Associate Professor, Faculty of Computer Science, Dalhousie University

Canada CIFAR Artificial Intelligence Chair

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 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)


Estimating Severity of Depression From Acoustic Features and Embeddings of Natural Speech

Sri Harsha Dumpala and Sheri Rempel and Katerina Dikaios and Mehri Sajjadian and Rudolf Uher and Sageev Oore


High Frequency-Low Amplitude Oscillometry: Continuous Unobtrusive Monitoring of Respiratory Function on PAP Machines

Hamed Hanafi Alamdari and Luke Hacquebard and Stephen Driscoll and Kamal El-Sankary and David Roach and Robin Leblanc and Scott Lowe and Sageev Oore and Thomas Penzel and I Fietze and Michael Schmidt and Debra Morrison


Controlling BigGAN Image Generation with a Segmentation Network


Significance of Speaker Embeddings and Temporal Context for Depression Detection

Sri Harsha Dumpala and Sebastian Rodriguez and Sheri Rempel and Rudolf Uher and Sageev Oore