Tomasz Kajdanowicz

Professor Tomasz Kajdanowicz graduated with an MSc and Ph.D., both with honors, from Wroclaw University of Technology, Poland, in 2008 and 2012, respectively. His doctoral dissertation was awarded The Best Polish Ph.D. Dissertation Award (2012/2013) by the European Association for Artificial Intelligence – Polish Chapter in 2014. He currently serves as an Associate Professor at Wroclaw University of Science and Technology, Poland, in the Faculty of Information and Communication Technology, where he leads the Department of Artificial Intelligence.

Professor Kajdanowicz has been the principal investigator of numerous research projects, including a prestigious European research grant under the CHIST-ERA program, National Science Center grants SONATA, and a grant funded by the Foundation for Polish Science - First Team Grant. He has also initiated and led 15 research projects in collaboration with commercial entities, including major international corporations.

He has served as a co-chair and organizing committee member for numerous workshops and summer schools focusing on Data Science, Artificial Intelligence, Machine Learning, and Big Data (including NeurIPS and ACL). Additionally, he co-organized panel sessions as part of AI House 2025 at the World Economic Forum in Davos.

To date, Professor Kajdanowicz has authored over 160 research articles spanning a broad spectrum of topics, including Ensemble Classification, Collective Classification, Machine Learning, Social Network Analysis, Collaborative Systems, Data Mining, Recommender Systems, Representation Learning, Large Language Models, and Big Data. He holds the ITIL v3 Foundation Certificate and several professional certifications in project management and data science.

His research experience includes conducting studies at Stanford University (USA), the University of Notre Dame (USA), the Technical University of Dortmund (Germany) and holding visiting professorships at the University of Sydney (Australia). His research interests focus on large language models, media analysis, machine learning, and representation learning.