Sageev Oore Homepage

My research interests include machine learning and deep learning, with an emphasis on music, audio processing & creative applications.

For Prospective Students

I have well-funded openings for motivated graduate and honours undergraduate students with strong backgrounds.
Please also see the important note below about diversity and inclusiveness
If you wish to email me about potential graduate or undergraduate studies, please include the subject line
"for consideration: studies at Dalhousie/Vector".
My contact information begins with "osageev", ends with "gmail.com", and has an 'at' sign in the middle.
Dalhousie University is committed to fostering a collegial culture grounded in diversity and inclusiveness. The University encourages applications from Indigenous people, persons with a disability, racially visible persons, women, and persons of minority sexual orientations and gender identities, and all candidates who would contribute to the diversity of our community.

News

2020 Outstanding Demonstration Award (Runner-up), NeurIPS, interactive tool for speech-to-music
PMLR paper by D'Eon (*), Dumpala (*), Sastry (*), Oore, and Oore
2020 Best Paper Award, CVPR ISIC Workshop on Medical Skin Image Classification (uses the Gram-OOD method below)
2020 ICML paper: Gram-OOD is a new method for OOD detection giving SOTA results on far-OOD examples
2018 Awarded Canada CIFAR AI Chair
2017 Best Demonstration Award, AAAI, Honorable Mention for interactive system for deep-learning-based musical dialogue
2016 Best Demonstration Award, NeurIPS, interactive music generation system
2016 - 2018 Visiting Research Scientist, Magenta Team at Google Brain Research

Prior News

PerformanceRNN: a recurrent neural network music generation system,
Autoencoders for Musical Dialogue
Radical Cycle is a CD that I released with my brother, Dani Oore. (Live performance excerpt)
Here is an old video of me using the Glove Talk II system.

Brief Biography

Sageev Oore is a faculty member in Computer Science at Dalhousie University (Halifax), a Research Faculty Member at the Vector Institute for Artificial Intelligence (Toronto), and a Canada CIFAR AI Chair. He is interested in basic research in machine learning and deep learning, with particular focus on applications of deep learning in audio and music, and computational creativity. He recently spent a year and a half as a Visiting Scientist in Google Brain (California), where he worked on the Magenta team developing generative music systems such as PerformanceRNN. He is also an award-winning musician; as a pianist he has performed as soloist with orchestras both as a classical soloist and as an improviser. Sageev studied piano performance with music teachers from Dalhousie, Juilliard, and UBC. He completed his undergraduate degree in Mathematics (Dalhousie), and MSc and PhD degrees in Computer Science (University of Toronto) working with Geoffrey Hinton.