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

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

Maîtrise recherche - UdeM
Doctorat - UdeM
Maîtrise recherche - UdeM
Superviseur⋅e principal⋅e :
Doctorat - UdeM
Superviseur⋅e principal⋅e :

Publications

Diagnosis and management of autoimmune diseases in the ICU
Yaseen M. Arabi
Raquel Bartz
Otavio Ranzani
Franziska Scheibe
Michaël Darmon
Julie Helms
From physics to sentience: Deciphering the semantics of the free-energy principle and evaluating its claims: Comment on "Path integrals, particular kinds, and strange things" by Karl Friston et al.
Zahra Sheikhbahaee
Adam Safron
Casper Hesp
Exploring the multidimensional nature of repetitive and restricted behaviors and interests (RRBI) in autism: neuroanatomical correlates and clinical implications
Aline Lefebvre
Nicolas Traut
Amandine Pedoux
Anna Maruani
Anita Beggiato
Monique Elmaleh
David Germanaud
Anouck Amestoy
Myriam Ly‐Le Moal
Christopher H. Chatham
Lorraine Murtagh
Manuel Bouvard
Marianne Alisson
Marion Leboyer
Thomas Bourgeron
Roberto Toro
Clara A. Moreau
Richard Delorme
Exploring the multidimensional nature of repetitive and restricted behaviors and interests (RRBI) in autism: neuroanatomical correlates and clinical implications
Aline Lefebvre
Nicolas Traut
Amandine Pedoux
Anna Maruani
Anita Beggiato
Monique Elmaleh
David Germanaud
Anouck Amestoy
Myriam Ly‐Le Moal
Christopher H. Chatham
Lorraine Murtagh
Manuel Bouvard
Marianne Alisson
Marion Leboyer
Thomas Bourgeron
Roberto Toro
Clara A. Moreau
Richard Delorme
Exploring the multidimensional nature of repetitive and restricted behaviors and interests (RRBI) in autism: neuroanatomical correlates and clinical implications
Aline Lefebvre
Nicolas Traut
Amandine Pedoux
Anna Maruani
Anita Beggiato
Monique Elmaleh
David Germanaud
Anouck Amestoy
Myriam Ly‐Le Moal
Christopher H. Chatham
Lorraine Murtagh
Manuel Bouvard
Marianne Alisson
Marion Leboyer
Thomas Bourgeron
Roberto Toro
Clara A. Moreau
Richard Delorme
From physics to sentience: Deciphering the semantics of the free-energy principle and evaluating its claims: Comment on "Path integrals, particular kinds, and strange things" by Karl Friston et al.
Zahra Sheikhbahaee
Adam Safron
Casper Hesp
Interoceptive technologies for psychiatric interventions: From diagnosis to clinical applications
Felix Schoeller
Adam Haar Horowitz
Abhinandan Jain
Pattie Maes
Nicco Reggente
Leonardo Christov-Moore
Giovanni Pezzulo
Laura Barca
Micah Allen
Roy Salomon
Mark Miller
Daniele Di Lernia
Giuseppe Riva
Manos Tsakiris
Moussa A. Chalah
Arno Klein
Ben Zhang
Teresa Garcia
Ursula Pollack
Marion Trousselard … (voir 4 de plus)
Charles Verdonk
Vladimir Adrien
Karl J. Friston
Interoceptive technologies for psychiatric interventions: From diagnosis to clinical applications
Felix Schoeller
Adam Haar Horowitz
Abhinandan Jain
Pattie Maes
Nicco Reggente
Leonardo Christov-Moore
Giovanni Pezzulo
Laura Barca
Micah Allen
Roy Salomon
Mark Miller
Daniele Di Lernia
Giuseppe Riva
Manos Tsakiris
Moussa A. Chalah
Arno Klein
Ben Zhang
Teresa Garcia
Ursula Pollack
Marion Trousselard … (voir 4 de plus)
Charles Verdonk
Vladimir Adrien
Karl J. Friston
Interoceptive technologies for psychiatric interventions: From diagnosis to clinical applications
Felix Schoeller
Adam Haar Horowitz
Abhinandan Jain
Pattie Maes
Nicco Reggente
Leonardo Christov-Moore
Giovanni Pezzulo
Laura Barca
Micah Allen
Roy Salomon
Mark Miller
Daniele Di Lernia
Giuseppe Riva
Manos Tsakiris
Moussa A. Chalah
Arno Klein
Ben Zhang
Teresa Garcia
Ursula Pollack
Marion Trousselard … (voir 4 de plus)
Charles Verdonk
Vladimir Adrien
Karl J. Friston
Typology of ICU-Healthcare Providers Who Delayed or Declined COVID-19 Vaccination
Elie Azoulay
Frédéric Pochard
Nancy Kentish-Barnes
OBJECTIVES: To assess COVID-19 vaccination rates in ICU-healthcare providers (HCPs) in France and to identify the typology of those who dela… (voir plus)yed or declined vaccination. DESIGN: Cross-sectional study. SETTING: Twenty-one ICUs in France. SUBJECTS: Members of the nursing and medical staff and other allied professionals. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Six hundred ninety-six of the 950 respondents (73.3%) had undergone a full vaccination schedule. Other HCPs either declined vaccination (n = 112) or delayed vaccination until it became mandatory (n = 142). Factors independently associated with full vaccination were age older than 50 years (odds ratio, 0.25 [95% CI, 0.12–0.51]), more than 5 years of ICU experience (0.66 [0.47–0.93]), increasing working time during the surge (0.94 [0.88–1.00]), and spending time with the family (0.92 [0.85–0.99]). Conversely, being a nurse (1.94 [1.25–2.99]) or a nurse assistant (2.77 [1.62–4.73]), and feeling not supported by hospital and ICU directors (1.49 [1.01–2.20]) was independently associated with not being vaccinated. CONCLUSIONS: These results are important to take into account to better implement vaccination strategies in HCPs for existing or future pandemics.
Attention Schema in Neural Agents
Dianbo Liu
Samuele Bolotta
Mike He Zhu
Zahra Sheikhbahaee
Attention has become a common ingredient in deep learning architectures. It adds a dynamical selection of information on top of the static s… (voir plus)election of information supported by weights. In the same way, we can imagine a higher-order informational filter built on top of attention: an Attention Schema (AS), namely, a descriptive and predictive model of attention. In cognitive neuroscience, Attention Schema Theory (AST) supports this idea of distinguishing attention from AS. A strong prediction of this theory is that an agent can use its own AS to also infer the states of other agents' attention and consequently enhance coordination with other agents. As such, multi-agent reinforcement learning would be an ideal setting to experimentally test the validity of AST. We explore different ways in which attention and AS interact with each other. Our preliminary results indicate that agents that implement the AS as a recurrent internal control achieve the best performance. In general, these exploratory experiments suggest that equipping artificial agents with a model of attention can enhance their social intelligence.
Prognosis of critically ill immunocompromised patients with virus-detected acute respiratory failure
Maxime Bertrand
Virginie Lemiale
Emmanuel Canet
François Barbier
Achille Kouatchet
Alexandre Demoule
Kada Klouche
Anne-Sophie Moreau
Laurent Argaud
Florent Wallet
Jean Herlé Raphalen
Djamel Mokart
Fabrice Bruneel
Frédéric Pène
Elie Azoulay