Page last updated: Monday, April 6 at 5:00pm ET
Call for Proposals: AI and COVID-19 Catalyst Grants (up to $15,000 CAD)
Deadline: April 3, 2020 11:59 PM EDT
CIFAR is issuing a targeted call for AI and COVID-19 interdisciplinary research collaborations to spark innovative, high-risk/high-reward ideas and projects. CIFAR’s Catalyst Program provides seed funding for time-limited activities.
Sample topics may include: pathogenicity and/or transmission of COVID-19, AI tools to accelerate vaccine development, or AI applications to advance our understanding of the economic impacts of the COVID-19, among others.
Examples of eligible catalyst-supported activities include, but are not limited to:
- Pilot projects
- Interdisciplinary projects involving trainees
- Large-scale, team-based grant proposal development
- Proposals must be submitted jointly by at least two Principal Investigators
- Must not exceed one page (approx. 500 words).
- Title and brief description of the project or activity
- Proposed start date of the activity
- Name(s) of co-supervised trainee(s) (if applicable/known)
- Estimated budget (not to exceed $15,000 CAD)
Proposal should describe the potential:
- impact on society;
- understanding of an aspect of COVID-19;
- impact on career development for trainees; and
Proposals will be reviewed and recommended for funding by CIFAR’s program advisory committee members and international college of reviewers.
For more information, please contact: Rachel Parker, PhD, Senior Director, Research (CIFAR)
REPORTS & OUTCOMES
International Roundtable on AI & COVID-19
March 23, 2020
Read the report
A virtual roundtable, chaired by Dr. Alan Bernstein, President & CEO, CIFAR included Canadian and international leaders in AI, start-ups, experts in infectious disease, epidemiology and clinicians. The group explored opportunities for collaboration and sharing of datasets between AI and infectious disease researchers.
- The Statistics Research Institute at Statistics Korea is organizing a meeting with G20 leaders to take decisive actions together in public health, data sharing, medical treatment, and vaccine.
AI and COVID-19 Virtual Policy Briefing
March 24, 2020
Following the first AI and COVID-19 Expert Roundtable, CIFAR held a virtual briefing for Canadian and international policy-makers to provide up-to-the-minute updates and insights. Over 50 participants from federal, provincial and municipal governments across Canada as well the US and UK were briefed on the policy relevant insights on data and the potential of AI that emerged from the expert roundtable.
Read the briefing report
Outcomes and Next Steps:
Data access and sharing: The need for high quality, integrated data is critical in responding to COVID-19 and future pandemics was a key discussion point. CIFAR is working with federal and provincial health data holders to better understand their needs and opportunities for collaboration within the context of COVID-19 and AI.
Contract tracing app development: As has been seen in other jurisdictions, contact tracing can be critical in reducing the rate of transmission of COVID-19. Two teams led by CIFAR Fellows and Canada CIFAR AI Chairs Alan Aspuru-Guzik (Vector Institute, University of Toronto) and Yoshua Bengio (Mila, Université de Montréal) are working very quickly to develop apps for contact tracing while maintaining the privacy of users.
Planning for a future virtual discussion to share insights from across jurisdictions is currently underway.
For more information, contact:
Vice-President, Engagement & Public Policy (CIFAR)
CIFAR Learning in Machines & Brains Program
Call to Action: Machine Learning & COVID-19
March 20, 2020
CIFAR held a conference call open to all members of its Learning in Machines & Brains program. The group discussed projects and initiatives Fellows and Advisors are engaged in that connect AI and machine learning applications to COVID-19.
Program members are currently working on projects that touch all areas of society including:
- biological aspects and vaccine creation;
- contamination models (prediction, health systems capacity, etc.);
- information dissemination (mis/dis-information); and
- tools for socialization during mass social distancing.
Areas of strategic importance that may require additional funding and support for collaborations include:
- need for increased computing power
- formation of data trusts
- open source data and information.
For more information, or to support these efforts, please contact:
Rachel Parker, PhD, Senior Director, Research (CIFAR)
COVID-19 RESOURCES FOR RESEARCHERS
White House Office of Science and Technology Policy Call to Action to the ML Community on a new machine readable COVID-19 dataset (CORD-19) which contains over 29,000 scientific articles on the virus. The Call to Action asks ML experts to develop new text and data mining techniques that can help the science community answer high-priority scientific questions related to COVID-19.
- The CORD-19 resource is available on the Allen Institute’s SemanticScholar.org website and will continue to be updated as new research is published in archival services and peer-reviewed publications.
- Researchers should submit the text and data mining tools and insights they develop in response to this call to action via the Kaggle platform.
Datasets and other resources
The Cyclica stimulus plan provides access to expertise and AI-enabled platforms suited for drug repurposing, target deconvolution, and drug design. The company offers their technical support with no upfront cost to academic groups and biotech companies working on COVID-19.
ElementAI has released an open research platform for the scientific community. This free semantic search platform has been developed to assist researchers and the scientific community at large, to help access information and accelerate research efforts vital to mitigating the pandemic. Currently built on the COVID-19 Open Research Dataset CORD-19), the database will continue to grow with both structured and unstructured formats as other open datasets become available.
DarwinAI announced COVID-Net: an open source neural network for COVID-19 detection. They are open-sourcing this model to the community in hopes of developing a robust tool to assist health care professionals in combating the pandemic. The source code, documentation, dataset, and scientific paper describing COVID-Net are available at this GitHub repository.
Canadian Institute for Health Information (CIHI) Canadian COVID-19 Resources: includes data on health care workers, hospital beds and ventilation, and further health system and spending data.
Crowdsourced symptom report and tracking sites:
UK Research and Innovation (UKRI) developed a primer on the science of Coronavirus, with articles summarizing the best available information on the virus, the disease it causes, and the progress of research into treatments, vaccines, epidemiology, and public health.
A team of Canadian researchers, students, activists, and web developers built a resource page with links for scientists, policy makers, and the public. They are compiling lists of research projects, volunteer opportunities, and reagent requirements https://covid19resources.ca/
CanCOVID - Canada's federally mandated, expert-led COVID-19 research response.
Mila researchers are using AI to tackle issues around COVID-19.They are seeking additional collaborators from all areas of computer science and health sciences to strengthen these projects. To join the effort, you are encouraged to reachout at: email@example.com.
Data Scientists Against COVID-19 with a mission to connect people who have COVID-19 related data and/or require help of data scientists with data scientists.
DeepMind has released structure predictions for six proteins associated with the virus that causes COVID-19, generated by the most up-to-date version of its AlphaFold deep learning system. DeepMind is releasing the open license research in consultation with the U.K.’s Francis Crick Institute. Read the full statement. Please note the predictions have not been experimentally verified but may contribute to the research community’s understanding of the virus.
CIFAR COMMUNITY COVID-19 RESEARCH
CIFAR researchers are conducting research in a variety of areas to address the COVID-19 crisis:
Alán Aspuru-Guzik (Fellow, Bio-inspired Solar Energy, Canada CIFAR AI Chair, Vector Institute) is scaling therapeutic molecules, new soaps and coatings, and inhibitors for its reproduction machinery – Aspuru-Guzik is also leading a project to use active learning models to find the most effective surfactants and surface coatings for reducing viral lifetimes. The goal is to reduce their lifetimes to under one hour.
The teams of Yoshua Bengio (Co-director, Learning in Machines & Brains, Canada CIFAR AI Chair, Mila), Jian Tang (Canada CIFAR AI Chair, Mila), graduate student Maksym Korablyov (Université de Montréal), and the Mila startup InVivo AI, has developed a deep reinforcement learning system which can quickly evaluate billions of candidate molecules. The approach can gradually modify the molecular structure by adding or removing building blocks in order to converge toward new molecular structures that can bind a target protein.
Arturo Casadevall (Fungal Kingdom, Johns Hopkins University) is developing a treatment for COVID-19 based on the antibodies of survivors, extracted from blood serum. The treatment has been approved for compassionate use by the US Food and Drug Administration. The National COVID-19 Convalescent Plasma Project is now asking for plasma donations from patients who have recovered from COVID-19.
Eran Elinav (Humans & the Microbiome, Weizmann Institute) has converted his institute’s robotics and microbiology expertise to set up a fully-automated, high-throughput SARS-CoV-2 detection system, capable of screening 20,000 diagnostic samples in one run of a few hours. He expects calibrations to be complete the week of March 30, followed by a roll-out to test Israelis, and then sharing the process globally.
David Fleet (Canada CIFAR AI Chair, Vector Institute) and a team of researchers have developed a software program that led to the first 3D mapping of the Coronavirus.
Marzyeh Ghassemi (Canada CIFAR AI Chair, Vector Institute) is using AI for screening and risk stratification of COVID-19 patients with CT/X-ray data.
Susan Helper (Innovation, Equity & the Future of Prosperity, Case Western Reserve University) is providing advice on the best economic policy options available. She has written articles on how hotels and car manufacturers could be repurposed to fight COVID-19, how the US supply chain must change, and articulated broad principles for economic policy during this time. She has also given a talk on the economic impact of coronavirus.
Evan Lieberman (Boundaries, Membership & Belonging, MIT) co-authored a white paper on solidarity, shedding light on the nature of social solidarity and how it might be effectively built and maintained in the face of the COVID-19 pandemic.
Quaid Morris (Canada CIFAR AI Chair, Vector Institute) is using AI to explore ways to analyze COVID-19 sequences.
Reihaneh Rabbany (Canada CIFAR AI Chair, Mila) is using data mining of COVID-19 related tweets to discover temporal and spatial trends, as well as common mentions and keywords linked to COVID-19.
Irina Rish (Canada CIFAR AI Chair,Mila) and Guy Wolf (Université de Montréal) are leading a project which leverages data analysis to provide mechanistic understanding of COVID-19 disease progression in order to assess the risk of given medical/patient profiles, as well as to help identify binding targets for antiviral agents and potential vaccines.
Bo Wang (Canada CIFAR AI Chair, Vector Institute) and collaborators at Vector Institute have released a scientific paper search tool to help with the COVID-19 crisis.The tool provides the most up-to-date capture of relevant research papers to aid researchers around the world.
Frank Wood (Canada CIFAR AI Chair, Mila) and his collaborators at the PLAI research group at UBC have produced the paper Planning as Inference in Epidemiological Models. The research demonstrates how existing software tools can be used to automate parts of infectious disease-control policy-making via performing inference in existing epidemiological dynamics models.
If there are additional resources or ongoing research you would like to see included on this page, please contact:
Elissa Strome, PhD
Interim Vice-President Research & Executive Director,
Pan-Canadian AI Strategy (CIFAR)
We will endeavour to keep this page as up to date as possible, given the quickly changing circumstances.