Eds.), Numerical Computations: Theory and Algorithms, NUMTA 2019. Lecture Notes in Computer Science, Vol. 11974, Springer. pp. 354–361. https://doi.org/10.1007/978-3-030-40616-5_30
  59. Kaklauskas, A., Zavadskas, E. K., Schuller, B., Lepkova, N., Dzemyda, G., Sliogeriene, J., Kurasova, O. (2020). Customized ViNeRS method for video neuro-advertising of green housing. International Journal of Environmental Research and Publick Health, 17 (7), 2244. https://doi:10.3390/ijerph17072244
  60. Daranda, A., Dzemyda, G. (2020). Navigation decision support: Discover of vessel traffic anomaly according to the historic marine data. International Journal of Computers Communications & Control, 15 (3), 3864. https://doi.org/10.15837/ijccc.2020.3.3864
  61. Kaklauskas, A., Abraham, A., Dzemyda, G., Raslanas, S., Seniut, M., Ubarte, I., Kurasova, O., Binkyte-Veliene, A., Cerkauskas, J. (2020). Emotional, affective and biometrical states analytics of a built environment. Engineering Applications of Artificial Intelligence, 91, 103621. https://doi.org/10.1016/j.engappai.2020.103621
  62. Sabaliauskas M., Dzemyda G. (2020) Visual Analysis of Multidimensional Scaling Using GeoGebra. In: Dzitac I., Dzitac S., Filip F., Kacprzyk J., Manolescu MJ., Oros H. (Eds.), Intelligent Methods in Computing, Communications and Control. ICCCC 2020. Advances in Intelligent Systems and Computing, Vol 1243. Springer, Cham. pp. 179-187. https://doi.org/10.1007/978-3-030-53651-0_15
  63. Karbauskaitė, R., Sakalauskas, L., Dzemyda, G. (2020). Kriging predictor for facial emotion recognition using numerical proximities of human emotions. Informatica, 31 (2), 249–275. 
  64. Dzemyda G., Sabaliauskas M. (2021). Geometric multidimensional scaling: A new approach for data dimensionality reduction. Applied Mathematics and Computation, Vol. 409, 125561.  https://doi.org/10.1016/j.amc.2020.125561
  65. Melnik-Leroy, G.A., Dzemyda, G. (2021). How to influence the results of MCDM? – Evidence of the impact of cognitive biases. Mathematics, 9 (2), 121. https://doi.org/10.3390/math9020121
  66. Poce, I., Arsenjeva, J., Kielaite-Gulla, A., Samuilis, A., Strupas, K., Dzemyda, G. (2021).  Pancreas segmentation in CT Images: State of the art in clinical practice. Baltic Journal of Modern Computing, 9 (1), 25–34 https://doi.org/10.22364/bjmc.2021.9.1.02 
  67. Daranda, A, Dzemyda, G. (2021). Artificial intelligence based strategy for vessel decision support system. In A. Rocha et al. (Eds.): Trends and Applications in Information Systems and Technologies. WorldCIST 2021. Advances in Intelligent Systems and Computing, Vol 1365. Springer, Cham. pp. 49–58. https://doi.org/10.1007/978-3-030-72657-7_5
  68. Dzemyda, G., Sabaliauskas, M. (2021). New capabilities of the geometric multidimensional scaling. In A. Rocha et al. (Eds.): Trends and Applications in Information Systems and Technologies. WorldCIST 2021. Advances in Intelligent Systems and Computing, Vol 1366. Springer, Cham. pp. 264–273. https://doi.org/10.1007/978-3-030-72651-5_26
  69. Kielaite-Gulla, A., Samuilis, A., Raisutis, A.R., Dzemyda, G., Strupas, K. (2021). The concept of AI-based algorithm: Analysis of CEUS images and HSPs for identification of early parenchymal changes in severe acute pancreatitis. Informatica, 32 (2), 305–319. https://doi.org/10.15388/21-INFOR453
  70. Daranda, A., Dzemyda, G. (2021). Novel Machine learning approach for Self-Aware prediction based on the Contextual reasoning. International Journal of Computers Communications & Control, 16 (4), 4345. https://doi.org/10.15837/ijccc.2021.4.4345
  71. Dzemyda, G., Sabaliauskas, M. (2022). On the computational efficiency of Geometric multidimensional scaling. In: 2021 2nd European Symposium on Software Engineering (ESSE 2021). ACM, New York. pp. 136-141. https://doi.org/10.1145/3501774.3501794
  72. Dzemyda, G., Medvedev, V., Sabaliauskas, M. (2022). Multi-core implementation of geometric multidimensional scaling for large-scale data. In: A. Rocha et al. (Eds.): Information Systems and Technologies. WorldCIST 2022, Volume 2. Lecture Notes in Networks and Systems, Vol. 469. Springer, Cham. pp. 74–82. https://doi.org/10.1007/978-3-031-04819-7_8
  73. Dzemyda, G., Sabaliauskas, M., Medvedev, V. (2022). Geometric MDS performance for large data dimensionality reduction and visualization. Informatica, 33 (2), 299–320. https://doi.org/10.15388/22-INFOR491
  74. Dzemyda, G., Sabaliauskas, M. (2022). Geometric multidimensional scaling: efficient approach for data dimensionality reduction. Journal of Global Optimization. https://doi.org/10.1007/s10898-022-01190-8
  75. Daranda, A., Dzemyda, G. (2022).  Reinforcement learning strategies for vessel navigation. Integrated Computer Aided Engineering, DOI: https://doi.org/10.3233/ICA-220688, 1-14, Published: 15 August 2022

 

Handbooks

  1. Dzemyda, G., V.Šaltenis, V.Tiešis (2007). Optimization Methods. Textbook. Institute of Mathematics and Informatics, Vilnius. 167 p. (in Lithuanian)
  2. Dzemyda, G., O.Kurasova, J.Žilinskas (2008). Multidimensional Data Visualization Methods. Textbook. Institute of Mathematics and Informatics, Vilnius. 204 p. (in Lithuanian)

 

Scientific and other projects

Projects

The most recent projects:

  • He is a principal investigator in the project of the Lithuanian Science Council SMART program “Correcting misperceptions of Covid-19 data: an Innovative E platform CognitiveSTATS for Training Statistical Intuitions in the General Public”, No. 01.2.2-LMT-K-718-05-0042 (2021-2023).
  • He currently leaded the project of the research groups of the Lithuanian Science Council "Geometric method for solving the problem of multidimensional scales" S-MIP-20-19 (2020-2022).
  • He was a principal investigator in the project of the research groups of the Lithuanian Science Council "Cognitive analysis of the impact of pandemic stress on the consumption decisions of the population and the forecast of its impact on the Lithuanian economy" S-COV-20-3 (2020-2021).

 

Recently he leaded and was principal investigator of three large projects:

  • “Theoretical and Engineering Aspects of e-Service Technology Development and Application in High-performance Computing Platforms” (2013-2015) (No. VP1-3.1-ŠMM-08-K-01-010) funded by the European Social Fund. Objectives of the project are to develop new algorithms and web service based solutions.
  • “National Open Access Research Data Archive” Project (2012-2015) (No. VP2-3.1-IVPK-13-V-01-001) funded by the European Union Structural Funds and national budget. The purpose of implementing National Open Access Research Data Archive is to establish the infrastructure that enables collection, organizing and storage of empirical and research data (with corresponding metadata), ensuring free, convenient, interactive search, access and analysis of data.
  • “Production Effectiveness Navigator, PEN” (2012-2014) (No. E!6232), funded by the European Union Eurostars program. Objectives of the project are to develop the risk- and cost-optimal methods and algorithms to conduct mid-term planning of the production programme in manufacturing enterprises.

 

Earlier, he also leaded and was a principal investigator of three international (NATO and European Commission’s Framework) projects in Lithuania: - NATO (No. CN.LG 931702: “NATO Research Programme: A Database of Key Resources in Telecommunications and Computer Science in Central and Eastern Europe – LINKGUIDE”, 1995-1996); - European Commission COPERNICUS Joint Action (No. 14201420 “A Database of Key Resources in Communication and Information Technologies in Central and Eastern Europe – LINKGUIDE”, 1996-1997); - European Commission Feamework Programme (No. IC15-CT98-1003 “Integrated Network of R.T.D. Accomplishments INTACCOMP”, 1998-2000).

 

Principal investigator at national projects:

  • “Creation of Production Planning Algorithm”, UAB Netcode, project No. 20161005-01/APS- 580000-1991, 2016-2017.
  • “Creation of Multiple Criteria Optimization Algorithm or Adaptation of Existing for Ortho Baltic Web-Logistics System DISPATCH”. UAB “Baltic Amadeus”, project No. 5082299-P1/APS-580000-1037, 2011.
  • “Development of the Effective System for Continuous Monitoring of Critical Physiological Indicators“, Lithuanian Business Support Agency, programme “Intelektas LT”, project No. VP2-1.3-ŪM-02-K-01-098, partner UAB “Algoritmų Sistemos”, 2010-2011.
  • “Creation of a Prototype for the Interaction between the Sensor Network and the Local Information Retrieval, Transmission and Imaging Component”, UAB “Diagnostinės sistemos”, project No. 12-17.01/2010, 2010-2011.
  • “Information Means for Clinical Decision Support and Public Health for e.Health System (Info Sveikata)”, Lithuanian State Foundation of Science and Studies, Grant No. B-07019, sub-task of the Institute of Mathematics and Informatics, 2007-2009.
  • “Development of Specialized Data Analysis Methods for Investigation of the Hearth Tissue Temperature Anisotropy”, Lithuanian State Foundation of Science and Studies, Grant No. T-08153, 2008.
  • “Information Technologies for Human Health - Clinical Decision Support (e-Health) (IT SVEIKATA)”, Lithuanian State Foundation of Science and Studies, Grant No. C-03013, sub-task of the Institute of Mathematics and Informatics, 2003-2006.
  • “Optimal Decisions in HIV/AIDS Infection Spread Models, Prognosis and Control”, Lithuanian State Foundation of Science and Studies, Grant No. 143, 1995.

PhD Supervision

Leads PhD Students:

Viktoras Bulavas, Evaldas Narmontas, Dalia Breskuvienė, Modestas Motiejauskas.

Presentations at Scientific Conferences

Keynote speaker at conferences: - 9th International Conference on Computers Communications and Control (ICCCC), 2022, Oradea, Romania; - 8th International Conference on Computers Communications and Control (ICCCC), 2020, Oradea, Romania; - 12th International Conference “Computer Data Analysis and Modeling” (CDAM-2019), 2019, Minsk, Belarus; - 13th International Baltic Conference on Databases and Information Systems (Baltic DB&IS 2018), 2018, Trakai, Lithuania; - Second International Conference on Modern Mathematical Methods and High Performance Computing in Science and Technology (M3HPCST 2018), 2018, Ghaziabad, India; - 30th International Conference “Problems of Decision Making Under Uncertainties 2017” (PDMU-2017), 2017, Vilnius, Lithuania; - 11th International Conference “Computer Data Analysis and Modeling” (CDAM-2016), 2016, Minsk, Belarus; - International Congress on Computer Science: Information Systems and Technologies (CSIST’2016), 2016, Minsk, Belarus; - 6th International Conference on Computers Communications and Control, 2016, Oradea, Romania; - 12th International Baltic Conference on Databases and Information Systems, 2016, - 4th Workshop on Software Quality Analysis, Monitoring, Improvement, and Applications (SQAMIA 2015), 2015, Maribor, Slovenia; Riga, Latvia; - 10th International Conference “Computer Data Analysis and Modeling” (CDAM-2013), 2013, Minsk, Belarus.

Qualification Courses

Short-term visits for research and/or lectures to universities abroad: -

- University of Maribor, Slovenia, 2022; - Bar-Ilan University, Izraelis, 2019; - University of Calabria, Italija, 2019; - Princess Sumaya University for Technology, Jordan, 2018; - University of Almeria, Spain, 2018; - Bar-Ilan University, Israel, 2017; - University of Ferrara, Italy, 2017; - Southwestern University of Finance and Economics, China, 2017; - University of Cyprus, Cyprus, 2016; - University of Calabria, Italy, 2016; - Las Palmas University, Spain, 2015; - Las Palmas University, Spain, 2014; - University of the Basque Country, Spain, 2013; - Freie Universität Berlin, Germany, 2012; - London Metropolitan University, United Kingdom, 2011; - London Metropolitan University, United Kingdom, 2010; - Middle East Technical University, Turkey, 2010; - National Institute of Applied Sciences of Lyon, France, 2010; - University of the Basque Country, Barcelona, Spain, 2008; - Kavala Institute of Technology, Greece, 2009; - University of Groningen, Holland, 2008; - Joensuu University, Finland, 2008; - Technical University of Thessaloniki, Greece, 2003.