The history of contemporary music creation is closely entwined with the history of digital technology, and today manifests as an ecosystem of software and hardware tools such as synthesizers, audio effects, and workstations. The modern musician can access a universe of sound, but traversing this space of possibilities grows exponentially more challenging as the capabilities and diversity of these tools continue to expand.
My work focuses on applications of machine learning as a support in navigating this growing complexity, and on solving some of the computational problems specific to the interface between machine learning and music technology. More broadly, my research interests include generative modeling, symmetric deep learning, audio synthesis algorithms, auditory perception.