Staff

Research Work

Scientific interests include

• Data Mining,
• Multidimensional Data Visualization,
• Big Data analysis,
• Artificial Neural Networks,
• Machine Learning,
• Optimization Methods,
• Multi-objective Optimization,
• Evolutionary Computing,
• Image Processing.

Scientific publications

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

Scientific and other projects

COST actions:

• BIG-SKY-EARTH: Big Data Era in Sky and Earth Observation (COST TD1403), 2014-2018.
• cHiPSet: High-Performance Modelling and Simulation for Big Data Applications (COST IC1406), 2015-2019.
• SoftStat: Combining Soft Computing Techniques and Statistical Methods to Improve Data Analysis Solutions (COST IC0702), 2009-2012.

National and international projects:

• EuroStars project: PEN – Production Effectiveness Navigator, EuroStars project E!6232, 2011-2014.
• Theoretical and Engineering Aspects of E-Service Technology Development and Application in High-Performance Computing Platforms, 2012-2015.
• Development of Data Analysis Methods and Tools (2013-02-12 Agreement between "Informatikos mokslų centras" and Vilnius University).
• Innovation voucher No. 31V-109 (JSC "Algoritmų sistemos", MITA): Analysis of optimization strategies, implementation possibilities and methods of Artificial Neural Networks for complex decisions, 2012.
• Comparative quantitative and qualitative research "Estimation of demand of engineering studies specialists in Lithuania, Estonia and Poland". Agreement with Vilniaus kolegija (University of Applied Sciences).
• Development of the prototype for interaction between the network of sensors and local component of information collecting, transferring and representation. Agreement with JSC "Diagnostinės sistemos." No. 12-17.01/2010.
• Representing of critical physiological parameters data, and development of a decision making algorithm and decision making subsystem prototype [sub-project of "Creation of an efficient system for constant observation of critical physiological parameters", 2010-12-08 – 2010-12-31.
• Human genome diversity determined pathogenetic mechanisms of atherosclerosis (ATHEROGEN), 2004-2006.
• Information technologies for human health - support of clinical decisions (e-health). Acronym "IT Health", 2003-2006.
• Development program of the technology project "Information technology tools of clinical decision support and citizens' wellness for the e- Health system (Info Health)", 2007-2009.

Presentations at Scientific Conferences

• Deep Learning Models for Pancreatic Cancer Detection in CT Images. EURO 2022. 32nd European Conference on Operational Research, Aalto University, Finland, July 3-6, 2022.
• Massive Data Visualization via Selecting a Data Subset. 8th international workshop on data analysis methods for software systems, Druskininkai, Lithuania, December 1-3, 2016.
• Classification-Based Storage of JPEG Images. 8th international workshop on data analysis methods for software systems, Druskininkai, Lithuania, December 1-3, 2016.
• Multi-Level Method for Big Data Visualization. 8th international workshop on data analysis methods for software systems, Druskininkai, Lithuania, December 1-3, 2016.
• Artificial Neural Networks for Massive Data Visualization. EURO 2016: 28th European Conference on Operational Research, Poznan University of Technology, Poznań, Poland, July 3-6, 2016.
• Decision Tree Classification to Predict Effect of JPEG Compression on Images. EURO 2016. 28th European Conference on Operational Research, Poznan University of Technology, Poznań, Poland, July 3-6, 2016.
• Visual Analytics for Big Data. 7th international workshop on data analysis methods for software systems, Druskininkai, Lithuania, December 3-5, 2015.
• Image Classification for Quality Prediction after JPEG Compression. 7th international workshop on data analysis methods for software systems, Druskininkai, Lithuania, December 3-5, 2015.
• Extensions of the Preference-based Multi-objective Evolutionary Algorithm R-NSGA-II. 5th international workshop on data analysis methods for software systems, Druskininkai, Lithuania, December 4-6, 2014.
• A review of Data Mining Systems Based on Web Services. 5th international workshop on data analysis methods for software systems, Druskininkai, Lithuania, December 5-7, 2013.
• Web Service-based Data Mining Systems. 26th European Conference on Operational Research, 1-4 July, 2013, Rome, Italy.
• Integrated Visualization of Vector Quantization by Multidimensional Scaling. Workshop on Computer Graphics, Vision and Mathematics (GraVisMa 2009), September 2-4, 2009, Plzen, Czech Republic.
• Visualization of Support Vectors. XIII International Conference on Applied Stochastic Models and Data Analysis (ASMDA-2009), June 30- July 3, 2009, Vilnius, Lithuania.
• Combination of Vector Quantization and Visualization. 6th International Conference on Machine Learning and Data Mining, MLDM 2009, July 23-25, Leipzig, Germany.
• Rule Induction for Ophthalmological Data Classification, EURO Mini Conference on Continuous Optimization and Knowledge-Based Technologies, May 20-23, 2008, Neringa, Lithuania.
• Parameter System for Human Physiological Data Representation and Analysis, 3rd Iberian Conference on Pattern Recognition and Image Analysis, June 6-8, 2007, Girona, Spain.

Ph.D. Supervision and Lecturing

Aušra Šubonienė

• Subject:
• Period of study: 1st October 2023–30th September 2027
• Science field: Informatics (N 009)
• The title of Thesis: Pancreatic cancer segmentation using semi-supervised machine learning methods
• Defended: –

Algimantas Skuodis

• Subject:
• Period of study: 1st October 2022–30th September 2026
• Science field: Informatics (N 009)
• The title of Thesis: Real-Time Multi-Person Body Pose Tracking Using Machine Learning Methods
• Defended: –

Rokas Gipiškis

• Subject:
• Period of study: 1st October 2020–30th September 2024
• Science field: Informatics (N 009)
• The title of Thesis: Convolutional Neural Networks for the Segmentation of Human Brain Images
• Defended: –

Žydrūnas Vaišnoras

• Subject:
• Period of study: 1st October 2019-12th November 2026
• Science field: Informatics (N 009)
• The title of Thesis: Developing Machine Learning Methods to Detect Intrusions in Computer Networks
• Defended: –

Supervisor of five Ph.D. students, who have defended successfully their Ph.D. theses:

• Alma Molytė "Investigation of Combinations of Vector Quantization Methods with Multidimensional Scaling". 2011-06-29.
• Ernestas Filatovas "Solving Multiple Criteria Optimization Problems in an Interactive Way". 2012-03-28.
• Gintaras Vaira "Genetic Algorithm for Vehicle Routing Problem". 2014-06-10.
• Pavel Stefanovič "Visualization of Self-Organizing Maps and Estimation of Their Quality". 2015-06-30.
• Olegas Niakšu "Development and Application of Data Mining Methods in Medical Diagnostics and Healthcare Management". 2015-09-21.

Teaching subjects (Bachelor and Master studies):

• Artificial Intelligence
• Computational Intelligence and Decision Making
• Artificial Neural Networks

Membership of editorial boards

• Nonlinear Analysis: Modelling and Control, http://www.mii.lt/NA/
• Baltic Journal of Modern Computing, http://www.lu.lv/baltic-journal-of-modern-computing/editorial-board/
• Computational Science and Techniques, http://journals.ku.lt/index.php/CST/about/editorialTeam
• Informatics MDPI, http://www.mdpi.com/journal/informatics/editors

Membership of scientific societies

• IEEE https://www.ieee.org/
• Association for Computing Machinery (ACM) https://www.acm.org/

Academic and professional awards

• A winner of the Lithuanian Science Prize 2021 – Gintautas Dzemyda and Olga Kurasova “From data science to artificial intelligence technologies (2006-2020)”