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

https://www.neuromodec.org/journal/4/2/NzBlvmDpUYspQQbvI4B Online Transcranial Random Noise Stimulation of the Right Temporoparietal Junction Acutely Modulates Human-Machine Social Interactions
Vincent Chamberland
Quentin Moreau
Lisane Moses
Gabriela Milanova
Is sharing always caring? Entropy, boundaries and the plurality of psychotherapeutic process.
Lena Adel
Ana Gómez-Carrillo
Jonas Mago
Michael Lifshitz
Effects of gene dosage on cognitive ability: A function-based association study across brain and non-brain processes
Thomas Renne
Cécile Poulain
Alma Dubuc
Kuldeep Kumar
Sayeh Kazem
Worrawat Engchuan
Omar Shanta
Elise Douard
Catherine Proulx
Martineau Jean-Louis
Zohra Saci
Josephine Mollon
Laura Schultz
Emma E M Knowles
Simon R. Cox
David Porteous
Gail Davies
Paul Redmond
Sarah E. Harris … (voir 10 de plus)
Gunter Schumann
Aurélie Labbe
Zdenka Pausova
Tomas Paus
Stephen W Scherer
Jonathan Sebat
Laura Almasy
David C. Glahn
Sébastien Jacquemont
Decomposing the Brain in Autism: Linking Behavioral Domains to Neuroanatomical Variation and Genomic Underpinnings.
Hanna Seelemeyer
Caroline Gurr
Johanna Leyhausen
Lisa M. Berg
Charlotte M. Pretzsch
Tim Schäfer
Bassem Hermila
Christine M. Freitag
Eva Loth
Beth Oakley
Luke Mason
Jan K. Buitelaar
Christian Beckmann
Dorothea L. Floris
Tony Charman
Tobias Banaschewski
Thomas Bourgeron
Jumana Ahmad
Sara Ambrosino
Bonnie Auyeung … (voir 56 de plus)
Simon Baron-Cohen
Sarah Baumeister
Sven Bölte
Carsten Bours
Michael Brammer
Daniel Brandeis
Claudia Brogna
Yvette de Bruijn
Bhismadev Chakrabarti
Ineke Cornelissen
Daisy Crawley
Flavio Dell’Acqua
Sarah Durston
Christine Ecker
Jessica Faulkner
Vincent Frouin
Pilar Garcés
David Goyard
Lindsay Ham
Hannah Hayward
Joerg F. Hipp
Rosemary Holt
Mark Johnson
Emily J. H. Jones
Prantik Kundu
Meng-Chuan Lai
Xavier Liogier D’ardhuy
Michael V. Lombardo
David J. Lythgoe
René Mandl
Andre Marquand
Maarten Mennes
Andreas Meyer-Lindenberg
Carolin Moessnang
Nico Bast
Laurence O’Dwyer
Marianne Oldehinkel
Bob Oranje
Gahan Pandina
Antonio Persico
Barbara Ruggeri
Amber N. V. Ruigrok
Jessica Sabet
Roberto Sacco
Antonia San José Cáceres
Emily Simonoff
Will Spooren
Julian Tillmann
Roberto Toro
Heike Tost
Jack Waldman
Steve C.R. Williams
Caroline Wooldridge
Marcel P. Zwiers
Declan Murphy
Decomposing the Brain in Autism: Linking Behavioral Domains to Neuroanatomical Variation and Genomic Underpinnings.
Hanna Seelemeyer
Caroline Gurr
Johanna Leyhausen
Lisa M. Berg
Charlotte M. Pretzsch
Tim Schäfer
Bassem Hermila
Christine M. Freitag
Eva Loth
Beth Oakley
Luke Mason
Jan K. Buitelaar
Christian Beckmann
Dorothea L. Floris
Tony Charman
Tobias Banaschewski
Emily Jones
Thomas Bourgeron
Jumana Ahmad
Sara Ambrosino … (voir 58 de plus)
Bonnie Auyeung
Simon Baron-Cohen
Sarah Baumeister
Sven Bölte
Carsten Bours
Michael Brammer
Daniel Brandeis
Claudia Brogna
Yvette de Bruijn
Bhismadev Chakrabarti
Ineke Cornelissen
Daisy Crawley
Flavio Dell’Acqua
Sarah Durston
Christine Ecker
Jessica Faulkner
Vincent Frouin
Pilar Garcés
David Goyard
Lindsay Ham
Hannah Hayward
Joerg F. Hipp
Rosemary Holt
Mark Johnson
Emily J. H. Jones
Prantik Kundu
Meng-Chuan Lai
Xavier Liogier D’ardhuy
Michael V. Lombardo
David J. Lythgoe
René Mandl
Andre Marquand
Maarten Mennes
Andreas Meyer-Lindenberg
Carolin Moessnang
Nico Bast
Laurence O’Dwyer
Marianne Oldehinkel
Bob Oranje
Gahan Pandina
Antonio Persico
Barbara Ruggeri
Declan G.M. Murphy
Amber N. V. Ruigrok
Jessica Sabet
Roberto Sacco
Antonia San José Cáceres
Emily Simonoff
Will Spooren
Julian Tillmann
Roberto Toro
Heike Tost
Jack Waldman
Steve C. R. Williams
Caroline Wooldridge
Marcel P. Zwiers
Declan Murphy
Decomposing the Brain in Autism: Linking Behavioral Domains to Neuroanatomical Variation and Genomic Underpinnings.
Hanna Seelemeyer
Caroline Gurr
Johanna Leyhausen
Lisa M. Berg
Charlotte M. Pretzsch
Tim Schäfer
Bassem Hermila
Christine M. Freitag
Eva Loth
Beth Oakley
Luke Mason
Jan K. Buitelaar
Christian Beckmann
Dorothea L. Floris
Tony Charman
Tobias Banaschewski
Thomas Bourgeron
Jumana Ahmad
Sara Ambrosino
Bonnie Auyeung … (voir 56 de plus)
Simon Baron-Cohen
Sarah Baumeister
Sven Bölte
Carsten Bours
Michael Brammer
Daniel Brandeis
Claudia Brogna
Yvette de Bruijn
Bhismadev Chakrabarti
Ineke Cornelissen
Daisy Crawley
Flavio Dell’Acqua
Sarah Durston
Christine Ecker
Jessica Faulkner
Vincent Frouin
Pilar Garcés
David Goyard
Lindsay Ham
Hannah Hayward
Joerg F. Hipp
Rosemary Holt
Mark Johnson
Emily J. H. Jones
Prantik Kundu
Meng-Chuan Lai
Xavier Liogier D’ardhuy
Michael V. Lombardo
David J. Lythgoe
René Mandl
Andre Marquand
Maarten Mennes
Andreas Meyer-Lindenberg
Carolin Moessnang
Nico Bast
Laurence O’Dwyer
Marianne Oldehinkel
Bob Oranje
Gahan Pandina
Antonio Persico
Barbara Ruggeri
Amber N. V. Ruigrok
Jessica Sabet
Roberto Sacco
Antonia San José Cáceres
Emily Simonoff
Will Spooren
Julian Tillmann
Roberto Toro
Heike Tost
Jack Waldman
Steve C. R. Williams
Caroline Wooldridge
Marcel P. Zwiers
Declan Murphy
Introducing Brain Foundation Models
Hena Ghonia
Bruno Aristimunha
Md Rifat Arefin
Sylvain Chevallier
Brain function represents one of the most complex systems driving our world. Decoding its signals poses significant challenges, particularly… (voir plus) due to the limited availability of data and the high cost of recordings. The existence of large hospital datasets and laboratory collections partially mitigates this issue. However, the lack of standardized recording protocols, varying numbers of channels, diverse setups, scenarios, and recording devices further complicate the task. This work addresses these challenges by introducing the Brain Foundation Model (BFM), a suite of open-source models trained on brain signals. These models serve as foundational tools for various types of time-series neuroimaging tasks. This work presents the first model of the BFM series, which is trained on electroencephalogram signal data. Our results demonstrate that BFM-EEG can generate signals more accurately than other models. Upon acceptance, we will release the model weights and pipeline.
LLMs and Personalities: Inconsistencies Across Scales
This study investigates the application of human psychometric assessments to large language models (LLMs) to examine their consistency and m… (voir plus)alleability in exhibiting personality traits. We administered the Big Five Inventory (BFI) and the Eysenck Personality Questionnaire-Revised (EPQ-R) to various LLMs across different model sizes and persona prompts. Our results reveal substantial variability in responses due to question order shuffling, challenging the notion of a stable LLM "personality." Larger models demonstrated more consistent responses, while persona prompts significantly influenced trait scores. Notably, the assistant persona led to more predictable scaling, with larger models exhibiting more socially desirable and less variable traits. In contrast, non-conventional personas displayed unpredictable behaviors, sometimes extending personality trait scores beyond the typical human range. These findings have important implications for understanding LLM behavior under different conditions and reflect on the consequences of scaling.
LLMs and Personalities: Inconsistencies Across Scales
This study investigates the application of human psychometric assessments to large language models (LLMs) to examine their consistency and m… (voir plus)alleability in exhibiting personality traits. We administered the Big Five Inventory (BFI) and the Eysenck Personality Questionnaire-Revised (EPQ-R) to various LLMs across different model sizes and persona prompts. Our results reveal substantial variability in responses due to question order shuffling, challenging the notion of a stable LLM "personality." Larger models demonstrated more consistent responses, while persona prompts significantly influenced trait scores. Notably, the assistant persona led to more predictable scaling, with larger models exhibiting more socially desirable and less variable traits. In contrast, non-conventional personas displayed unpredictable behaviors, sometimes extending personality trait scores beyond the typical human range. These findings have important implications for understanding LLM behavior under different conditions and reflect on the consequences of scaling.
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
Sangeeta Mehta
Diagnostic tests for infections in critically ill immunocompromised patients
Adrien Joseph
Lara Zafrani
Oxygen thresholds in critically ill patients: need for personalized targets. Author's reply.
Laveena Munshi