Portrait de Guillaume Dumas

Guillaume Dumas

Membre académique associé
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
Site web
Google Scholar
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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
Site web
Github
Google Scholar
Nadine Mohamed
Maîtrise recherche - UdeM
Rémy Ramadour
Visiteur de recherche indépendant - CHU Sainte Justine / Université de Montréal
Site web
Github
Mahta Ramezanian
Maîtrise recherche - UdeM
Superviseur⋅e principal⋅e :
Irina Rish
Pascal Tikeng Notsawo
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Guillaume Rabusseau
Site web
Github
Google Scholar

Publications

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
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
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
Long-term survival and functional outcomes of critically ill patients with hematologic malignancies: a Canadian multicenter prospective study
Laveena Munshi
Guillaume Dumas
Bram Rochwerg
Farah Shoukat
Michael Detsky
Dean A. Fergusson
Bruno Ferreyro
Paul Heffernan
Margaret Herridge
Sheldon Magder
Mark Minden
Rakesh Patel
Salman Qureshi
Aaron Schimmer
Santhosh Thyagu
Han Ting Wang
Sangeeta Mehta
2024-03-10
Intensive Care Medicine (publié)
doi.org
Predicting Grokking Long Before it Happens: A look into the loss landscape of models which grok
Tikeng Notsawo Pascal Junior
Pascal Notsawo
Hattie Zhou
Mohammad Pezeshki
Irina Rish
Guillaume Dumas
2024-03-03
ICLR.cc/2024/Workshop/ME-FoMo (poster)
doi.org
openreview.net
Distinct social behavior and inter-brain connectivity in Dyads with autistic individuals
Quentin Moreau
Florence Brun
Anaël Ayrolles
Jacqueline Nadel
Guillaume Dumas
Autism Spectrum Disorder (ASD) is defined by distinctive socio-cognitive behaviors that deviate from typical patterns. Notably, social imita… (voir plus)tion skills appear to be particularly impacted, manifesting early on in development. This paper compared the behavior and inter-brain dynamics of dyads made up of two typically developing (TD) participants with mixed dyads made up of ASD and TD participants during social imitation tasks. By combining kinematics and EEG-hyperscanning, we show that individuals with ASD exhibited a preference for the follower rather than the lead role in imitating scenarios. Moreover, the study revealed inter-brain synchrony differences, with low-alpha inter-brain synchrony differentiating control and mixed dyads. The study’s findings suggest the importance of studying interpersonal phenomena in dynamic and ecological settings and using hyperscanning methods to capture inter-brain dynamics during actual social interactions.
2024-03-02
Social Neuroscience (publié)
doi.org
The « jingle-jangle fallacy » of empathy: Delineating affective, cognitive and motor components of empathy from behavioral synchrony using a virtual agent
Julia Ayache
Guillaume Dumas
Alexander Sumich
D. Kuss
Darren Rhodes
Nadja Heym
2024-02-29
Personality and Individual Differences (publié)
doi.org