Sep 02, 2026|ELLIS Institute Finland
recruits 7 new principal investigators, and I am one of them! The institute is now grown to 52 professor in various areas of machine learning and artificial intelligence.
News
Sep 02, 2026|Uniarts Helsinki
released this lovely news post: https://www.uniarts.fi/en/articles/news/kivanc-tatar-appointed-professor-of-digital-arts-at-the-uniarts-helsinki-research-institute/.
Aug 28, 2026|New position in Finland
I am starting a new position in Helsinki as a Full Professor in Digital art with emphasis on AI and Co-Creativity at University of the Arts Helsinki (Uniarts Helsinki), which is one of the largest arts universities in Europe. Excitingly, this position also have a second affiliation, as a PI position at ELLIS Institute Finland located at Aalto University.
Aug 26, 2026|New website
New website is up! It has better project tagging, although it is still not populated in blog and press pages, and some issues in publications listing.
Selected current projects
View all projectsRecent publications
View bibliographyARIA: A Diagnostic Framework for Music Training Data Attribution
Han, C., Panahi, A., and Tatar, K. 2026. ARIA: A Diagnostic Framework for Music Training Data Attribution. arXiv. arXiv:2605.16181 [cs.SD]. DOI:10.48550/arXiv.2605.16181.
Expanding the machine: Notating generative synthesis with a state-based representation and a navigable timbre space
Madaghiele, V., Lund, L., Holzer, D., Kelkar, T., Tatar, K., and Holzapfel, A. 2026. Expanding the machine: Notating generative synthesis with a state-based representation and a navigable timbre space. Organised Sound. DOI:10.1017/S1355771825100915.
Grounding Machine Creativity in Game Design Knowledge Representations: Empirical Probing of LLM-Based Executable Synthesis of Goal Playable Patterns under Structural Constraints
Liu, H. X. and Tatar, K. 2026. Grounding Machine Creativity in Game Design Knowledge Representations: Empirical Probing of LLM-Based Executable Synthesis of Goal Playable Patterns under Structural Constraints. arXiv. arXiv:2603.07101 [cs]. DOI:10.48550/arXiv.2603.07101.
Mage: Multi-Axis Evaluation of LLM-Generated Executable Game Scenes Beyond Compile-Pass Rate
Liu, H. X. and Tatar, K. 2026. Mage: Multi-Axis Evaluation of LLM-Generated Executable Game Scenes Beyond Compile-Pass Rate. arXiv. arXiv:2605.07342 [cs.LG]. DOI:10.48550/arXiv.2605.07342.