Agnė Paulauskaitė-Tarasevičienė

Professor at KTU Faculty of Informatics

Agnė Paulauskaitė-Tarasevičienė – Head of the KTU Artificial Intelligence Centre and Professor at the KTU Faculty of Informatics. She is currently actively involved in highlighting the importance of artificial intelligence (AI) in business, curricula, public education programs, and social media.

In 2019, A. Paulauskaitė-Tarasevičienė chaired a working group on the presentation of recommendations for the strengthening of AI in studies and later on the development of a strategy for the integration of AI into studies. An associate professor at KTU and her team have developed a mass open online course in artificial intelligence (MAIK) in a virtual learning environment. In addition, A. Paulauskaitė-Tarasevičienė led the preparation of the first artificial intelligence study program in Lithuania “Artificial Intelligence” (4 years, 1st cycle), which was successfully launched in September 2020.

A. Paulauskaitė-Tarasevičienė is one of the four Lithuanian scientists participating in the process of providing and evaluating the ethics and recommendations of UNESCO artificial intelligence. For 10 years she has been a research and study mentor, co-author, and reviewer of scientific publications from international publishers. The associate professor has participated in various research projects on the topic of artificial intelligence as a researcher and as a supervisor. Areas of research include machine learning algorithms, image processing technologies, AI ethics, intelligent management systems, and the Internet of Things.

  • Image processing and analysis
  • Intelligent decision making systems
  • Ethics of artificial intelligence

Biological Feedback Measurement and Analysis Technology Center for Strengthening Personal and Public Health (Bio-MAC)

During the project, the researcher developed a deep-learning algorithm with team members to identify human motor problems during specific physical movements. The project aims to develop existing concepts of innovative solutions for measurement and analysis of biological feedback in order to offer commercially ready solutions for self-monitoring of health status and proper and effective application of tools, continuous monitoring of health status / condition and remote quantification.

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

The researcher led the R&D of Cell Nucleus Detection Model Based on Artificial Intelligence (DItect) project. 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.

Center of Excellence for Smart and Adaptive Buildings (SAVAS)

During the project “Center of Excellence for Smart and Adaptive Buildings (SAVAS)”, researchers analyzed the BIM problems with the team members and developed a formal Z-model of the hardware, and performed its verification. The project aimed to concentrate the intellectual potential of a high international level in the center of excellence to carry out research activities in the field of information modeling for the development and use of smart, cost-effective, and adaptive buildings and to commercialize performance.

Research on Smart Home Environment and Development of Intelligent Technologies (BIATech)

During the project “Research of Smart Home Environment and Development of Intelligent Technologies – BIATech”, the researchers developed a lighting control algorithm based on artificial neural networks. The project aimed to develop intelligent technologies and methodologies for smart housing, including multi-agent, machine learning, and other artificial intelligence algorithms.

No. Title Authors Year
1. A machine learning approach for wear monitoring of end mill by self-powering wireless sensor nodes Vytautas Ostaševičius, Paulius Karpavičius, Agnė Paulauskaitė-Tarasevičienė, Vytautas Jurėnas, Arkadiusz Mystkowski, Ramūnas Česnavicius, Laura Kižauskienė 2021
2. Enhancing multi-tissue and multi-scale cell nuclei segmentation with deep metric learning Tomas Iešmantas, Agnė Paulauskaitė-Tarasevičienė, Kristina Šutienė 2020
3. Towards the automation of early-stage human embryo development detection Vidas Raudonis, Agnė Paulauskaitė-Tarasevičienė, Kristina Šutienė, Domas Jonaitis 2019
4. Wooden dowels classification using convolutional neural network Agnė Paulauskaitė-Tarasevičienė, Kristina Šutienė, Laurynas Pipiras 2019
5. Intelligent lighting control providing semi-autonomous assistance
Agnė Paulauskaitė-Tarasevičienė, Aistė Štulienė, Egidijus Kazanavičius 2017