Martin Tamajka

Research areas: natural language processing, large language models, machine learning implementation, IT architecture, computational infrastructure, clouds computing

Position: Technology Lead

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Martin is a Technology Lead focusing on natural language processing, machine learning, and artificial intelligence. Besides his activities in research and technology-transfer projects, he leads research engineers across the institute’s research teams, supports the systematic introduction of new technologies, and leads the development of KInIT’s technical infrastructure.

Martin, as a member of the NLP team, actively collaborates with companies to support technology transfer, strengthen industry–research cooperation, and help boost the broader innovation ecosystem.

Martin is the principal investigator of the Horizon Europe project ALFIE and has contributed to multiple European and national projects, including AI4Europe, LorAI, and Hopero. His work combines research, engineering leadership, and practical AI deployment, with a focus on making modern AI technologies useful, reliable, and impactful in real-world settings.

Besides research and technology leadership, Martin is a passionate teacher. He has supervised and co-supervised 20 Master’s and Bachelor’s theses and led multiple successful student teams in project competitions. His teaching activities are not limited to academia. As an invited speaker, he has presented at industrial conferences and tech meetups, including Data Science Club, Tech Summit, CodeCon and others. He regularly leads hands-on tech workshops, such as OpenSlava, CESCG, and BaseCamp, focused on natural language processing, machine learning, deep learning, and other cutting-edge technologies, and he led a series of eight machine learning courses for an international company.

Selected activities

Selected Student Supervising

Master

  • Zaťko Timotej – Application of interpretability and explainability of neural networks in the evaluation of medical images. Defended 2021.
  • Kolibášová Martina – Estimating the reliability of neural network decisions in evaluation of medical images. Defended 2021.
  • Sebechlebský Šimon – Application of interpretability and explainability in detection of false predictions in the evaluation of medical images. Ongoing.
  • Mikuš Matej – Prediction of Alzheimer’s disease progression based on deep learning. Defended 2020.
  • Pavlík Peter – Interpretable diagnosis of Alzheimer’s disease based on deep learning. Defended 2020.
  • Grivalský Štefan – Segmentation of brain tumours in volumetric medical data using deep learning. Defended 2019.
  • Mňačko Tomáš – Nerve segmentation in ultrasound images using deep learning. Defended 2019.

Bachelor

  • Háberová Ivana – Explainability and interpretability of neural network in classification of medical images. Defended 2021.
  • Sandanus Michal – Classification of volumetric medical images and other data using methods of machine learning. Defended 2021.
  • Veselý Marcel – Explainability and interpretability of neural network in classification of medical images. Defended 2021. — Research Intern at KInIT
  • Králik Timotej – Video analysis of sport matches using methods of computer vision and artificial intelligence. Defended 2021.
  • Mikuš Matej – Segmentation of anomalies in volumetric medical data. Defended 2018.