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.