Martin Krutský

Research Consultant

Martin Krutský is a ML/AI researcher at the Czech Technical University where he is currently working towards his PhD. On the technical side of his research, he is interested in explainability techniques for deep neural networks, especially graph neural nets and other structured models. His previous work is in the field of geometric deep learning and symmetries in machine learning. On the hand, he is also interested in the intersection of AI research with other fields, especially concerning a responsible and ethical use of AI and AI safety.

Graduate

Czech Technical University: M.S. in Artificial Intelligence

Undergraduate

Czech Technical University: Computer Science and Artificial Intelligence

Representative Publications
  • Martin, K. (2021). Basics of symmetries in deep learning (Bachelor’s thesis, Czech University of Technology in Prague. Computing and Information Center.).
  • Martin, K. (2024). Interpretable deep learning with symmetries for planning (Master’s thesis, Czech University of Technology in Prague. Computing and Information Center.).

Deep Learning

85%

Explainable AI

75%

Geometric Deep Learning

75%

ML Engineering

80%
Your ideal research study

Robust explanation of global concepts in large transformer models

What drew you to consciousness

The question of how similar do artificial neural networks behave compared to the biological ones, despite the difference in substance and processes.

Hobbies

chess, badminton, functional training, specialty coffee

Contact Martin Directly.