Research Interests
- Machine learning to generate music, audio, text and images
- Computational creativity: what it might be (exploring its limits) and how to work with it (e.g. developing tools for artists)
Biography
Sageev Oore completed an undergraduate degree in Mathematics (Dalhousie), and MSc and PhD degrees in Computer Science (University of Toronto) working with Geoffrey Hinton. In his MSc, he developed a minimally-supervised learning algorithm for robot localization– a precursor of particle filtering and SLAM; and in his PhD, co-supervised also by Demetri Terzopoulos, he developed a real-time tool for generating character animation. A professional musician, Sageev has performed as soloist with orchestras both as a classical pianist and as a jazz improviser. Together with his brother Dani, he recorded an album combining classical art songs with improvisation. In 2016, Sageev’s long-standing fascination with combining machine learning & music surpassed his long-standing resistance to that same topic, and he joined the Magenta project at Google Brain (Mountain View, California) as a visiting scientist, applying deep learning approaches to music. Having now joined the Faculty of Computer Science at Dalhousie University and the Vector Institute, he is building a research programme exploring machine learning in computational creativity.
Research Activity and News
- With Ian Simon: Performance RNN, a system for generating music with expressive timing & dynamics https://magenta.tensorflow.org/performance-rnn
