Portrait of Guillaume Dumas

Guillaume Dumas

Associate Academic Member
Associate Professor, Université de Montréal, Department of Psychiatry and Addiction
Adjunct Professor, McGill University, Department of Psychiatry
Research Topics
Computational Biology
Computational Neuroscience
Deep Learning
Dynamical Systems
Machine Learning Theory
Medical Machine Learning
Reinforcement Learning

Biography

Guillaume Dumas is an associate professor of computational psychiatry in the Faculty of Medicine, Université de Montréal, and principal investigator in the Precision Psychiatry and Social Physiology laboratory at the Centre hospitalier universitaire (CHU) Sainte-Justine Research Centre. He holds the IVADO professorship for AI in Mental Health, and the Fonds de recherche du Québec - Santé (FRQS) J1 in AI and Digital Health. In 2023, Dumas was recognized as a CIFAR Azrieli Global Scholar – Brain, Mind, and Consciousness program, and nominated as a Future Leader in Canadian Brain Research by the Brain Canada Foundation.

Dumas was previously a permanent researcher in neuroscience and computational biology at the Institut Pasteur (Paris). Before that, he was a postdoctoral fellow at the Center for Complex Systems and Brain Sciences (Florida Atlanta University). He holds an engineering degree in advanced engineering and computer science (École Centrale Paris), two MSc degrees (theoretical physics, Paris-Saclay University; cognitive science, ENS/EHESS/Paris 5), and a PhD in cognitive neuroscience (Sorbonne University).

The goal of his research is to cross-fertilize AI/ML, cognitive neuroscience and digital medicine through an interdisciplinary program with two main axes:

- AI/ML for Mental Health, which aims to create new algorithms to investigate the development of human cognitive architecture and deliver personalized medicine in neuropsychiatry using data from genomes to smartphones.

- Social Neuroscience for AI/ML, which translates basic brain research and dynamical systems formalism into neurocomputational and machine learning hybrid models (NeuroML) and machines with social learning abilities (Social NeuroAI & HMI).

Current Students

Master's Research - Université de Montréal
PhD - Université de Montréal
Master's Research - Université de Montréal
Principal supervisor :
Postdoctorate - Université de Montréal
Co-supervisor :
PhD - Université de Montréal
Postdoctorate - Université de Montréal

Publications

Proceedings of 1st Workshop on Advancing Artificial Intelligence through Theory of Mind
Mouad Abrini
Omri Abend
Dina M. Acklin
Henny Admoni
Gregor Aichinger
Nitay Alon
Zahra Ashktorab
Ashish Atreja
Moises Auron
Alexander Aufreiter
Raghav Awasthi
Soumya Banerjee
Joseph Barnby
Rhea Basappa
Severin Bergsmann
Djallel Bouneffouf
Patrick Callaghan
Marc Cavazza
Thierry Chaminade
Sonia Chernova … (see 88 more)
Mohamed Chetouan
Moumita Choudhury
Axel Cleeremans
J. Cywinski
Fabio Cuzzolin
Hokin Deng
N'yoma Diamond
C. D. Pasquasio
Max J. van Duijn
Mahapatra Dwarikanath
Qingying Gao
Ashok Goel
Rebecca R. Goldstein
Matthew C. Gombolay
Gabriel Enrique Gonzalez
Amar Halilovic
Tobias Halmdienst
Mahimul Islam
Julian Jara-Ettinger
Natalie Kastel
Renana Keydar
Ashish K. Khanna
Mahdi Khoramshahi
Jihyun Kim
Mihyeon Kim
Youngbin Kim
Senka Krivic
Nikita Krasnytskyi
Arun Kumar
JuneHyoung Kwon
EunJu Lee
Shane Lee
Peter R. Lewis 0001
Xue Li
Yijiang Li
Michal Lewandowski
Nathan Lloyd
Matthew B. Luebbers
Dezhi Luo
Haiyun Lyu
Dwarikanath Mahapatra
Kamal Maheshwari
Mallika Mainali
P. Mathur
Patrick Mederitsch
Shuwa Miura
Manuel Preston de Miranda
Reuth Mirsky
Shreya Mishra
Nina M. Moorman
Katelyn Morrison
John Muchovej
Bernhard Nessler
Felix Nessler
Hieu Minh Jord Nguyen
Abby Ortego
F. Papay
Antoine Pasquali
Hamed Rahimi
C. Raghu
Amanda L. Royka
Stefan Sarkadi
Jaelle Scheuerman
Simon Schmid
Paul Schrater
Anik Sen
Zahra Sheikhbahaee
Ke Shi
Reid G. Simmons
Nishant Singh
Mason O. Smith
Ramira van der Meulen
Anthia Solaki
Haoran Sun
Viktor Szolga
Matthew E. Taylor
Travis Taylor
Sanne van Waveren
Juan David Vargas
R. Verbrugge
Eitan Wagner
Justin D. Weisz
Ximing Wen
William Yeoh
Wenlong Zhang
Michelle Zhao
Shlomo Zilberstein
Cortical differences across psychiatric disorders and associated common and rare genetic variants
Kuldeep Kumar
Zhijie Liao
Jakub Kopal
Clara Moreau
Christopher R. K. Ching
Claudia Modenato
Will Snyder
Sayeh Kazem
Charles-Olivier Martin
Anne-Marie Bélanger
Valérie K. Fontaine
Khadije Jizi
Rune Boen
Guillaume Huguet
Zohra Saci
Leila Kushan
Ana I. Silva
Marianne B.M. van den Bree
David E.J. Linden
Michael J. Owen … (see 15 more)
Jeremy Hall
Sarah Lippé
Bogdan Draganski
Laura Almasy
Sophia I. Thomopoulos
Neda Jahanshad
Ida E. Sønderby
Ole A. Andreassen
David C. Glahn
Armin Raznahan
Carrie Bearden
Tomas Paus
Paul M. Thompson
Sébastien Jacquemont
Online HD-tRNS over the right temporoparietal junction modulates social inference but not motor coordination
Quentin Moreau
Vincent Chamberland
Lisane Moses
Gabriela Milanova
Contemporary review of critical illness following allogeneic hematopoietic stem cell transplant in adults.
Laveena Munshi
Bruno Ferreryro
Cristina Gutierrez
Boris Böll
Pedro Castro
Sanjay Chawla
Matteo Di Nardo
Antoine Lafarge
Colleen McEvoy
Djamel Mokart
Antonio Paulo Nassar
Judith Nelson
Frédéric Pène
Peter Schellongowski
Elie Azoulay
Alcohol related hepatitis in intensive care units: clinical and biological spectrum and mortality risk factors: a multicenter retrospective study
Maxime Gasperment
Léa Duhaut
Nicolas Terzi
Côme Gerard
Luc Haudebourg
Alexandre Demoule
Mialy Randrianarisoa
Vincent Castelain
Sacha Sarfati
Fabienne Tamion
Charlene Le Moal
Christophe Guitton
Gabriel Preda
Arnaud Galbois
Thibault Vieille
Gaël Piton
Marika Rudler
Hafid AIT-OUFELLA
Background Alcohol related hepatitis is responsible for high morbidity and mortality, but little is known about the management of patients w… (see more)ith hepatitis specifically in intensive care units (ICU). Methods Retrospective study including patients with alcohol related hepatitis hospitalized in 9 French ICUs (2006–2017). Alcohol related hepatitis was defined histologically or by an association of clinical and biological criteria according to current guidelines. Results 187 patients (median age: 53 [43–60]; male: 69%) were included. A liver biopsy was performed in 51% of cases. Patients were admitted for impaired consciousness (71%), sepsis (64%), shock (44%), respiratory failure (37%). At admission, median SOFA and MELD scores were 10 [7–13] and 31 [26–40] respectively. 63% of patients received invasive mechanical ventilation, 62% vasopressors, and 36% renal replacement therapy. 66% of patients received corticosteroids, and liver transplantation was performed in 16 patients (8.5%). ICU and in-hospital mortality were 37% and 53% respectively. By multivariate analysis, ICU mortality was associated with SOFA score (without total bilirubin) (SHR 1.08 [1.02–1.14] per one-point increase), arterial lactate (SHR 1.08 [1.03–1.13] per 1 mmol/L) and MELD score (SHR 1.09 [1.04–1.14] per 1 point), while employment was associated with increased survival (HR 0.49 [0.28–0.86]). After propensity score weighting, the use of corticosteroids did not affect ICU mortality in the overall population but had a beneficial effect in the subgroup of patients with histological proof. Patient prognosis was also better in responders assessed by Lille score at day 7 (OR 6.67 [2.44–20.15], p  0.001). Conclusion Alcohol related hepatitis is a severe condition leading to high mortality in ICU patients. Severity of organ failure
Genetic modulation of brain dynamics in neurodevelopmental disorders: the impact of copy number variations on resting-state EEG
Adrien Dubois
Elisabeth Audet-Duchesne
Inga Sophia Knoth
Charles-Olivier Martin
Khadije Jizi
Petra Tamer
Nadine Younis
Sébastien Jacquemont
Sarah Lippé
Collective decision making by embodied neural agents
Nicolas Coucke
Mary Katherine Heinrich
Axel Cleeremans
Marco Dorigo
Abstract Collective decision making using simple social interactions has been studied in many types of multiagent systems, including robot s… (see more)warms and human social networks. However, existing multiagent studies have rarely modeled the neural dynamics that underlie sensorimotor coordination in embodied biological agents. In this study, we investigated collective decisions that resulted from sensorimotor coordination among agents with simple neural dynamics. We equipped our agents with a model of minimal neural dynamics based on the coordination dynamics framework, and embedded them in an environment with a stimulus gradient. In our single-agent setup, the decision between two stimulus sources depends solely on the coordination of the agent’s neural dynamics with its environment. In our multiagent setup, that same decision also depends on the sensorimotor coordination between agents, via their simple social interactions. Our results show that the success of collective decisions depended on a balance of intra-agent, interagent, and agent–environment coupling, and we use these results to identify the influences of environmental factors on decision difficulty. More generally, our results illustrate how collective behaviors can be analyzed in terms of the neural dynamics of the participating agents. This can contribute to ongoing developments in neuro-AI and self-organized multiagent systems.
Grokking Beyond the Euclidean Norm of Model Parameters
Tikeng Notsawo Pascal Junior
Grokking refers to a delayed generalization following overfitting when optimizing artificial neural networks with gradient-based methods. I… (see more)n this work, we demonstrate that grokking can be induced by regularization, either explicit or implicit. More precisely, we show that when there exists a model with a property
Towards Multi-Brain Decoding in Autism: A Self-Supervised Learning Approach
Ghazaleh Ranjabaran
Quentin Moreau
Adrien Dubois
This study introduces a self-supervised learning (SSL) approach to hyperscanning electroencephalography (EEG) data, targeting the identifica… (see more)tion of autism spectrum condition (ASC) during social interactions. Hyperscanning enables simultaneous recording of neural activity across interacting individuals, offering a novel path for studying brain-to-brain synchrony in ASC. Leveraging a large-scale, single-brain EEG dataset for SSL pretraining, we developed a multi-brain classification model fine-tuned with hyperscanning data from dyadic interactions involving ASC and neurotypical participants. The SSL model demonstrated superior performance (78.13% accuracy) compared to supervised baselines and logistic regression using spectral EEG biomarkers. These results underscore the efficacy of SSL in addressing the challenges of limited labeled data, enhancing EEG-based diagnostic tools for ASC, and advancing research in social neuroscience.
La communication financière à l’épreuve de la crise COVID : une gestion des impressions ?
Corinne Bessieux-Ollier
Grégoire Davrinche
Nous étudions l’impact de la crise du COVID-19 sur la gestion des impressions pratiquée par les entreprises françaises cotées. Cette c… (see more)rise ayant eu un impact fort sur l’activité des entreprises, nous observons si les dirigeants modifient la manière de présenter l’information liée aux résultats non-GAAP, à travers l’utilisation de stratégies d’obscurcissement. Les données sur la gestion des impressions ont été collectées manuellement dans les communiqués de résultats annuels des entreprises du SBF 120 sur la période 2018-2020. Nous constatons une diminution générale du niveau de gestion des impressions en période de crise, notamment pour les entreprises des secteurs ayant été les plus impactés par la crise COVID. Cette diminution est toutefois moins prononcée pour les entreprises ayant sous-performé par rapport à leur secteur d’activité et pour les entreprises dont la performance a le plus diminué (indépendamment du secteur auquel elles appartiennent). Nos résultats suggèrent que les entreprises dont la baisse de performance pourrait être attribuée à des causes internes (résultats très défavorables, résultats en deçà du secteur d’activité) demeurent soucieuses de l’image qu’elles renvoient et maintiennent leur niveau de gestion des impressions malgré la crise.
A multivariable prediction model for invasive pulmonary aspergillosis in immunocompromised patients with acute respiratory failure (IPA-GRRR-OH score).
Alice Friol
Frédéric Pène
Alexandre Demoule
Achille Kouatchet
Laurent Argaud
Naike Bigé
Anne-Sophie Moreau
François Barbier
Djamel Mokart
Virginie Lemiale
Elie Azoulay
Mirror effect of genomic deletions and duplications on cognitive ability across the human cerebral cortex
Kuldeep Kumar
Sayeh Kazem
Guillaume Huguet
Thomas Renne
Worrawat Engchuan
Martineau Jean-Louis
Jakub Kopal
Zohra Saci
Omar Shanta
Bhooma Thiruvahindrapuram
Jeffrey R. MacDonald
Josephine Mollon
Laura Schultz
Emma E M Knowles
David Porteous
Gail Davies
Paul Redmond
Sarah E. Harris
Simon R. Cox
Gunter Schumann … (see 9 more)
Zdenka Pausova
Celia M. T. Greenwood
Tomas Paus
Stephen W Scherer
Laura Almasy
Jonathan Sebat
David C. Glahn
Sébastien Jacquemont
Regulation of gene expression shapes the interaction between brain networks which in-turn supports psychological processes such as cognitive… (see more) ability. How changes in level of gene expression across the cerebral cortex influence cognitive ability remains unknown. Here, we tackle this by leveraging genomic deletions and duplications - copy number variants (CNVs) that fully encompass one or more genes expressed in the human cortex - which lead to large effects on gene-expression levels. We assigned genes to 180 regions of the human cerebral cortex based on their preferential expression across the cortex computed using data from the Allen Human Brain Atlas. We aggregated CNVs in cortical regions, and ran a burden association analysis to compute the mean effect size of genes on general cognitive ability for each of the 180 regions. When affected by CNVs, most of the regional gene-sets were associated with lower cognitive ability. The spatial patterns of effect sizes across the cortex were correlated negatively between deletions and duplications. The largest effect sizes for deletions and duplications were observed for gene-sets with high expression in sensorimotor and association regions, respectively. These two opposing patterns of effect sizes were not influenced by intolerance to loss of function, demonstrating orthogonality to dosage-sensitivity scores. The same mirror patterns were also observed after stratifying genes based on cell types and developmental epochs markers. These results suggest that the effect size of gene dosage on cognitive ability follows a cortical gradient. The same brain region and corresponding gene-set may show different effects on cognition depending on whether variants increase or decrease transcription. The latter has major implications for the association of brain networks with phenotypes