Portrait de Guillaume Dumas

Guillaume Dumas

Membre académique associé
guillaume.dumas@mila.quebec
Professeur agrégé, Université de Montréal, Département de psychiatrie et d’addictologie
Professeur adjoint, McGill University, Département de psychiatrie
Sujets de recherche
Apprentissage automatique médical
Apprentissage par renforcement
Apprentissage profond
Biologie computationnelle
Neurosciences computationnelles
Systèmes dynamiques
Théorie de l'apprentissage automatique
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Biographie

Guillaume Dumas est professeur agrégé de psychiatrie computationnelle à la Faculté de médecine de l'Université de Montréal et chercheur principal du laboratoire de psychiatrie de précision et de physiologie sociale du Centre de recherche du CHU Sainte-Justine. Il est titulaire de la chaire IVADO IA en santé mentale et chercheur-boursier junior 1 du Fonds de recherche du Québec - Santé (FRQS) dans le domaine de l’ IA en santé et de la santé numérique. En 2023, il a été retenu dans le cadre du Programme des chercheurs mondiaux CIFAR-Azrieli pour le programme de recherche Cerveau, esprit et conscience. Il a également été nommé parmi les Futurs leaders canadiens de la recherche sur le cerveau par la Fondation Brain Canada.

Il a auparavant été chercheur permanent en neurosciences et en biologie computationnelle à l'Institut Pasteur (Paris, France), ainsi que chercheur postdoctoral au Center for Complex Systems and Brain Sciences à l’Université Florida Atlantic (FAU), aux États-Unis. Il est titulaire d'un diplôme d'ingénieur en ingénierie avancée et informatique (École centrale Paris), de deux masters (physique théorique, Université Paris-Saclay; sciences cognitives, ENS/EHESS/Paris 5) et d'un doctorat en neurosciences cognitives (Sorbonne Université).

Ses recherches visent à combiner l’intelligence artificielle, les neurosciences cognitives et la médecine numérique à travers un programme interdisciplinaire suivant deux axes principaux :

- L’intelligence artificielle en santé mentale, par la création de nouveaux algorithmes pour étudier le développement de l'architecture cognitive humaine et pour fournir une médecine personnalisée en neuropsychiatrie grâce à des données allant du génome à celles des téléphones intelligents;

- Les neurosciences sociales en intelligence artificielle, par la traduction de la recherche fondamentale sur le cerveau et le formalisme des systèmes dynamiques en des modèles hybrides neurocomputationnels et d’apprentissage automatique (NeuroML) et de nouvelles architectures présentant des capacités d'apprentissage social (NeuroIA Sociale et IHM).

Étudiants actuels

Saskia Helbling
Postdoctorat - UdeM
saskia.helbling@mila.quebec
Site web
Github
Google Scholar
Nadine Mohamed
Maîtrise recherche - UdeM
nadine.mohamed@mila.quebec
Rémy Ramadour
Visiteur de recherche indépendant - CHU Sainte Justine / Université de Montréal
remy.ramadour.hsj@ssss.gouv.qc.ca
Site web
Github
Mahta Ramezanian
Maîtrise recherche - UdeM
Superviseur⋅e principal⋅e :
Irina Rish
mahta.ramezanian@mila.quebec
Pascal Tikeng Notsawo
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Guillaume Rabusseau
pascal.tikeng@mila.quebec
Site web
Github
Google Scholar

Publications

Long-term outcomes of critically ill patients with hematological malignancies: what is the impact of the coronavirus disease 2019 pandemic? Author's reply
Laveena Munshi
Guillaume Dumas
Sangeeta Mehta
2024-09-30
Intensive Care Medicine (publié)
doi.org
Diagnostic tests for infections in critically ill immunocompromised patients
Guillaume Dumas
Adrien Joseph
Lara Zafrani
2024-09-18
Intensive Care Medicine (publié)
doi.org
Non-invasive electroencephalography in awake cats: Feasibility and application to sensory processing in chronic pain
Aliénor Delsart
Aude Castel
Guillaume Dumas
Colombe Otis
Mathieu Lachance
Maude Barbeau-Grégoire
Bertrand Lussier
Franck Péron
Marc Hébert
Nicolas Lapointe
Maxim Moreau
Johanne Martel-Pelletier
Jean-Pierre Pelletier
Eric Troncy
2024-08-21
Journal of Neuroscience Methods (publié)
doi.org
Oxygen thresholds in critically ill patients: need for personalized targets. Author's reply.
Guillaume Dumas
Laveena Munshi
2024-08-11
Intensive Care Medicine (publié)
doi.org
Challenges in multi-task learning for fMRI-based diagnosis: Benefits for psychiatric conditions and CNVs would likely require thousands of patients
Annabelle Harvey
Clara A. Moreau
Kuldeep Kumar
Guillaume Huguet
Sebastian G.W. Urchs
Hanad Sharmarke
Khadije Jizi
Charles-Olivier Martin
Nadine Younis
Petra Tamer
Jean-Louis Martineau
Pierre Orban
Ana Isabel Silva
Jeremy Hall
Marianne B.M. van den Bree
Michael J. Owen
David E.J. Linden
Sarah Lippé
Carrie E. Bearden
Guillaume Dumas … (voir 2 de plus)
Sébastien Jacquemont
Pierre Bellec
There is a growing interest in using machine learning (ML) models to perform automatic diagnosis of psychiatric conditions; however, general… (voir plus)ising the prediction of ML models to completely independent data can lead to sharp decrease in performance. Patients with different psychiatric diagnoses have traditionally been studied independently, yet there is a growing recognition of neuroimaging signatures shared across them as well as rare genetic copy number variants (CNVs). In this work, we assess the potential of multi-task learning (MTL) to improve accuracy by characterising multiple related conditions with a single model, making use of information shared across diagnostic categories and exposing the model to a larger and more diverse dataset. As a proof of concept, we first established the efficacy of MTL in a context where there is clearly information shared across tasks: the same target (age or sex) is predicted at different sites of data collection in a large functional magnetic resonance imaging (fMRI) dataset compiled from multiple studies. MTL generally led to substantial gains relative to independent prediction at each site. Performing scaling experiments on the UK Biobank, we observed that performance was highly dependent on sample size: for large sample sizes (N > 6000) sex prediction was better using MTL across three sites (N = K per site) than prediction at a single site (N = 3K), but for small samples (N 500) MTL was actually detrimental for age prediction. We then used established machine-learning methods to benchmark the diagnostic accuracy of each of the 7 CNVs (N = 19–103) and 4 psychiatric conditions (N = 44–472) independently, replicating the accuracy previously reported in the literature on psychiatric conditions. We observed that MTL hurt performance when applied across the full set of diagnoses, and complementary analyses failed to identify pairs of conditions which would benefit from MTL. Taken together, our results show that if a successful multi-task diagnostic model of psychiatric conditions were to be developed with resting-state fMRI, it would likely require datasets with thousands of patients across different diagnoses.
2024-07-25
Imaging Neuroscience (publié)
doi.org
Scalable Approaches for a Theory of Many Minds
Maximilian Puelma Touzel
Amin Memarian
Matthew D Riemer
Andrei Mircea
Andrew Robert Williams
Elin Ahlstrand
Lucas Lehnert
Rupali Bhati
Guillaume Dumas
Irina Rish
A major challenge as we move towards building agents for real-world problems, which could involve a massive number of human and/or machine a… (voir plus)gents, is that we must learn to reason about the behavior of these many other agents. In this paper, we consider the problem of scaling a predictive Theory of Mind (ToM) model to a very large number of interacting agents with a fixed computational budget. Motivated by the limited diversity of agent types, existing approaches to scalable TOM learn versatile single-agent representations for quickly adapting to new agents encountered sequentially. We consider the more general setting that many agents are observed in parallel and formulate the corresponding Theory of Many Minds (ToMM) problem of estimating the joint policy. We frame the scaling behavior of solutions in terms of parameter sharing schemes and in particular propose two parameter-free architectural features that endow models with the ability to exploit action correlations: encoding a multi-agent context, and decoding through an abstracted joint action space. The increased predictive capabilities that have come with foundation models have made it easier to imagine the possibility of using these models to make simulations that imitate the behavior of many agents within complex real-world systems. Being able to perform these simulations in a general-purpose way would not only help make more capable agents, it also would be a very useful capability for applications in social science, political science, and economics.
2024-06-17
ICML.cc/2024/Workshop/Agentic_Markets (poster)
openreview.net
openreview.net
Lost in Translation: The Algorithmic Gap Between LMs and the Brain
Tosato Tommaso
Tikeng Notsawo Pascal Junior
Helbling Saskia
Irina Rish
Guillaume Dumas
Language Models (LMs) have achieved impressive performance on various linguistic tasks, but their relationship to human language processing … (voir plus)in the brain remains unclear. This paper examines the gaps and overlaps between LMs and the brain at different levels of analysis, emphasizing the importance of looking beyond input-output behavior to examine and compare the internal processes of these systems. We discuss how insights from neuroscience, such as sparsity, modularity, internal states, and interactive learning, can inform the development of more biologically plausible language models. Furthermore, we explore the role of scaling laws in bridging the gap between LMs and human cognition, highlighting the need for efficiency constraints analogous to those in biological systems. By developing LMs that more closely mimic brain function, we aim to advance both artificial intelligence and our understanding of human cognition.
2024-06-16
ICML.cc/2024/Workshop/LLMs_and_Cognition (poster)
openreview.net
openreview.net
Bringing together multimodal and multilevel approaches to study the emergence of social bonds between children and improve social AI
Julie Bonnaire
Guillaume Dumas
Justine Cassell
This protocol paper outlines an innovative multimodal and multilevel approach to studying the emergence and evolution of how children build … (voir plus)social bonds with their peers, and its potential application to improving social artificial intelligence (AI). We detail a unique hyperscanning experimental framework utilizing functional near-infrared spectroscopy (fNIRS) to observe inter-brain synchrony in child dyads during collaborative tasks and social interactions. Our proposed longitudinal study spans middle childhood, aiming to capture the dynamic development of social connections and cognitive engagement in naturalistic settings. To do so we bring together four kinds of data: the multimodal conversational behaviors that dyads of children engage in, evidence of their state of interpersonal rapport, collaborative performance on educational tasks, and inter-brain synchrony. Preliminary pilot data provide foundational support for our approach, indicating promising directions for identifying neural patterns associated with productive social interactions. The planned research will explore the neural correlates of social bond formation, informing the creation of a virtual peer learning partner in the field of Social Neuroergonomics. This protocol promises significant contributions to understanding the neural basis of social connectivity in children, while also offering a blueprint for designing empathetic and effective social AI tools, particularly for educational contexts.
2024-05-16
Frontiers in Neuroergonomics (publié)
doi.org
WOODS: Benchmarks for Out-of-Distribution Generalization in Time Series
Jean-christophe Gagnon-audet
Kartik Ahuja
Mohammad-Javad Darvishi-Bayazi
Pooneh Mousavi
Guillaume Dumas
Irina Rish
2024-05-06
International Conference on Learning Representations (Accept (Poster))
doi.org
openreview.net
295. Rare Variant Genetic Architecture of the Human Cortical MRI Phenotypes in General Population
Kuldeep Kumar
Sayeh Kazem
Zhijie Liao
Jakub Kopal
Guillaume Huguet
Thomas Renne
Martineau Jean‐Louis
Zhe Xie
Zohra Saci
Laura Almasy
David C. Glahn
Tomáš Paus
Guillaume Dumas
Carrie E. Bearden
Paul M. Thompson
Richard A. I. Bethlehem
Varun Warrier
Sébastien Jacquemont
2024-04-28
Biological Psychiatry (inconnu)
doi.org
Association between arterial oxygen and mortality across critically ill patients with hematologic malignancies: results from an international collaborative network
Guillaume Dumas
Idunn S. Morris
Tamishta Hensman
Alexandre Demoule
Achille Kouatchet
Virginie Lemiale
Djamel Mokart
Frédéric Pène
Elie Azoulay
Laveena Munshi
Laurent François Dominique Naike Fabrice Emmanuel Yves Mic Argaud Barbier Benoit Bigé Bruneel Canet Cohen Dar
Laurent Argaud
François Barbier
Dominique Benoit
Naike Bigé
Fabrice Bruneel
Emmanuel Canet
Yves Cohen
Michael Darmon
Didier Gruson … (voir 31 de plus)
Kada Klouche
Loay Kontar
Alexandre Lautrette
Christine Lebert
Guillaume Louis
Julien Mayaux
Anne-Pascale Meert
Anne-Sophie Moreau
Martine Nyunga
Vincent Peigne
Pierre Perez
Jean Herlé Raphalen
Carole Schwebel
Jean-Marie Tonnelier
Florent Wallet
Lara Zafrani
Bram Rochwerg
Farah Shoukat
Dean Fergusson
Bruno Ferreyro
Paul Heffernan
Margaret Herridge
Sheldon Magder
Mark Minden
Rakesh Patel
Salman Qureshi
Aaron Schimmer
Santhosh Thyagu
Han Ting Wang
Sangeeta Mehta
Sean M. Bagshaw
2024-04-09
Intensive Care Medicine (publié)
doi.org
On quasi-homomorphism rigidity for lattices in simple algebraic groups
Guillaume Dumas
2024-03-24
ArXiv (prépublication)
arxiv.org
arxiv.org