R³AI: Research Regroupements to support the major advancement of AI knowledge

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.

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
Université de Montréal
CHU Sainte-Justine

Researchers

Pierre Lune Bellec
Université de Montréal

Catherine Duclos
Université de Montréal
Hôpital du Sacré-Cœur de Montréal

Guillaume Dumas
Université de Montréal
CHU Sainte-Justine

Sarah Lippé
Université de Montréal
CHU Sainte-Justine

Eilif Muller
Université de Montréal Matthew Perich
Université de Montréal

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.

Researchers

Simon Lacoste-Julien
Université de Montréal

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.

Researchers

Aishwarya Agrawal
Université de Montréal

Philippe Langlais
Université de Montréal

Fatiha Sadat
Université du Québec à Montréal

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.

Researchers

Catherine Beaudry
Polytechnique Montréal

Dalia Gesualdi-Fecteau
Université de Montréal

Jean-François Godbout
Université de Montréal

Anne-Sophie Hulin
Université de Sherbrooke

Aude Motulsky
Université de Montréal
CRCHUM

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.

Annie Pullen Sansfaçon
Université de Montréal

Researchers

Isabelle Archambault
Université de Montréal

Marc-Antoine Dilhac
Université de Montréal

Karine Gentelet
Université du Québec en Outaouais

Allison Marchildon
Université de Sherbrooke

Dominic Martin
Université du Québec à Montréal

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.

Researchers

Alex Hernandez Garcia
Université de Montréal

Sébastien Lemieux
Université de Montréal

Teodor Veres
National Research Council Canada

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.

Étienne Laliberté
Université de Montréal

Researchers

Soumaya Cherkaoui
Polytechnique Montréal

Samira Keivanpour
Polytechnique Montréal

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.

Michaël Chassé
Université de Montréal
CRCHUM

Researchers

Robert Avram
Université de Montréal
Montreal Heart Institute

Julien Cohen-Adad
Polytechnique Montréal

Julie Hussin
Université de Montréal
Montreal Heart Institute

Venkata Manem
Université Laval
CHU de Québec

Jean Noel Nikiema
Université de Montréal

Esli Osmanlliu
McGill University
McGill University Health Centre

Louis-Martin Rousseau
Polytechnique Montréal

Mireille Schnitzer
Université de Montréal

Pablo Valdes Donoso
Université de Montréal

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.

Researchers

Margarida Carvalho
Université de Montréal

Roussos Dimitrakopoulos
McGill University

The goal of this cluster is to advance machine learning algorithms and the development of robust security protocols, enabling the development of AI systems that are safe, controllable and aligned with intentions and instructions from their designers. Misalignment between human intentions and the AI system behavior is at the heart of many current harms to humans (in particular the violation of the human rights of marginalized or minority groups). It is also the central concern regarding a possible loss of control of very powerful AI in the future. In addition to the issue of misalignment, this group will consider how to avoid the exploitation of AI systems by bad actors (criminals, terrorist groups or rogue states) that threaten our collective security. The research proposed to explore possible solutions will draw on expertise in language models, causal and Bayesian probabilistic modeling, reinforcement learning, multi-agent AI, game theory, systems engineering, and computer security.

Researchers

Nora Boulahia-Cuppens
Polytechnique Montréal

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

R³AI: Research Regroupements to support the major advancement of AI knowledge

What is a Regroupement?

Why set up these Regroupements?

Regroupements objectives

R1 - AI and neuroscience

Researchers

R2 - Machine learning

Researchers

R3 - Natural Language Processing (NPL)

Researchers

R4 - Implementation science and responsible governance

Researchers

R5 - Ethics, EDI, and Indigenous engagement

Researchers

R6 - Molecules and materials

Researchers

R7- Environment

Researchers

R8 - Healthcare systems

Researchers

R9 - Supply chains and mobility systems

Researchers

R10 - AI safety and alignment

Researchers