Description and objectives

This program supports research in the areas delineated in our Canada First funding proposal: data science in the broad sense, encompassing methodological research in data science (machine learning, operations research, statistics) and its applications in multiple sectors.

The program will enable the funding and support of up to five large-scale research framework programs related to strategic topic areas. The total IVADO budget for this funding opportunity is of the order of $6 million. IVADO’s financial contribution will be from $1 million to $2 million per research framework program, with, ideally, complementary additional funding coming from outside sources (e.g., funding organizations, industry contributions, …).

These research framework programs will be developed in collaboration by research teams to address major scientific challenges in digital intelligence. Projects must have an impact in terms of advancing knowledge in digital intelligence as well as strong potential for impact on socio-economic development in Québec and the rest of Canada, while strengthening the international and global positioning of our community. Moreover, the research framework programs must have the capability to structure and mobilize a dynamic community (academia, industry, field of application, etc.) in Québec and the rest of Canada and, ideally, help to build new international collaborations.

To ensure that the advancement of knowledge and opportunities in digital intelligence equitably benefits all members of society, we promote principles of equity, diversity, and inclusion across all of our programs and are committed to providing you with an inclusive, non-discriminatory, open, and transparent recruitment process and research environment.

It is important to reiterate that a governance process adapted to the specific nature of this program has been set up so as to ensure the highest standards for conflict-of-interest management and for respect of equity, diversity and inclusion principles throughout the process.

Process

This funding opportunity is not a competition but a call to the research ecosystem to work together. For this reason, a consultation was launched (Stage 1) to identify, together, strategic topic areas that the chosen research framework programs could address (Stage 2) before developing a detailed research proposal (Stage 3). Please send us your ideas for strategic topic areas before July 9, 2021.

Stage 1: Consultation within the IVADO ecosystem (June 1 to July 20, 2021)

IVADO began by holding an iterative consultation with members of its ecosystem to help shape strategic topic areas relevant to this funding opportunity. In addition to the proposed strategic topic areas, the consultation will also be an occasion to receive high-level drafts of framework programs along with lists of the professors and partners potentially relevant in the funding opportunity as well as potential funding partners for each proposed strategic topic area.

To enable the iterative process with the ecosystem, IVADO is regularly updating the “Strategic Topic Areas” section of the program Web page as proposals are received and invites the ecosystem members to submit comments throughout the consultation process, which will conclude on July 20, 2021.

Stage 2: Identification and selection of the strategic topic areas (August to November, 2021)

An advisory committee was created to assist the IVADO team in identifying strategic themes based on the proposals received during the consultation (Step 1). This committee is composed of:

  • Janice Bailey (Scientific Director, FRQ-NT) and Carole Jabet (Scientific Director, FRQ-S): co-chairs of the committee;
  • Yves Joanette (Université de Montréal), Michèle Breton (HEC Montréal) and Louis-Martin Rousseau (Polytechnique Montréal): representatives appointed by the Vice-Rectorates Research;
  • Jean-François Cordeau (HEC Montréal), Marc Bellemare (Google Brain, Mila) and Julien Cohen-Adad (Polytechnique Montréal): representatives proposed by the IVADO scientific committee.

IVADO will continually collect and collate the strategic topic area received during the consultation period for presentation to the Advisory Committee. The Committee will discuss the strategic topic areas and may suggest modifications, pooling of proposals or other improvements on an iterative basis with the IVADO team. The Committee will consult the IVADO International Committee. The Committee will present the selected strategic topic areas to the IVADO Executive Committee, including, for each, identification of the “champions”/professors (potential PI) with an eye to establishing the program committees. No member of the Advisory Committee can be a PI of a research framework program selected by IVADO. The Scientific Committee will be kept informed throughout the process.

In early November 2021, the Advisory Committee forwarded its recommendations to the Executive Committee*, who continued to reflect on these bases. Some lead researchers have been invited to a presentation with the Executive Committee*, where a draft scientific program will be tabled for discussion and approval. 

Stage 3: Co-building of the framework programs for research (November to December 2021)

IVADO will assist the PIs in the development of the Research Framework Program proposal (team composition, description of the research project, preliminary budget and justifications, dissemination plan of the results, training plan for HQP, EDI commitments). The research framework proposal will be presented by the PIs to the Executive Committee* for approval. The Executive Committee may request modifications. The proposal, while not in competition with others at this stage, will meet the standards generally expected of an application of this size. The scientific committee will be informed of the progress of the proposals during the co-construction process.

Stage 4: Rollout of the framework programs (January 2022 – August 2024)

Research activities may begin as early as January 2022. Research teams, supported by a dedicated full-time IVADO resource for each Framework Program (funded from the Framework Program budget), will be required to commit to submitting scientific and financial reports for annual evaluation by an independent committee of reviewers. IVADO funds must be used by the end of August 2024, the current deadline for the use of IVADO funds.

*Excluding the Executive Committee members identified by the community for the selected strategic themes.

Strategic topic areas

Please consult the page in French for a detail of the submitted proposals.

STRATEGIC TOPIC AREA PROPOSAL

Selected research programs

Here are the research programs that will be funded through this initiative, stemming from the 5 strategic topics selected among the 48 proposed during the consultation stage (see process above).

Topic 1 – Integrated Machine Learning and Optimization for Decision Making under Uncertainty: Towards Robust and Sustainable Supply Chains

Lead researchers: Erick Delage (HEC Montréal, GERAD), Yossiri Adulyasak (HEC Montréal, GERAD), Emma Frejinger (Université de Montréal, CIRRELT)

Nearly all decision problems involve some form of uncertainty. This is especially true in supply chains where, e.g., demand, cost, capacity, and travel time’s high variability considerably complicate the planning of procurement, production, distribution, and service activities. Due to constantly evolving environments and the high frequency of data acquisition, classical decision-making that is based on training models, validating them, to finally optimize decisions does not suffice anymore. This research program aims at developing new methods for making the most effective and adaptive use of data in decision-making. It is founded on modern optimization and machine learning perspectives that encompasses developments in deep reinforcement/end-to-end learning, risk averse decision theory, and contextual/distributionally robust optimization. Its mission is three-fold: (i) develop the next generation of methods to deal with uncertainty in data-driven risk-aware optimization models by integrating machine learning; (ii) identify scientifically challenging and high-impact opportunities for improving robustness in supply chains; and finally (iii) stimulate the integration of stochastic optimization models among our partners while defining use cases that will guide future methodological advances. Overall, this program envisions a virtuous cycle of scientific discoveries that are both fueled by and transformative for an important sector of the Canadian economy.

Topic 2 – AI, Biodiversity and Climate Change

Lead researchers: Etienne Laliberté (Université de Montréal, IRBV), Christopher Pal (Polytechnique Montréal, Mila), David Rolnick (Université McGill, Mila), Oliver Sonnentag (Université de Montréal), Anne Bruneau (Université de Montréal, IRBV)

Climate change is altering plant biodiversity, with potentially catastrophic consequences on the resilience and functioning of terrestrial ecosystems. A major source of uncertainty in the global terrestrial carbon budget, and thus for future climate projections, is how plant species differ in their phenological responses to seasonal climate fluctuations. In addition, climate change reshuffles plant species distributions across entire landscapes, but we are unable to keep track of those changes in biodiversity using classical field-based sampling. Remote sensing technologies such as phenocams or drones offer potential to study plant phenology and biodiversity in great detail across spatial scales. These new approaches could revolutionise biodiversity science and conservation, and help guide the design of nature-based solutions essential to mitigate the effects of  climate change. New AI algorithms are needed to unlock the full potential of this transformative technology and its links to more traditional data streams and products. This program will develop these new algorithms, building on the most recent developments in computer vision and meta-learning to map plant species and their phenological signatures. Algorithms will be put directly into the hands of scientific and non-scientific end-users via the development of an active learning platform. This AI research will empower researchers and practitioners to turn imagery into actionable data about plant biodiversity and phenology, providing them with tools to help fight biodiversity loss and the effects of climate change.

Topic 3 – Human health and secondary use of data

Lead researchers: Michaël Chassé (Université de Montréal, CRCHUM), Nadia Lahrichi (Polytechnique Montréal, CIRRELT), An Tang (Université de Montréal, CRCHUM)

Artificial intelligence (AI) technologies hold the potential to transform healthcare. These technologies are emergent in logistics and imaging, and hundreds of algorithms are now being developed to help support care delivery. Many challenges remain, however, when it comes to scale-up for use in the field. One such challenge is ensuring the generalizability of such algorithms. How can we guarantee the effectiveness of one model on a data set with characteristics that differ from the one the algorithm learned with? For example, an algorithm trained using data from a specific population may not perform as well when applied to a different population.

This program therefore aims to study new methods for improving generalization, and pursues four objectives. First, set up a research environment enabling the study of methods likely to improve generalization in real-world contexts. Second, optimize data flows obtained in real-world healthcare settings to serve algorithm research. Next, investigate specific issues related to algorithm generalization and secondary use of medical data. Lastly, create an open data set that can be used to build upon the research program findings.

Topic 4 – AI for the discovery of materials and molecules

Lead researchers: Yoshua Bengio (Université de Montréal, Mila), Michael Tyers (Université de Montréal, IRIC), Mickaël Dollé (Université de Montréal), Lena Simine (Université McGill)

Designing molecules with desired properties is a fundamental problem in drug, vaccine, and material discovery. Traditional approaches to designing a new drug can take over 10 years and a billion US dollars. Materials have been developed solely based on their performance characteristics leading to materials composed of rare, often toxic elements, which can inflict significant environmental damage. Artificial intelligence (AI) has the potential to revolutionize drug and material discovery by analyzing evidence from large amounts of data accumulated and learning how to search in the compositional space of molecules, and hence significantly accelerate and improve the process.

This program aims to build an efficient and effective machine learning framework for searching molecules with designed properties. It will be crucial to build upon, and extend, ongoing collaborations (i) between Mila and IRIC, aimed at optimizing the algorithms to discover new antibiotics and (ii) between Mila and materials experts at McGill and Université de Montréal, on the development of materials with environmental applications like fighting climate change. This multidisciplinary project also raises exciting fundamental challenges in AI regarding learning to search, modeling and sampling complex data structures like graphs, and may have applications to scientific discovery more broadly.

Topic 5 – Human-centered AI: From Responsible Algorithm Development to Human Adoption of AI

Lead researchers: Pierre-Majorique Léger (HEC Montréal, Tech3lab), Sylvain Sénécal (HEC Montréal, Tech3lab)

Human-AI interactions are common nowadays. We interact with artificial intelligence daily in performing many professional and personal tasks. Humans’ adoption of AI, however, is far from automatic, successful or satisfactory. Whether we are citizens, employees or consumers, issues such as bias, lack of trust and even low user satisfaction affect our likelihood of adopting AI in various contexts. To foster adoption, a holistic approach to AI is therefore needed. This multidisciplinary research program is investigating the full cycle of responsible AI development, from inception to adoption by users, putting people at the heart of the process. The goal is to map out guidelines for human-centred AI design using an iterative, multimethod methodological approach led by a multidisciplinary research team.

Conditions of funding

The requirements in this section apply only to IVADO’s financial contribution to the selected research framework programs. Ideally, the budgets will comprise other funding sources with their own specific conditions and that may, for example, allow for funding of researchers on the team who are not eligible to receive IVADO funding.

IVADO’s financial contribution to this program is drawn from its Canada First grant. These funds may be transferred to and managed solely by eligible professors in the institutions covered by the initial Canada First agreement, as described below:

  • You must be a faculty member at one of the following institutions: HEC Montréal, Polytechnique Montréal, Université de Montréal, McGill University* or University of Alberta*;
    *If you are a faculty member at the McGill University or University of Alberta, you must also be a member of one of our research groups (Mila, CIRRELT, GERAD, CERC in Data Science for Real-Time Decision-Making, CRM, Tech3Lab, Consortium Santé Numérique);
  • You must have one of the following eligible statuses: assistant professor, associate professor, full professor, research professor, or visiting professor. Adjunct professors are not eligible.

Professors who do not meet these eligibility criteria may take part in a research framework program as a collaborator.

Information sessions

2 virtual information sessions took place on June 9 and 14.

F.A.Q.

Please consult the F.A.Q. available on the French version of the web page.

You can send your questions to recherche-strategique@ivado.ca.

In the event of any differences in translations or interpretations, the French version shall prevail.