Research Group
Tomas is a versatile scientist and data expert with a rich background spanning particle physics, distributed data management, and data science. He began his academic journey studying Particle Physics at Charles University before earning his Ph.D. in experimental physics from the University of Freiburg. There, he conducted groundbreaking research searching for new particles beyond the Standard Model using channels with boosted W and Z bosons.
After completing his Ph.D., Tomas transitioned from fundamental physics to distributed data management for the ATLAS experiment at CERN, where he spent five years working in both operations and the development of tools for data management. His career path then led him to the field of Software Defined Networks (SDNs), where he automated networks for telecommunication companies.
Lately, Tomas focused on data science, employing machine learning techniques to analyze data for major companies like Walmart and Amazon. He developed a keen interest in AI, particularly in Natural Language Processing (NLP), and believes that the future of AI lies in multi-modality—integrating multiple types of data and models. He is especially interested in the potential of combining imagination and computer vision with current language models and sees a significant role for physics-informed machine learning in the coming decade.