Research projects will be funded by the CIFAR AI Catalyst Grants program.
The projects explore a range of areas such as the exploration of modern machine learning algorithms; machine learning to generate rhythmic auditory stimulation to help patients with Parkinson’s walk; enable intelligent machines to better understand natural language and carry out complex tasks; and, to predict and control the future state of an AI system, among others.
In partnership with the RBC Foundation, two CIFAR AI Catalyst Grants will be awarded to support research in the areas of privacy, accountability, transparency and bias in machine learning.
The call for proposals for CIFAR AI Catalyst Grants was launched in January 2020 as an initiative of the Pan-Canadian AI Strategy’s National Program of Activities. The grants will catalyze new research areas and collaborations in machine learning and its application to different areas of science and society. Projects will receive up to $50,000 to support their research for a period of up to two years. The grants are intended to support collaborative research and exchange with Canada CIFAR AI Chairs as part of the Pan-Cananadian AI Strategy’s National Program of Activities.
- DeepCell: analyze and integrate spatial single-cell RNA-seq data
Developing deep learning-based tools to analyze and integrate spatial single-cell RNA-seq data for brain tumours
Collaborators: Bo Wang (Canada CIFAR AI Chair, Vector Institute, UHN, University of Toronto), Michael Taylor (University of Toronto, Sick Kids Hospital)
- Rethinking generalization and model diagnostics in modern machine learning
Exploring the interesting properties of modern machine learning algorithms
Collaborators: Murat Erdogdu (Canada CIFAR AI Chair, Vector Institute, University of Toronto), Ioannis Mitliagkas (Canada CIFAR AI Chair, Mila, Université de Montréal), Manuela Girotti (Mila, Concordia University)
- Learning to solve mixed-integer linear programs
Utilizing machine learning for mixed-integer linear programming
Collaborators: Laurent Charlin (Canada CIFAR AI Chair, Mila, HEC, Université de Montréal), Chris Maddison (Vector Institute, University of Toronto)
- Language grounded in vision for embodied agent navigation and interaction
Enabling an intelligent agent the ability to understand natural language in the context of navigational tasks
Collaborators: Chris Pal (Canada CIFAR AI Chair, Mila, Polytechnique Montréal, Université de Montréal), Sanja Fidler (Canada CIFAR AI Chair, Vector institute, University of Toronto), David Meger (Mila, McGill University)
- Privacy and ethics in AI: Understanding the synergies and tensions
Exploring the tensions and synergies that can emerge in the deployment of Machine Learning algorithms, with a focus on accountability, transparency and bias
Collaborators: Nicolas Papernot (Canada CIFAR AI Chair, Vector Institute, University of Toronto, Google), Sébastien Gambs (Université du Quebec)
- Being politic smart in the age of misinformation
Using graph mining to detect and combat misinformation in mass information systems
Collaborators: Reihaneh Rabbany (Canada CIFAR AI Chair, Mila, McGill University), André Blais (Université de Montréal, Royal Society of Canada), Jean-François Gagné (Université de Montréal), Jean-Francois Godbout (Université de Montréal)
- Adaptive generative rhythmic models for neurorehabilitation
Exploring the benefits of sound and music, specifically rhythmic auditory stimulation (RAS) to Parksinson’s patients
Collaborators: Sageev Oore (Canada CIFAR AI Chair, Vector Institute, Dalhousie University), Michael Thaut (Canada Research Chair, University of Toronto)
- A reinforcement learning based system for automation level adaptation in automated vehicles for people with dementia
Advancing the field of human compatibility of AI as applied to individuals with dementia by using novel algorithms to facilitate compatibility
Collaborators: Sarath Chandar (Canada CIFAR AI Chair, Mila, Polytechnique Montréal), Alex Mihailidis (University of Toronto, UHN)
- Modeling embodied agents with Koopman Embeddings
Using dynamical systems to predict a future state of a system, and then control it
Collaborators: Liam Paull (Canada CIFAR AI Chair, Mila, Université de Montréal), James Forbes (McGill University)
The National Program of the CIFAR Pan-Canadian AI Strategy brings together AI researchers and trainees from across the country to share ideas, foster collaboration, advance knowledge and drive Canada’s leadership in AI research and innovation. We offer training opportunities on the latest technical advances and social considerations of the applications of AI; an annual conference to bring our national community together; and other programs and activities to support and advance Canadian AI research. Our programs are open to all Canadian researchers and trainees who are interested in AI research. We aim to support world-leading AI training, research and innovation while at the same time, fostering the values of equity, diversity and inclusion and social responsibility in AI research.
In partnership with: