CIFAR AI Catalyst Grants
Accelerating interdisciplinary AI research
AI is transforming the world, across a wide range of domains, from healthcare to fundamental physics. But none of these applications are possible without the breakthroughs developed through fundamental, curiosity-driven, and long-term research.
CIFAR AI Catalyst Grants are designed to catalyze new research areas and collaborations in machine learning and its application to different areas of science and society.
If you have any questions about these and upcoming catalyst grant calls, please contact us at ai@cifar.ca.
Catalyst Grants awarded to date
2024 awards
Generating Images with Multimodal Instruction
Advancing novel machine learning methods and successful applications for a more generalized training image generation model.
Collaborators: Wenhu Chen (Canada CIFAR AI Chair, Vector Institute, University of Waterloo), Aishwarya Agrawal (Canada CIFAR AI Chair, Mila, Université de Montréal)
Fundamentals of Transformers: From Optimization to Generalization
Creating faster algorithms and more efficient architectures for concrete optimization and statistical guarantees of transformers, applicable to natural language processing, computer vision and time-series forecasting.
Collaborators: Murat Erdogdu (Canada CIFAR AI Chair, Vector Institute, University of Toronto), Christos Thrampoulidis (University of British Columbia)
Solving Simulators with Reinforcement Learning for Material and Process Design
Applying reinforcement learning optimization for less costly, more efficient material and process design simulators for clean fuel production.
Collaborators: Martha White (Canada CIFAR AI Chair, Amii, University of Alberta), Mouloud Amazouz (Natural Resources Canada; University of Waterloo), &Ahmed Ragab (Natural Resources Canada; Polytechnique Montréal)
AI Auditing through Exploration of Model Multiplicity
Generating new insights about how user inputs (“prompts”) personalize the behavior of foundation models, and suggesting ways forward for auditing all of the behaviors exhibited by a single foundation model.
Collaborators: Golnoosh Farnadi (Canada CIFAR AI Chair, Mila, McGill University), Elliot Creager (University of Waterloo)
Robust Strategic Classification and Causal Modelling for Long-Term Fairness
Addressing errors in decision-making algorithms by taking an alternative approach to fairness, the goal being to allow individuals labelled with ‘undesirable outcomes’ to achieve ‘desired’ outcomes long-term.
Collaborators: Nidhi Hegde (Canada CIFAR AI Chair, Amii; University of Alberta) & Dhanya Sridhar (Canada CIFAR AI Chair, Mila; Université de Montréal)
Natural Language Processing for Users from Diverse Cultures
Evaluating the cultural awareness of current Western-centric LLMs with the goal of enhancing their cultural competence.
Collaborators: Vered Shwartz (Canada CIFAR AI Chair, Vector Institute, University of British Columbia), Siva Reddy (Canada CIFAR AI Chair, Mila, McGill University)
2022/2023 awards
Survival analysis with informative censoring
Improving statistical methods to make time-to-event predictions in the presence of right censored data, a field known as survival analysis, with myriad applications across industries.
Collaborators: Rahul G. Krishnan (Canada CIFAR AI Chair, Vector Institute, University of Toronto), Russ Greiner(Canada CIFAR AI Chair, Amii, University of Alberta)
Developing a framework for the evaluation of disclosure risks from tabular synthetic health data
Collaborators: Linglong Kong (Canada CIFAR AI Chair, Amii and University of Alberta), Khaled El Emam (University of Ottawa)
Hiccups on the road to Explainable Reinforcement Learning (XRL)
Advancing the emerging field of trustworthy machine learning by ensuring that DRL models are deployed in a way that reduces risks to Canadians and Canadian industries.
Collaborators: Samira Ebrahimi Kahou (Canada CIFAR AI Chair, Mila, McGill University), Marlos Machado (Canada CIFAR AI Chair, Amii, University of Alberta), Ulrich Aïvodji (ÉTS Montréal)
Human-machine co-adaptation in music improvisation via multi-agent RL
Designing agents that can improvise with humans as collaborative partners, capable of adapting to a musician's skill level and style, whether the musician is a novice or professional.
Collaborators: Cheng-Zhi Anna Huang (Canada CIFAR AI Chair, Mila, McGill University), Patrick M. Pilarski (Canada CIFAR AI Chair, Amii, University of Alberta)
An artificial intelligence-based MR imaging reconstruction framework
Collaborators: Mojgan Hodaie, Frank Rudzicz (Canada CIFAR AI Chair, Vector, Dalhousie), Timur Latypov, Marina Tawfik
Funded in partnership with Temerty Centre for AI Research and Education in Medicine
Explaining Explainability for Machine Learning Applications in STEM
Collaborators: Audrey Durand (Canada CIFAR AI Chair, Mila, Laval), Flavie Lavoie-Cardinal (Laval), Jess McIver (UBC), Renee Hlozek (CIFAR Azrieli Global Scholar, GEU), Ashish Mahabal (Cal Tech), Daryl Haggard (CIFAR Azrieli Global Scholar, GEU)
Culturally-Inclusive AI In Actua's Indigenous Youth in STEM Program
INDIGENOUS AI TRAINING GRANT
Collaborators: Valeria Ianniti (Actua)
Connecting Indigenous Youth and AI
INDIGENOUS AI TRAINING GRANT
Collaborators: Kate Arthur (Digital Moments)
Privacy-preserving generative models for retina image synthesis used for diagnosis purposes
SYNTHETIC HEALTH DATA CATALYST GRANT
Lead: Xiaoxiao Li, University of British Columbia in partnership with Roche.
Privacy-preserving data synthesis of a cohort to study and stimulate research on the opioid crisis in Canada
SYNTHETIC HEALTH DATA CATALYST GRANT
Lead: Sébastien Gambs, Université du Québec à Montréal in partnership with Statistics Canada.
Generation of confidentiality-preserving synthetic data from prescription drug consumption administrative databases for the analysis of drug use in the Quebec population
SYNTHETIC HEALTH DATA CATALYST GRANT
Co-leads: Christian Gagné, Université Laval in partnership with The Régie de l’assurance maladie du Québec.
A generator capable of creating images and associated labels for different types of images such as retina images, skin lesions and histopathology
SYNTHETIC HEALTH DATA CATALYST GRANT
Co-leads: Raymond Ng & Mathias Lecuyer, University of British Columbia Data Science Institute in partnership with Microsoft Research.
2020 awards
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)