With the support of the Apogee Canada Research Excellence Fund to develop Robust, Intelligent and Responsible Artificial Intelligence (AI) (R³AI), IVADO offers several programs to its community. The flagship program of R³AI is the Strategic Research Regroupement program: ten Regroupements, whose function is to deploy and implement cross-sectoral scientific programming to achieve the R³AI vision.

What is a Regroupement?

The purpose of a Strategic Research Regroupement is to deploy and implement the cross-sectoral scientific programming proposed in the R³AI application. Each Regroupement is made up of a group of researchers from IVADO’s academic members, as well as experts and partners relevant to its development. Each Regroupement is responsible for one theme, and receives financial, strategic and operational support from IVADO’s management.

Why set up these Regroupements?

In our view, the Regroupement model is the best way to achieve our goals of developing and deploying R³AI, requiring bold, cross-sectoral scientific programming with a transformative reach. Under IVADO’s leadership, this means creating original, interdisciplinary research alliances that will be able to respond to the complex challenges of the program.

Regroupements objectives

Each Regroupement has the following objectives:

  • Develop ambitious, cross-sector scientific programming, i.e.:
    • a collaborative research strategy;
    • a knowledge mobilization plan tailored to the sector;
    • indicators and impact measurements;
    • national collaborations, notably with other Apogée teams, and international collaborations.
  • Encourage multi-center projects;
  • Train the next generation of researchers;
  • Propose and organize scientific activities to advance the research theme in collaboration with other IVADO strategic research groups;
  • Integrate best practices in terms of Equity, Diversity and Inclusion (EDI) into the composition and all activities of the Regroupement;

The Regroupements are expected to advance the science of R³AI and collectively contribute to the goals of creating robust, reasoned and responsible AI, as well as its adoption.

R1 - AI - Neurosciences

The aims of this Regroupement will be to formalize the functions of conscious processing (generalization to new contexts – robustness, knowledge representation and causal reasoning, metacognition or reflection on thoughts) in order to advance AI and stimulate the development of new explanatory theories for cognitive neuroscience. These theories can then be experimentally tested and used in neurocognitive research. These new R³AI algorithms will be deployed and tested in IVADO’s various application domains, including AI for science, healthcare systems and supply chains.

Sylvana Côté
(UdeM)
Karim Jerbi
(UdeM)
Flavie Lavoie-Cardinal
(ULaval)
Blake Richards (McGill)

R2 - Machine Learning

The aim of the Regroupement will be to develop cutting-edge research on machine learning algorithms, deep learning and decision making under uncertainty. This Regroupement will also be invited to respond to the problems of other Regroupements and to collaborate closely with them, with the aim of accelerating the development of more robust, better reasoning and more transparent algorithms.

Aaron Courville
(UdeM)
Gauthier Gidel
(UdeM)
Doina Precup
(McGill)

R3 - Natural Language Processing (NPL)

The aims of this Regroupement are to design a new generation of ML algorithms for NLP whose architecture is more structured, in terms of knowledge elements that can be recombined. Applying these new systems and working in collaboration with minorities to mitigate the prejudice and discrimination associated with modern NLP and towards systems that can be guided by humans to understand stories, with a particular focus on the stories and learning of indigenous cultures. Research in this Regroupement should be able to address the following issues: (1) facilitating the integration of natural language instructions, social or moral norms, or verbalized knowledge ontologies into R³AI systems, (2) developing systems that allow AIs to be subject to moral principles (in the spirit of Asimov’s Laws of Robotics) and (3) examining indigenous narratives about land and biodiversity.

Siva Reddy
(McGill)
Amal Zouaq
(Polytechnique Montréal)

R4 - Implementation Science and Responsible Governance

This Regroupement has four main goals that need to be worked on in collaboration with minority or marginalized groups: (1) informing an accountable process while ensuring greater representation of marginalized groups and leading to more equitable and safer design, implementation, and outcomes of AI, (2) understanding the new business models that R³AI could help support, identifying the human factors that contribute to the use of AI, defining transparent and reliable governance models that avoid concentration of power, for the responsible and environmentally sustainable use of AI, and the data needed to calibrate AI models, and determine how AI can create better and more meaningful work, (3) study the drivers of AI adoption, uncover implicit human responses to AI systems, and correlate these responses with design choices made by developers, (4) clearly define trustworthy and safe AI, and produce methods, tools, and technologies that enable the development of trustworthy intelligent applications, (5) propose governance norms and models for democratic societies that are resilient to the impacts, harms, and risks of increasingly powerful AI.

Foutse Khomh
(Polytechnique Montréal)
Lyse Langlois
(ULaval)
Pierre-Majorique Léger (HEC Montréal)

R5 - Ethics, EDI, and Indigenous Engagement

The aim of this Regroupement is to develop cutting-edge research in this field and explore pressing ethical questions, while ensuring that their findings can be integrated into the research of other Regroupements. Among other things, this Regroupement will work on the various parameters that impact the robustness of algorithms and induce biases (e.g. the data collected), as well as on explainability. Working methods based on co-construction and including minority groups and indigenous communities will also be examined by this Regroupement. Research on ethical aspects of AI and the social implications of R³AI is included. Finally, research interests will also need to reflect the priorities of indigenous communities so that they can be included in R³AI.

Joé Martineau
(HEC Montréal)
Annie Pullen Sansfaçon
(UdeM)
Daniel Weinstock
(McGill)

R6 - Molecules and Material Discovery

The goal of this Regroupement will be to transform the discovery of molecules and materials by replacing brute force screening with AI-based modeling and experimental design methods, allowing a more targeted but diversified search in both model space and the space of possible experiments. R³AI models will incorporate physico-chemical knowledge and be integrated into classical drug discovery pipelines with active learning loops to (1) support search in molecular space, (2) discover causal models involving drug targets, genes and proteins in cells (for target identification), and molecular interactions, (3) enable prediction of molecular structure and properties, and (4) optimize synthesis strategies. A similar methodology will be used to efficiently explore the phase space of new materials for batteries, energy storage or carbon capture.

Yves Brun
(UdeM)
Audrey Durand
(ULaval)
Audrey Laventure
(UdeM)
Carlos Silva
(UdeM)

R7- Environment

The goals of this Regroupement will focus on (1) developing algorithms capable of learning from ever-increasing data sets (drones, transportation companies, etc.) and providing analyses to develop effective climate change and biodiversity policies, (2) understanding biodiversity loss in the context of climate change, (3) designing the active learning pipelines needed to guide targeted drone surveys over areas undergoing vegetation change to minimize epistemic uncertainty about, for example, the future fate of carbon and nitrogen stocks and fluxes in Canada and beyond.

Julie Carreau
(Polytechnique Montréal)
Étienne Laliberté
(UdeM)
Laura Pollock
(McGill)
David Rolnick
(McGill)

R8 - Health Systems

The two primary goals of this Regroupement are: (1) to develop and validate a value-based integrative framework to guide the development and deployment of R³AI in health care and to develop guidelines for policymakers and AI developers to support R³AI in health care systems, and (2) to study cases of AI development and deployment in a variety of contexts to empirically explore a) the perspectives and assumptions that guide developers’ work, (b) the expectations and claims that potential users, including minority groups, have about AI for specific innovations, and c) the expectations and strategies that users mobilize in a real-world context to adopt, implement, and assess the value of an innovation.

Mickaël Chassé
(UdeM)
Christian Gagné
(ULaval)
Nadia Lahrichi
(Polytechnique Montréal)
Aude Motulsky
(UdeM)
An Tang
(UdeM)

R9 - Supply Chains

The objectives of this Regroupement will be to exploit the data generated to optimize solutions to environmental problems, healthcare system challenges or the supply of active ingredients for the production of molecules (drugs or materials), and to propose better planning systems relevant to all the application sectors described above, as well as innovative ML and OR methods to manage them at strategic, tactical and operational levels, taking into account the economic, societal, environmental and human context.

Yossiri Adulyasak
(HEC Montréal)
Jean-François Cordeau
(HEC Montréal)
Erick Delage
(HEC Montréal)
Emma Frejinger
(Université de Montréal)

R10 - AI Safety and Alignment

This Regroupement will integrate modern optimization and machine learning methods to improve the efficiency and resilience of supply chains and mobility systems, while reducing their environmental footprint. The goal will be to exploit machine learning: 1) in the design of optimization models that will make these systems more reactive to changing conditions; 2) to accelerate the optimization of extra large systems that account for economic, societal, environmental and/or human context; 3) to quantify uncertainty and make supply chains more resistant to disruptions caused by natural disasters, pandemics, wars and other global events.

Yoshua Bengio
(UdeM)
Chris Pal
(Polytechnique Montréal)
Danya Sridhar
(UdeM)

If you have any questions, please contact the team at: programmes-excellence@ivado.ca