Lívia Kostová

Research areas: artificial intelligence, machine learning, computer vision, vision-language processing, large language models

Position: Research Engineer

Lívia graduated with a Master’s degree in Computer Science from the Faculty of Informatics and Information Technologies (STU FIIT). Her professional expertise focuses on applied machine learning, particularly within the field of computer vision.

She started at KInIT as an intern after finishing her bachelor’s degree. At that time, her work focused on projects in the dental medical domain, where she explored applications of deep learning in medical imaging.

Since then, her work has expanded into broader computer vision applications, including image and video analysis, image classification, object detection, and deep learning–based visual recognition systems. She has experience working with complex visual data in both structured and unstructured environments, with applications ranging from medical imaging to real-world multimedia analysis. As part of research activities during her studies, she also worked on deep learning approaches for detecting aseptic implant loosening.

Currently, she is involved in the AI-Auditology project, where she works on auditing social media platforms using AI methods, with a particular focus on image processing and analysis of visual content in multimodal environments.

Selected achievements

  • Dean’s Commendation for a bachelor’s thesis in the 2022/2023 academic year, entitled Emotion Recognition Using Raspberry Pi 4 Technology Combined with Machine Learning
  • Dean’s Commendation for a master’s thesis in the 2024/2025 academic year, entitled Development and validation of deep learning approaches for detecting hip implants in high-resolution X-ray images
  • CEng academic accreditation – programme Informatics, in 2023
  • CEng academic accreditation – programme Intelligent software systems, in 2025

Selected activities

  • Research visit/ summer school at National Cheng-kung University Alumni Association of Hsin-chu, Taiwan in 2022 (duration 7 weeks)