Ivan Vykopal

Ivan is a research assistant and member of the NLP team, focusing on machine learning, deep learning, the analysis of large language models, and computer vision.  He holds a Master’s degree in Computer Science from the Faculty of Informatics and Information Technologies at the Slovak University of Technology. During his studies, he focused on artificial intelligence, deep learning, participating in several research projects related to the application of deep learning and computer vision in the field of medicine. Ivan’s master’s thesis and research delved into deep neural networks in the domain of medical image processing, with a specific focus on the segmentation of higher morphological structures for identifying possible cardiac transplant rejection.

PhD topic: Natural language processing

Supervising team: Marián Šimko (KInIT)

Multilingual low-resource language processing presents a unique set of challenges and opportunities in the field of natural language processing. As our increasingly connected world brings together people from diverse linguistic backgrounds, the need to support multiple languages, especially those with limited resources, has become paramount. In this context, the use of advanced machine learning techniques, such as cross-lingual transfer learning and multilingual embeddings, has gained prominence. These approaches enable models to leverage knowledge from resource-rich languages to improve the performance of low-resource language processing tasks.Additionally, when working with low-resource languages, we often encounter linguistic diversity and complexity that demand special attention. Each language has its unique features, such as different alphabets, grammar structures, and word forms. Adapting models to these variations is essential for accurate understanding and processing. Moreover, as we strive to support low-resource languages, we must address the issue of limited data resources. To tackle these challenges, researchers are continually exploring innovative algorithmic approaches, data augmentation methods, and collaborative initiatives to expand linguistic resources for underrepresented languages. In this multilingual landscape, the pursuit of effective low-resource language processing solutions remains a vital imperative for research and development.