My research focuses on data-driven music visualization that supports students and teachers. For example, augmented and alternative sheet music notation helps reading and understanding a piece more quickly. Collecting and visualizing practice data supports musicians in learning their instruments more efficiently. Besides audio and MIDI, such practice data may include motion capture of the musician. To make analysis more immersive and increase the visual connection to the instrument, visualization can be displayed in virtual and augmented reality. My collaborations include work on visualization for human-AI composing and participating in the DR4ET hackathon.