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Dr. Ernestas Filatovas

Department: Blockchain and Quantum Technologies Group
Position: Senior Researcher, Project Principal Researcher, Co-founder of the group

Address: Akademijos st. 4, room 633, Vilnius
Tel: (+370 5) 219 3299
E-mail:  

Professional social profiles: linked in   researchgate   google   orcid

ef foto 2018

  

Education

Ph.D. in Technological Sciences (Informatics Engineering, 07T)

  • Vilnius University, Institute of Mathematics and Informatics, 2012
  • Topic of the thesis: Solving multiple criteria optimization problems in an interactive way
  • Scientific supervisor: prof. dr. Olga Kurasova

Positions

Vilnius University

  • Pricipal Researcher in the project (from 2023)
  • Senior Researcher at the Institute of Data Science and Digital Technologies (former Institute of Mathematics and Informatics) (2019-2024)
  • Researcher at the Institute of Mathematics and Informatics (2015−2019)
  • PostDoc at Institute of Mathematics and Informatics (2013−2015)

Vilnius Gediminas Technical University

  • Associate Professor at the Faculty of Fundamental Sciences (2016−2019)
  • Lecturer at the Faculty of Fundamental Sciences (2013−2016)

  

Research interests

  • Blockchain Technologies
  • Quantum Computing
  • Artificial Intelligence
  • Machine Learning
  • MultiObjective Optimization
  • Global Optimization
  • Evolutionary Algorithms
  • Internet-of-Things
  • High-Performance Computing
  • Image Processing

 

Scientific publications

Over 50 scientific publications, with over 25 appearing in journals indexed by the Clarivate Analytics database. The majority of these publications are collaborations with international co-authors.

Complete list of publications with Vilnius University affiliation

Scientific publications in peer-reviewed periodical journals with citation index (Impact Factor) indexed by Clarivate Analytics Web of Science database: 

  1. E. Filatovas, L. Stripinis, F. Orts, R. Paulavičius (2024) Advancing Research Reproducibility in Machine Learning through Blockchain Technology. Informatica,  Vol. 35(2), 1-27, DOI: https://doi.org/10.15388/24-INFOR553.
  2. M. Juodis, E. Filatovas, R. Paulavičius (2024) Overview and empirical analysis of wealth decentralization in blockchain networks. ICT Express, Early Access 1-7, DOI: https://doi.org/10.1016/j.icte.2024.02.002.
  3. M. Marcozzi, E. Filatovas, L. Stripinis, R. Paulavičius (2024) Data-driven consensus protocol classification using machine learning. Mathematics, Vol. 12(2), 221, DOI: https://doi.org/10.3390/math12020221.
  4. R. Paulavičius, L. Stripinis, S. Sutavičiūtė, D. Kočegarov, E. Filatovas (2023) A novel greedy genetic algorithm-based personalized travel recommendation system.  Expert Systems with Applications, Vol. 230, 120580, DOI: https://doi.org/10.1016/j.eswa.2023.120580.
  5. F. Orts, E. Filatovas, G. Ortega, E. M. Garzón (2023) A quantum circuit to generate random numbers within a specific interval. EPJ quantum technology, Vol. 10, 17, DOI:  https://doi.org/10.1140/epjqt/s40507-023-00174-1.
  6. P. Gudžius, O. Kurasova, V. Darulis, E. Filatovas (2023) AutoML-based neural architecture search for object recognition in satellite imagery. Remote Sensing, Vol 15(1), 91, DOI: https://doi.org/10.3390/rs15010091.
  7. F. Orts, E. Filatovas, G. Ortega, J. F. SanJuan-Estrada, E. M. Garzón (2023) Improving the number of T gates and their spread in integer multipliers on quantum computing. Physical Review A, Vol. 107(4), 4042621, DOI: https://doi.org 10.1103/PhysRevA.107.042621.
  8. F. Orts, R. Paulavičius, E. Filatovas (2023) Improving the implementation of quantum blockchain based on hypergraphs. Quantum information processing, Vol. 22, 330, DOI:  https://doi.org/10.1007/s11128-023-04096-w.
  9. E. Filatovas, M. Marcozzi, L. Mostarda, R. Paulavičius (2022) A MCDM-based framework for blockchain consensus protocol selection. Expert systems with applications, Vol. 204, 117609, 1-18, DOI:https://doi.org/10.1016/j.eswa.2022.117609.
  10. F. Orts, G. Ortega, E. Filatovas, E.M. Garzón (2022) Implementation of three efficient 4-digit fault-tolerant quantum carry lookahead adders. Journal of supercomputing, Vol. 78, 13323-13341, DOI:  https://doi.org/10.1007/s11227-022-04401-x.
  11. Gudžius, O. Kurasova, V. Darulis, E. Filatovas (2021) Deep learning-based object recognition in multispectral satellite imagery for real-time applications. Machine Vision and Applications, Vol. 32(98), DOI: https://doi.org/10.1007/s00138-021-01209-2.
  12. Paulavičius, S. Grigaitis, E. Filatovas (2021) A Systematic Review and Empirical Analysis of Blockchain Simulators. IEEE Access, Vol. 9, 38010-38028, 2021, DOI: https://doi.org/10.1109/ACCESS.2021.3063324.
  13. Orts, G. Ortega, A.C. Cucura, E. Filatovas, E.M. Garzón (2021) Optimal fault-tolerant quantum comparators for image binarization. The Journal of Supercomputing, Vol. 77, 8433-8444, DOI: https://doi.org/10.1007/s11227-020-03576-5.
  14. J. Moreno, J. Miroforidis, E. Filatovas, I. Kaliszewski, E. M. Garzón (2020) Parallel radiation dose computations with GENOCOP III on GPUs. The Journal of Supercomputing, Vol. 77, 66–76, DOI: https://doi.org/10.1007/s11227-020-03254-6.
  15. Filatovas, O. Kurasova, J.L. Redondo, J. Fernández (2020) A reference point-based evolutionary algorithm for approximating regions of interest in multiobjective problems. TOP, Vol. 28, 402–423, DOI: https://doi.org/10.1007/s11750-019-00535-z.
  16. Paulavičius, S. Grigaitis, A. Igumenov, E. Filatovas (2019) A Decade of Blockchain: Review of the Current Status, Challenges, and Future Directions. Informatica, Vol. 30(4), 729-748, DOI: https://doi.org/10.15388/Informatica.2019.227.
  17. Orts, E. Filatovas, G. Ortega, O. Kurasova, E.M. Garzón (2019) Improving the energy efficiency of SMACOF for multidimensional scaling on modern architectures. The Journal of Supercomputing, Vol. 75(3), 1038-1050, DOI: https://doi.org/10.1007/s11227-018-2285-x.
  18. J. Orts Gómez, G. Ortega López, E. Filatovas, O. Kurasova, E.M. Garzón (2019) Hyperspectral Image Classification Using Isomap with SMACOF. Informatica, Vol. 30(2), 349–365, DOI:  https://doi.org/10.15388/Informatica.2019.209.
  19. J. Moreno, G. Ortega, E. Filatovas, J.A. Martínez, E.M. Garzón (2018) Improving the performance and energy of Non-Dominated Sorting for evolutionary multiobjective optimization on GPU/CPU platforms. Journal of Global Optimization, Vol. 71(3), 631–649, DOI: https://doi.org/10.1007/s10898-018-0669-3.
  20. E. Filatovas, A. Lančinskas, O. Kurasova, J. Žilinskas (2017) A preference-based multi-objective evolutionary algorithm R-NSGA-II with stochastic local search. Central European Journal of Operations Research, Vol. 25(4), 859-878, DOI: https://doi.org/10.1007/s10100-016-0443-x.
  21. J. J. Moreno, G. Ortega, E. Filatovas, J. A. Martínez, E. M. Garzón (2017) Using low-power platforms for Evolutionary Multi-Objective Optimization algorithms. The Journal of Supercomputing, Vol. 73(1), 302–315, DOI: https://doi.org/10.1007/s11227-016-1862-0.
  22. G. Ortega, E. Filatovas, J. A. Martínez, E. M. Garzón, L. G. Casado (2016) Non-dominated sorting procedure for Pareto dominance ranking on multicore CPU and/or GPU. Journal of Global Optimization, Vol. 69(3), 607–627, DOI: https://doi.org/10.1007/s10898-016-0468-7.
  23. E. Filatovas, D. Podkopaev, O. Kurasova (2015) A Visualization Technique for Accessing Solution Pool in Interactive Methods of Multiobjective Optimization. International Journal of Computers Communications & Control, Vol. 10(4), 508–519, DOI: https://doi.org/10.15837/ijccc.2015.4.1672.
  24. E. Filatovas, O. Kurasova, K. Sindhya (2015) Synchronous R-NSGA-II: an extended preference-based evolutionary algorithm for multi-objective optimization. Informatica, Vol. 26(1), 33-50, DOI: https://doi.org/10.15388/informatica.2015.37.
  25. O. Kurasova, T. Petkus, E. Filatovas (2013) Visualization of Pareto Front Points when Solving Multi-objective Optimization Problems. Information Technology and Control, Vol. 42(4), 353-361, DOI: http://dx.doi.org/10.5755/j01.itc.42.4.3209.
  26. T. Petkus, E.Filatovas, О. Kurasova (2009) Investigation of Human Factors while Solving Multiple Criteria Optimization Problems in Computer Network. Тechnological and Economic Development of Economy, Vol. 15(3), 464–479, DOI: https://doi.org/10.3846/1392-8619.2009.
  27. T. Petkus, E.Filatovas (2008) Decision Making to Solve Multiple Criteria Optimization Problems in Computer Networks. Information Technology and Control, Vol. 37(1), 63–68.

Scientific and other projects

2023-2027 "Development and validation of quantum machine learning methods using trained datasets".
Supported by the Ministry of Education, Science, and Sports of Lithuania.
Member of the group of researchers at the Institute of Data Science and Digital Technologies.

2021-2024 "Resolving research reproducibility problems in Artificial Intelligence using Blockchain Technologies". S-MIP-21-53.
Supported by the Lithuanian State Science and Studies Foundation.
Lead of the group of researchers at the Institute of Data Science and Digital Technologies

2019-2021 "High Performance Computing to Optimize Intensity Modulated Radiotherapy Schedules". UAL18-TIC-A020-B.
Supported by Andalusian Board, Spain.
Member of the group of researchers / project manager from Lithuanian side.

2019-2021 "High-performance solutions for today's scientific computing challenges (HPC4Sci)". RTI2018-095993-B-10.
Supported by the Ministry of Science and Innovation, Spain.
Member of the group of researchers / project manager from Lithuanian side.

2017-2020 “Development and applications of bilevel optimization algorithms". S-MIP-17-67.
Supported by the Lithuanian State Science and Studies Foundation.
Member of the group of researchers.

2018-2020 Developing a Smart Real-Time Planning System. J05-LVPA-K-04-0047.
Supported by Lithuanian Business Support Agency (LVPA).
Member a group of scientists at Vilnius University, developed routing and planning algorithms for “UAB Girteka Logistics” company.

2016-2018 Computational methodologies for societal challenges. TIN2015-66680-C2-1-R.
Supported by the Ministry of Science and Innovation, Spain.
Member of the group of researchers / project manager from the Lithuanian side.

2014–2015 Incubation of new technological companies (Technostart). VP2-1.4-ŪM-05-V-01-003.
Supported by European Structural funds.
Director of the startup “Optimization of Business Performance” / project manager.

2013-2015 Postdoctoral fellowship “Development and application of the interactive algorithms for multiobjective optimization problems” at Vilnius University Institute of Mathematics and Informatics, Lithuania.
 Funded by European Union Structural Funds project “Postdoctoral Fellowship Implementation in Lithuania”.
Post-doctoral researcher.

2012 Improvement of the management of the national health insurance fund”.
Supported by European Union Structural Funds.
Member of the group of researchers.

2008–2009Global optimization of complex systems using high performance computing and grid technologies”.
Supported by the Lithuanian State Science and Studies Foundation through the Programme for Higher Technologies.
Member of the group of researchers.

2007–2008 “The research of human factors in multiple criteria optimization problems applying parallel computing”
Supported by the Lithuanian State Science and Studies Foundation.
Member of the group of researchers.

 

Expert activity

Expert at the Agency for Science, Innovation and Technology (MITA) from 2019.

Reviewer of the scientific journals: 

Member of the Programme Committee and reviewer at the annual conference:

  • „4th International Congress on Blockchain and Applications (Blockchain'22)“
  • „5th International Congress on Blockchain and Applications (Blockchain'23)“
  • „6th International Congress on Blockchain and Applications (Blockchain'24)“

 

Courses currently taught

 Bachelor and Ph.D. studies

  • Blockchain Technologies

Presentations at Scientific Conferences

  • 2023 NUMTA 2023: 4th International Conference and Summer School “Numerical Computations: Theory and Algorithms”, Calabria, Italy. Towards Reproducible Research in AI via Blockchain.
  • 2022 EURO 2022: 32nd European Conference on Operational Research, Espoo, Finland. Evaluation of the energy usage of Ethereum blockchain network.
  • 2022 DAMSS 2022: 13th International Workshop „Data Analysis Methods for Software Systems“, Druskininkai, Lithuania. Impact of transition from Prof-of-Work to Prof-of-Stake consensus protocols on energy consumption.
  • 2022 LOD 2022: the 8th International Online & Onsite Conference on Machine Learning, Optimization, and Data Science, Tuscany, Italy. Convolutional Neural Networks and Neural Architecture Search for Satellite Imagery Analysis.
  • 2021 EURO 2021: 31st European Conference on Operational Research, Athens, Greece. Application of MCDM approaches to select consensus algorithms for blockchain systems.
  • 2021 DAMSS 2021: 12th International Workshop „Data Analysis Methods for Software Systems“, Druskininkai, Lithuania. Application of MCDM Techniques for Consensus Protocol Selection.
  • 2019 NUMTA 2019: Numerical Computations: Theory And Algorithms, Calabria, Italy. High-performance algorithms for large-scale multiobjective radiotherapy planning problems.
  • 2019 DAMSS 2019: 11th International Workshop „Data Analysis Methods for Software Systems“, Druskininkai, Lithuania. HPC Tool for ISOMAP.
  • 2018 EURO2018: 29th European Conference on Operational Research, Valencia, Spain. A preference-based multiobjective evolutionary algorithm with controllable approximation accuracy.
  • 2017 DAMSS 2017: 9th International Workshop „Data Analysis Methods for Software Systems“, Druskininkai, Lithuania. Satellite Imagery Application to Financial Markets via Machine Learning.
  • 2016 DAMSS 2016: 8th International Workshop „Data Analysis Methods for Software Systems“, Druskininkai, Lithuania. Evaluation of an efficient NSGA-II version on heterogeneous low-power platforms.
  • 2016 GOW’16: XIII Global Optimization Workshop, Braga, Portugal. Energy-aware computation of Evolutionary Multi-Objective Optimization.
  • 2016 EURO2016: 28th European Conference on Operational Research, Warsaw, Poland. NSGA-NBI: A Preference-Based Multi-Objective Evolutionary Algorithm.
  • 2015 DataMSS2015: 7th International Workshop „Data Analysis Methods for Software Systems“, Druskininkai, Lithuania. Solving Multi-Objective Competitive Facility Location Problems Using Preference-Based Evolutionary Approach.
  • 2015 MCDM2015: 23rd International Conference on Multiple Criteria Decision Making, Hamburg, Germany. A Preference-Based Evolutionary Approach for Solving a Three-Objective Competitive Facility Location and Design Problem.
  • 2015 EURO2015: 27th European Conference on Operational Research, Glasgow, UK. Parallelization of the Non-dominated Sorting Procedure.
  • 2014 VOCAL 2014: The 6th Veszprém Optimization Conference: Advanced Algorithms, Veszprém, Hungary. The Preference-Based Multi-Objective Evolutionary Algorithm with Local Search.
  • 2014 DataMSS2014: 6th International Workshop „Data Analysis Methods for Software Systems“, Druskininkai, Lithuania. Extensions of the Preference-Based Multi-Objective Evolutionary Algorithm R-NSGA-II.
  • 2014 IFORS2014: 20th Conference of the International Federation of Operational Research Societies, Barcelona, Spain. A New Visualization Technique for Enhancing Interactive Methods of Multiobjective Optimization.
  • 2014 XXX EURO mini Conference: Optimization in the Natural Sciences, Aveiro, Portugal. A new Hybrid Method for Interactive Multiobjective Optimization Based on NSGA-II and Synchronous NIMBUS Method.
  • 2013 DataMSS2013: 5th International Seminar „Data Analysis Methods for Software Systems“, Druskininkai, Lithuania. Integration of Genetic Multiobjective Optimization Algorithm into Interactive Nimbus Method.
  • 2013 EURO2013: 26th European Conference on Operational Research, Rome, Italy. A New Strategy Based on Genetic Algorithms for Solving Multiobjective Problems Interactively.
  • 2011 Computer Days – 2011, Klaipėda, Lietuva. A Decision Support System for Multiobjective Optimization Problems.
  • 2010 MMA2010: The 15th International Conference „Mathematical Modelling and Analysis“. Druskininkai, Lithuania. Parallel Computing and Matlab to Solve Multiple Criteria Optimization Problems.
  • 2009 KORSD2009: Knowledge-Based Technologies and OR methodologies for Strategic Decisions of Sustainable Development, Vilnius, Lithuania. Decision Support System for Optimal Selection of Feed Ingredients.
  • 2008 EUROPT2008: Continuous Optimization and Knowledge-Based Technologies, Neringa, Lithuania. Investigation of Human Factors Solving Multiple Criteria Optimization Problems in Computer Networks.
  • 2008 INYS2008: International Networking for Young Scientists on High-Performance Scientific Computing, Druskininkai, Lithuania. Investigation of Human Factors Solving Multiple Criteria Optimization Problems in Computer Networks.
  • 2007 LOTD2007: Operations Research and Applications, Vilnius, Lithuania. Study of Decision Making when Solving Multiobjective Optimization Problems in Parallel.
  • 2007 MCDA65: The 65th Meeting of the European Working Group on Multiple Criteria Decision Aiding, Poznan, Poland. Parallel solution strategies to solve multiple criteria optimization problems.

 

Qualification visits

 

Membership in scientific organizations

 

  • International Society on Multiple Criteria Decision Making (MCDM)
  • Lithuanian Computer Society (LIKS)
  • Associate member of the Lithuanian Quantum Technology Association
  • Member of the Artificial Intelligence and Digital Transformation Working Group of the Arqus University Alliance, which brings together the Universities of Bergen, Graz, Granada, Leipzig, Leipzig, Lyon, Padua, Wroclaw and Vilnius

 

Awards

2014 Laureate of the Fourth Conference of Young Scientists “Physical and Technological Sciences Interdisciplinary Studies”

2014 InfoBalt grant Winner of the 2nd place with the work “The structural similarity index method in the evaluation of high-resolution images”

2009 and 2010 Lithuanian State Science and Studies Foundation Support for researchers. Awarded to the most active and productive Ph.D. students in Lithuania

2006 Graduating With Honors. Awarded to the top student of a master's degree