Publications
Full list. Summaries and context for each paper live on the Research page.
Journal articles
Utz, V., & DiPaola, S. (2020). Using an AI creativity system to explore how aesthetic experiences are processed along the brain's perceptual neural pathways. Cognitive Systems Research, 59, 63–72. Paper
Stein, T., Utz, V., & van Opstal, F. (2020). Unconscious semantic priming from pictures under backward masking and continuous flash suppression. Consciousness and Cognition, 78, 102864. Paper
Conference papers
Utz, V., & DiPaola, S. (2023). Climate implications of diffusion-based generative visual AI systems and their mass adoption. Proceedings of the 14th International Conference on Computational Creativity, 264–272. Paper
Utz, V., & DiPaola, S. (2021). Exploring the application of AI-generated artworks for the study of aesthetic processing. Proceedings of the IEEE Fourth International Conference on Multimedia Information Processing and Retrieval (MIPR 2021), 393–398. Paper
Utz, V., & DiPaola, S. (2020). Aesthetic judgments, movement perception and the neural architecture of the visual system. Proceedings of the Tenth Annual Meeting of the BICA Society, 538–546. Paper
Workshop papers
Utz, V. (2026). Environmental Slow AI: Design principles for generative systems. International Conference on Machine Learning (ICML) 2026, Culture × AI Workshop: Evaluating AI as a Cultural Technology. Seoul, South Korea. Paper
Utz, V. (2026). Generative AI has a slag problem. International Joint Conference on Artificial Intelligence (IJCAI), 1st Workshop on Sustainability and Resource Efficiency of Artificial Intelligence (SuRE). Paper
Yalcin, O. N., Utz, V., & DiPaola, S. (2024). Empathy through aesthetics: Using AI stylization for visual anonymization of interview videos. Conference on Human Factors in Computing Systems (CHI) 2024, EmpathiCH Workshop. Paper
Utz, V., & DiPaola, S. (2023). Digital overconsumption and waste: A closer look at the impacts of generative AI. Conference on Computer Vision and Pattern Recognition (CVPR) 2023, Ethical Considerations in Creative Applications of Computer Vision (EC3V) Workshop. Paper
Extended abstracts
Utz, V. (2025). Green user interface design for generative AI systems: Ensuring data generation and energy consumption cognizance. ACM Celebration of Cascadia Women in Computing. Vancouver, Canada. Abstract
Preprints
Utz, V. (2025). Responsible data stewardship: Generative AI and the digital waste problem. arXiv:2505.21720. Preprint
Talks and presentations
Environmental Slow AI: Design Principles for Generative Systems
ICML 2026, Culture × AI Workshop. Seoul, South Korea. Poster
Green User Interface Design for Generative AI Systems
ACM Celebration of Cascadia Women in Computing. Vancouver, Canada. Poster
Research in Human-Centered Generative AI
SFU VINCI, Shaping the Future with AI: Innovations in Visual and Interactive Computing. Vancouver, Canada. Poster
Diffusion-based Visual Generative Systems and their Climate Impact
International Conference on Computational Creativity (ICCC) 2023. Waterloo, Canada.
Digital Overconsumption and Waste: A Closer Look at Generative AI
CVPR 2023, EC3V Workshop. Vancouver, Canada. Poster
Exploring the Application of AI-Art for the Study of Aesthetic Processing
IEEE MIPR 2021, AIArt21 Workshop. Tokyo, Japan.
Computational Creativity Systems and their Application in Empirical Aesthetics
International Association of Empirical Aesthetics (IAEA) 2021 Congress. London, United Kingdom. Poster
Aesthetic Judgments, Movement Perception and the Neural Architecture of the Visual System
Biologically Inspired Cognitive Architectures (BICA) 2019 Conference. Seattle, United States.
Acknowledged contributions
Work I contributed to without authorship.
van Maanen, L., van der Mijn, R., van Beurden, M. H. P. H., Roijendik, L. M. M., Kingma, B. R. M., Miletic, S., & van Rijn, H. (2019). Core body temperature speeds up temporal processing and choice behavior under deadlines. Scientific Reports, 9, 10053. Paper
Shakeri, H., Nixon, M., & DiPaola, S. (2018). Saliency-based artistic abstraction with deep learning and regression trees. Electronic Imaging. Paper