Kristina Šutienė

Associate Professor at KTU Faculty of Mathematics and Natural Sciences

Kristina Šutienė is an associate professor at KTU Faculty of Mathematics and Natural Sciences, member of the scientific group “Mathematical modeling of stochastic, economic and medical systems”. K. Šutienė’s research is related to financial risk assessment and modeling and decision-making using financial mathematics, artificial intelligence and data science methods. She is also interested in statistical modeling in solving medical problems.

K. Šutienė teaches in applied mathematics, business big data analytics, medical physics and artificial intelligence study programs. Together with the team, she developed a massive open online course on artificial intelligence in a virtual learning environment. The scientist is a member of the Lithuanian Society of Mathematicians and the international association “Institute of Data Science and Artificial Intelligence”.

  • Data analysis and anomaly detection
  • Compilation of prognostic and statistical models
  • Modeling of stochastic economic, financial and medical systems
  • Risk management in business systems

Pension fund regulation during times of uncertainty in the context of sustainable investments (PenReg)

Researchers develop a regulatory model for pension funds to make sustainable investment decisions in an uncertain environment, taking into account the fund’s risk profile. One of the most important components of this model is the formation of future scenarios, under which optimal investment decisions would be made with respect to several criteria (return maximization, risk minimization), their rebalancing is expected and stress tests are proposed.

Fintech and Artificial Intelligence in Finance – Towards a transparent financial industry

K. Šutienė is the representative of the COST project – Fintech and Artificial Intelligence in Finance – Towards a transparent financial industry – committee for Lithuania (2020–2024). Researchers collaborate in a large international network with the common goal of developing the Fintech sector. In the project, artificial intelligence and data science are applied in various fields of finance to understand them, identify risks and make decisions. Particular attention is paid to the interpretation and explainability of decisions made by artificial intelligence. More information:

FINancial Supervision and TECHnological Compliance training programme – FIN-TECH 

Big data and artificial intelligence are applied to create models to identify credit, investment and fraud risks. In this way, the aim is to contribute to the improvement of the competitiveness of the Fintech sector and the development of the common ecosystem in EU countries. The applicability of these models is demonstrated to market regulatory and supervisory authorities, Fintech associations and centers during periodic trainings.

R&D of Cell Nucleus Detection Model Based on Artificial Intelligence (DItect)

The project team aimed to develop the model based on the artificial intelligence used to detect cell nucleus in fluorescence images. Scientists focused on images containing noise, touching and partially overlapping nuclei, and possible observed under different lighting conditions.

Development of Excellence Centre for the Analysis, Modelling and Risk management

This is an ESFA project, the coordinator of which is the Lithuanian State Tax Inspectorate, 2016–2019. Various tasks are being solved that would improve tax collection, reduce the shadow and contribute to the development of a smart tax administration system. In many cases, artificial intelligence, optimization and network analysis methods are applied, the training of which uses big data describing the activities, behavior and taxes paid by Lithuanian legal and natural persons.

No. Title Authors Year
1. Key Roles of Crypto-Exchanges in Generating  Arbitrage Opportunities. Audrius Kabašinskas, Kristina Šutienė 2021
2. Fast Multi-Focus Fusion Based on Deep Learning for Early-Stage Embryo Image Enhancement. Vidas Raudonis, Agnė Paulauskaitė-Tarasevičienė, Kristina Šutienė 2021
3. A multistage risk-averse stochastic programming  model for personal savings accrual: the evidence from Lithuania. Audrius Kabašinskas, Francesca Maggioni, Kristina Šutienė, Eimutis Valakevičius 2019
4. Deep Learning-based Detection of Overlapping Agnė Paulauskaitė-Tarasevičienė, Kristina Šutienė, Justas Valotka, Vidas Raudonis, Tomas Iešmantas 2019
5. Enhancing university competitiveness through ICT infrastructure: the case of Kaunas University of Technology Regina Misevičienė, Kristina Šutienė, Danutė Ambrazienė, Dalius Makackas 2018