| Topic title |
Possible scientific supervisors |
Source of funding |
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Applications of Agent-based Models (ABM) to analyze finance growth in a sustainable manner over a long-term period
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prof. dr. Audrius Kabašinskas |
state-funded |
Research Topic Summary.
This topic is financed by project MSCA Industrial Doctoral Network on Digital Finance.
Agent-based systems are computer models that simulate the behaviours and interactions of autonomous agents, either as individuals or in groups, in order to gain a deeper understanding of how a system behaves and what factors influence its outcomes. In agent-based modelling, a system is represented as a collection of autonomous decision-making units, or agents (ABM). Each agent evaluates its own situation and makes decisions according to a set of rules. Agents are capable of a variety of appropriate behaviours for the system they represent. ABM has been utilised in numerous financial investigations. The literature contains few ABM studies that model economies and markets while assuming the industry's adoption of sustainable finance
This study aims to use agent-based models to simulate different market scenarios in which industry agents take sustainable actions. Long-term financial growth will be analysed, and the findings will aid in the development and modification of industry policies and strategies. A public repository containing a library of the developed agent-based models is another anticipated outcome. The Work Package will place a strong emphasis on disseminating and the anticipated outcomes. Several channels, including peer-reviewed articles in high-impact journals, research talks at national and international conferences, and use case presentations at industry workshops, will be utilised to accomplish this objective.
Planned Secondments: a) Deloitte Consulting S.r.l.S.B (DEL), M12, 18 months, analyse finance growth in an applied research setting; b) Athena Research and Innovation Centre (ARC) Greece, M33, 4 months, applied industry-research, exposure to world-leading research centre and infrastructure.
Detailed description can be found at: https://www.digital-finance-msca.com/applications-of-agent-based-models
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Data science and information integration research for disease and prevalence prediction
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prof. dr. Robertas Alzbutas |
state-funded |
Research Topic Summary.
The objective of the research is the creation of methodology and test calculations based on data science development and information integration for the most accurate diseases and their prevalence prediction.
Tasks:
1. Review and compare possibilities of Bayes methods and other information integration methods and models as well as the application of related software and data science methods intended for disease prediction.
2. Define information integration methods and relevant models accuracy metrics, criteria and their evaluation procedures in the context of data relevant to disease prediction.
3. Develop and demonstrate the information integration tools whose usage increases the accuracy and/or reduces the risk of incorrect decision-making.
4. Perform the pilot studies of methods and models intended for disease prediction and their prevalence and create a methodology for the effective application of these means.
For further information please contact the supervisor of the topic.
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Hierarchical models for uncertainty estimation within deep feature spaces and application to medical diagnostics
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doc. dr. Tomas Iešmantas |
state-funded |
Research Topic Summary.
Applications of deep learning within the field of medical diagnostics are numerous - from point of view of the variety of pathologies as well as number of different imaging modalities. The main focus being construction of deeper neural network or forming new architectures. However, the main drawback of such approach is that this group of methods has no mechanism to model uncertainties of predictions. This hinders the applicability of the methods as well as it is a key requirement for the medical diagnostic systems in order for them to be applicable in the standard practice of hospitals.
This research will seek to integrate mathematical uncertainty estimation methods within the framework of deep neural networks and their application within the field of medical diagnostics. It is foreseen to consider methods from these groups: hierarchical Bayesian inference, Gaussian and deep Gaussian processes, stochastic normalizing flows for generative model construction within the space spanned by deep features.
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Development and analysis of methods for estimation dependencies in long series
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prof. dr. Tomas Ruzgas |
state-funded |
Research Topic Summary.
During the past decades, due to the fundamental discoveries in the field of molecular biology, it has become a central subject of biology sciences. That, in turn, opened the door to the technologies of the so-called post-genomic era. They are often based on the computer analysis of the entire genome. Even the short sequences (genitive case) are used in bioinformatics. Genome of the simple life form (virus) can be very big and can exceed 3.5×E5 nucleotides. The number of bacteria genomes varies from 0.5×E6 to 10×E6 nucleotides. Human genomes have about 3.12×E7 nucleotides. It is a big issue to visualize the data of such size or its statistics. The short sequences (e.g. genes or proteins) are often compared interdependently, but the results of comparison are hardly interpreted if there are plenty of them. Musical compositions are arranged from the special information units, i.e. sequence of musical notes. Their length in comparison to the length of nucleotides’ sequence is considerably shorter. The biggest classical music composition Ludwig van Beethoven Symphony No. 9 in D minor takes almost 70 minutes and has about 3,8×E4 notes. There are also some even bigger compositions, e.g. Frederic Rzewski The Road is supposed to be one of the longest piano solo’s lasting for about 10 hours, or Jacob Mashak Beatus Vir for two pianos takes 11 hours. But even the note sequences with the rank of E3 are not easy to analyse and to compare in large variety of note sequences. Furthermore, the variety of different notes, compared to four nucleotides requires bigger area of more dimensions. Different length of notes makes this task even more complicated. The research goal is to create knowledge about autocorrelation methods of long series that are effective in the case of non-stationarity. In order to achieve this goal, it is necessary to study mathematical estimators based on the non-parametric analysis.
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Cryptographic one-way functions construction using matrix power functions
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prof. dr. Eligijus Sakalauskas |
state-funded |
Research Topic Summary.
Actuality is based on the fact that quantum computers are in a very intensive development stage. It is a great need to create such cryptographic methods that could be realized by the classical computers but resistant to the quantum cryptanalysis methods.
In 2016 National Institute of Standards and Technology – NIST announced the tender for cryptographic schemes construction being resistant to quantum cryptanalysis. In 2022 three e.signature algorithm and one public key encryption algorithm were declared as finalists. These algorithms belong to the so-called post-quantum cryptography. Although the first results have already been obtained but the research in this field is carried out very intensively in the world. In "The Economist" magazine publication “The World Ached 2023” said that “post-quantum cryptography is hot“.
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Creation and investigation of the payment system based on the blockchain technology with the elements of non-commuting cryptography
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doc. dr. Aleksejus Michalkovič |
state-funded |
Research Topic Summary.
Blockchain technologies are widely used in the modern world. Surely, almost every adult has heard of Bitcoin cryptocurrency. This technology is also useful for banks, where it can be implemented in the development of payment systems.
Cryptography has a major role in this development. Using cryptographic algorithms blocks are linked into a chain. These algorithms must be cryptographically secure to withstand quantum cryptanalysis. However, algorithms used nowadays do not provide sufficient protection from such attacks. Moreover, quantum algorithms to be applied for the analysis of this technology are known.
Hence it is time to implement the elements of non-commutative cryptography in blockchain technology. Relying on the previously published results of the research of our group we plan to propose a payment system based on quantum cryptanalysis-resistant blockchain technology.
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| Development and research on new models of genetic sequences and their mutations |
doc. dr. Mantas Landauskas |
state-funded |
| Application of neural networks for predictive modeling of bacterial behavior on TiO2 thin films |
doc. dr. Paulius Palevičius |
state-funded |
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Simulation and automated design of optimal and reliable systems integrating artificial and ambient intelligence
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prof. dr. Robertas Alzbutas |
state-funded |
Research Topic Summary.
Aim of research - to create the methodology for the smart design of optimal and reliable systems based on ambient and artificial intelligence and demonstrate the application of developed simulation means.
Research tasks:
1. Develop the concepts and techniques for the simulation and automated design of systems;
2. Prepare and update the simulation means and models suitable for automated smart design;
3. Create a concept for system optimization in terms of resource consumption and reliability;
4. Connect the concepts of automated design and optimization and demonstrate this as a unified methodology that enables the automated design of reliable systems.
Activities planned in this research will solve the relevant issues and tasks mentioned above, i.e., ensuring reliable operation of the various systems and optimizing these systems' resource consumption. The developed methodology will be demonstrated based on simulation means (methods and ICT) and various applications and it enable their further development in creating reliable systems. This will also be related to the smart environments and automated design technology to optimize resources used for this technology.
For further information please contact the supervisor of the topic.
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Coupled Map Lattice of Matrices
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doc. dr. Rasa Šmidtaitė |
state-funded |
Research Topic Summary.
Relevance. Coupled Map Lattices (CML) are widely used and applied in the research of dynamical systems. Coupled Map Lattice of Matrices (CMLM) represents a new class of models and a promising research area introduced by the author of the proposed dissertation topic along with co-authors in 2018. Matrix iterative maps and their networks have been introduced and ongoing research is being presented in high-level scientific journals - indicating the importance of the research and its application possibilities. There is an increasing number of publications citing applied CMLM models and attracting growing attention from scientists willing to collaborate by combining their own research topics with CMLM studies.
The scientific research problem is to examine CMLM tasks aiming to explore and apply nth order matrix CMLs that have never been explored before.
The aim of the scientific research is to explore and apply networks of nxn matrix iterative maps.
The abundance of scalar coupled map lattices and related studies examined in the literature only confirms the countless possible directions in which new extensions of matrix iterative models can be introduced. However, more specific research objectives can also be identified: trantient processes (e.g., traveling or spiral waves, solitons, chimeras) in coupled map lattices of matrices; theorethical aspects and application of divergence processes in coupled map lattices of matrices, as well as the finite-time stabilization of unstable solutions in CML of matrices.
The proposed research aims to intensify breakthroughs in the respective areas guaranteeing CMLM studies.
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