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
LinkedIn
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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

Genetic correlates of phenotypic heterogeneity in autism
Varun Warrier
Xinhe Zhang
Patrick Reed
Alexandra Havdahl
Tyler M. Moore
Freddy Cliquet
Claire Leblond
Thomas Rolland
Anders Rosengren
Antonia San Jose Hannah Daisy Jessica Jessica Claire Bethany Eva Tony Declan Rosemary Jack Jessica Nicola Meng-Chuan Gwilym Amber Emily Hisham Julia Sara Ambrosino Sarai Yvonne Tabitha Miriam Alyssia Iris Maarten Anna Ver Loren Nico Sarah Larry Carsten Annika Daniel Ineke Yvette Maartje Elzbieta Elodie Kristiina Rouslan Guillaume Yang-Min Thomas Caceres
Antonia San Jose Hannah Daisy Jessica Jessica Claire Betha Caceres Hayward Crawley Faulkner Sabet Ellis Oakle
Antonia San José Cáceres
Hannah Hayward
Daisy Crawley
Jessica Faulkner
Jessica Sabet
Claire Ellis
Beth Oakley
Eva Loth
Tony Charman … (voir 67 de plus)
Declan Murphy
Rosemary Holt
Jack Waldman
Jessica Upadhyay
Nicola Gunby
Meng-Chuan Lai
Gwilym Renouf
Amber N. V. Ruigrok
Emily Taylor
Hisham Ziauddeen
Julia Deakin
Sara Ambrosino di Bruttopilo
Sarai van Dijk
Yvonne Rijks
Tabitha Koops
Miriam Douma
Alyssia Spaan
Iris Selten
Maarten Steffers
Anna Ver Loren van Themaat
Nico Bast
Sarah Baumeister
Larry O’Dwyer
Carsten Bours
Annika Rausch
Daniel von Rhein
Ineke Cornelissen
Yvette de Bruin
Maartje Graauwmans
Elzbieta Kostrzewa
Elodie Cauvet
Kristiina Tammimies
Rouslan Sitnikow
Guillaume Dumas
Yang-Min Kim
Thomas Bourgeron
David M. Jonas Thomas Preben Bo Ole Merete Hougaard
David M. Hougaard
Jonas Bybjerg-Grauholm
Thomas Werge
Preben Bo Mortensen
Ole Mors
Merete Nordentoft
Dwaipayan Armandina Carrie Isabelle Tracey Paula Alex Graham J. Alexander E. P. Lidia V. Tal Madeline A. Deepak P. Jonathan Adhya
Dwaipayan Armandina Carrie Isabelle Tracey Paula Alex Graham Adhya Alamanza Allison Garvey Parsons Smith Tsompa
Dwaipayan Adhya
Armandina Alamanza
Carrie Allison
Isabelle Garvey
Tracey Parsons
Paula Smith
Alex Tsompanidis
Graham J. Burton
Alexander E. P. Heazell
Lidia V. Gabis
Tal Biron-Shental
Madeline A. Lancaster
Deepak P. Srivastava
Jonathan Mill
David H. Rowitch
Matthew E. Hurles
Daniel H. Geschwind
Anders D. Børglum
Elise B. Robinson
Jakob Grove
Hilary C. Martin
Simon Baron-Cohen
2022-06-01
Nature Genetics (publié)
doi.org
From inter‐brain connectivity to inter‐personal psychiatry
Guillaume Dumas
2022-05-06
World Psychiatry (publié)
doi.org
Social Neuro AI: Social Interaction as the "Dark Matter" of AI
Samuele Bolotta
Guillaume Dumas
This article introduces a three-axis framework indicating how AI can be informed by biological examples of social learning mechanisms. We ar… (voir plus)gue that the complex human cognitive architecture owes a large portion of its expressive power to its ability to engage in social and cultural learning. However, the field of AI has mostly embraced a solipsistic perspective on intelligence. We thus argue that social interactions not only are largely unexplored in this field but also are an essential element of advanced cognitive ability, and therefore constitute metaphorically the dark matter of AI. In the first section, we discuss how social learning plays a key role in the development of intelligence. We do so by discussing social and cultural learning theories and empirical findings from social neuroscience. Then, we discuss three lines of research that fall under the umbrella of Social NeuroAI and can contribute to developing socially intelligent embodied agents in complex environments. First, neuroscientific theories of cognitive architecture, such as the global workspace theory and the attention schema theory, can enhance biological plausibility and help us understand how we could bridge individual and social theories of intelligence. Second, intelligence occurs in time as opposed to over time, and this is naturally incorporated by dynamical systems. Third, embodiment has been demonstrated to provide more sophisticated array of communicative signals. To conclude, we discuss the example of active inference, which offers powerful insights for developing agents that possess biological realism, can self-organize in time, and are socially embodied.
2022-05-05
Frontiers in Computer Science (publié)
doi.org
arxiv.org
P397. Genomic Deletions and Duplications Show Mirror Effects on Cognitive Ability According to Spatial Patterns of Gene Expression in the Human Brain
Kuldeep Kumar
Sayeh Kazem
Guillaume Huguet
Elise Douard
Zohra Saci
Laura Almasy
David Glahn
Guillaume Dumas
Sébastien Jacquemont
2022-04-30
Biological Psychiatry (publié)
doi.org
A neurodynamic model of inter-brain coupling in the gamma band
Quentin Moreau
Lena Adel
Caitriona Douglas
Ranjbaran Ghazaleh
Guillaume Dumas
2022-04-10
bioRxiv (prépublication)
doi.org
Neurobiological Correlates of Change in Adaptive Behavior in Autism.
Charlotte M. Pretzsch
Tim Schäfer
Michael V. Lombardo
Varun Warrier
Caroline Mann
Anke Bletsch
Chris H. Chatham
Dorothea L. Floris
Julian Tillmann
Afsheen Yousaf
Emily J. H. Jones
Tony Charman
Sara Ambrosino
Thomas Bourgeron
Guillaume Dumas
Eva Loth
Beth Oakley
Jan K. Buitelaar
Freddy Cliquet
Claire Leblond … (voir 7 de plus)
Simon Baron-Cohen
Christian Beckmann
Tobias Banaschewski
Sarah Durston
Christine M. Freitag
Declan Murphy
Christine Ecker
2022-03-24
American Journal of Psychiatry (publié)
doi.org
WOODS: Benchmarks for Out-of-Distribution Generalization in Time Series Tasks
Jean-christophe Gagnon-audet
Kartik Ahuja
Mohammad Javad Darvishi Bayazi
Guillaume Dumas
Irina Rish
2022-03-17
ArXiv (prépublication)
doi.org
arxiv.org
Interindividual Differences in Cortical Thickness and Their Genomic Underpinnings in Autism Spectrum Disorder.
Christine Ecker
Charlotte M. Pretzsch
Anke Bletsch
Caroline Mann
Tim Schaefer
Sara Ambrosino
Julian Tillmann
Afsheen Yousaf
Andreas Chiocchetti
Michael V. Lombardo
Varun Warrier
Nico Bast
Carolin Moessnang
Sarah Baumeister
Flavio Dell’Acqua
Dorothea L. Floris
Mariam Zabihi
Andre Marquand
Freddy Cliquet
Claire Leblond … (voir 19 de plus)
Clara A. Moreau
Nick Puts
Tobias Banaschewski
Emily J. H. Jones
Luke Mason
Sven Bölte
Andreas Meyer-Lindenberg
Antonio Persico
Sarah Durston
Simon Baron-Cohen
Will Spooren
Eva Loth
Christine M. Freitag
Tony Charman
Guillaume Dumas
Thomas Bourgeron
Christian Beckmann
Jan K. Buitelaar
Declan Murphy
2022-02-28
American Journal of Psychiatry (publié)
doi.org
Multilevel development of cognitive abilities in an artificial neural network
Konstantin Volzhenin
Jean-Pierre Changeux
Guillaume Dumas
Multiple biological mechanisms support the unique ability of the brain to develop complex cognitive abilities. Nevertheless, it remains uncl… (voir plus)ear which mechanisms are necessary and sufficient. We propose a neurocomputational model of the developing brain spanning sensorimotor, cognitive, and conscious levels. The model solves three tasks of increasing complexity: from visual recognition to cognitive manipulation and maintenance of conscious percepts. Results highlight two fundamental mechanisms for the multilevel development of cognitive abilities in biological neural networks: 1) synaptic epigenesis, with Hebbian learning at the local scale and reinforcement learning at the global scale; and 2) self-organized dynamics, through spontaneous activity and balanced excitatory/inhibitory ratio of neurons. We emphasize how these core features of human intelligence could guide future development in artificial intelligence.
2022-01-24
bioRxiv (prépublication)
doi.org
Analysis of the Human Pineal Proteome by Mass Spectrometry
Mariette Matondo
Guillaume Dumas
Erik Maronde
2021-12-31
Methods in molecular biology (publié)
doi.org
Generative Models of Brain Dynamics -- A review
Mahta Ramezanian Panahi
Germán Abrevaya
Jean-christophe Gagnon-audet
Vikram Voleti
Irina Rish
Guillaume Dumas
The principled design and discovery of biologically- and physically-informed models of neuronal dynamics has been advancing since the mid-tw… (voir plus)entieth century. Recent developments in artificial intelligence (AI) have accelerated this progress. This review article gives a high-level overview of the approaches across different scales of organization and levels of abstraction. The studies covered in this paper include fundamental models in computational neuroscience, nonlinear dynamics, data-driven methods, as well as emergent practices. While not all of these models span the intersection of neuroscience, AI, and system dynamics, all of them do or can work in tandem as generative models, which, as we argue, provide superior properties for the analysis of neuroscientific data. We discuss the limitations and unique dynamical traits of brain data and the complementary need for hypothesis- and data-driven modeling. By way of conclusion, we present several hybrid generative models from recent literature in scientific machine learning, which can be efficiently deployed to yield interpretable models of neural dynamics.
2021-12-21
ArXiv (prépublication)
arxiv.org
arxiv.org
Preference for biological motion is reduced in ASD: implications for clinical trials and the search for biomarkers
Luke Mason
F. Shic
T. Falck-Ytter
Bhismadev Chakrabarti
Tony Charman
Eva Loth
Julian Tillmann
Tobias Banaschewski
Simon Baron-Cohen
Sven Bölte
J. Buitelaar
Sarah Durston
Bob Oranje
Antonio Persico
C. Beckmann
Thomas Bougeron
Flavio Dell’Acqua
Christine Ecker
Carolin Moessnang
D. Murphy … (voir 49 de plus)
M. H. Johnson
Emily J. H. Jones
Jumana Sara Sarah Carsten Michael Daniel Claudia Yvette Chris Ineke Daisy Guillaume Jessica Vincent Pilar David Lindsay Joerg Rosemary Meng-Chuan Xavier Liogier Michael V. David J. René Andre Maarten Andreas Nico Bethany Laurence Marianne Gahan Barbara Amber Jessica Roberto Antonia San José Emily Will Roberto Heike Jack Steve C. R. Caroline Marcel P. Ahmad
Jumana Sara Sarah Carsten Michael Daniel Claudia Yvette C Ahmad Ambrosino Baumeister Bours Brammer Brandeis
Jumana Ahmad
Sara Ambrosino
Sarah Baumeister
Carsten Bours
Michael Brammer
Daniel Brandeis
Claudia Brogna
Yvette de Bruijn
Christopher H. Chatham
Ineke Cornelissen
Daisy Crawley
Guillaume Dumas
Jessica Faulkner
Vincent Frouin
Pilar Garcés
David Goyard
Lindsay Ham
Joerg F. Hipp
Rosemary Holt
Meng-Chuan Lai
Xavier Liogier D’ardhuy
Michael V. Lombardo
David J. Lythgoe
René Mandl
Andre Marquand
Maarten Mennes
Andreas Meyer-Lindenberg
Nico Bast
Beth Oakley
Larry O’Dwyer
Marianne Oldehinkel
Gahan Pandina
Barbara Ruggeri
Amber N. V. Ruigrok
Jessica Sabet
Roberto Sacco
Antonia San José Cáceres
Emily Simonoff
Will Spooren
Roberto Toro
Heike Tost
Jack Waldman
Steve C. R. Williams
Caroline Wooldridge
Marcel P. Zwiers
2021-12-14
Molecular Autism (publié)
doi.org