I am a choreographer and performer with a background in data science, working at the intersection of embodied practice, media arts, and machine learning. At a time when generative AI is rapidly entering cultural production, my research asks how computational systems can encounter human expression without erasing agency, ambiguity, or care.


I design artist-centered ML systems that listen and respond rather than generate or replace. A key focus of my work is identifying what should remain partial, invisible, or resistant to computation when technology engages with lived, embodied experience.


My recent projects include the development of a real-time movement recognition system created specifically for a full-length dance performance, where the choreography and the machine learning system were developed together through iterative collaboration with performers. I am currently expanding this research through new performance works, ethical and speculative frameworks for creative AI, and cross-disciplinary collaborations with artists, musicians, and curators.