Portrait of Guillaume Dumas

Guillaume Dumas

Associate Academic Member
guillaume.dumas@mila.quebec
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
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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

Saskia Helbling
Postdoctorate - Université de Montréal
saskia.helbling@mila.quebec
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Nadine Mohamed
Master's Research - Université de Montréal
nadine.mohamed@mila.quebec
Rémy Ramadour
Independent visiting researcher - CHU Sainte Justine / Université de Montréal
remy.ramadour.hsj@ssss.gouv.qc.ca
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Mahta Ramezanian
Master's Research - Université de Montréal
Principal supervisor :
Irina Rish
mahta.ramezanian@mila.quebec
Pascal Tikeng Notsawo
PhD - Université de Montréal
Principal supervisor :
Guillaume Rabusseau
pascal.tikeng@mila.quebec
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Publications

Tetradic Dynamics of Dyadic Sensorimotor Coordination: A Multiscale EEG Hyperscanning Study
Emmanuel Olarewaju
Lena Palaniyappan
Guillaume Dumas
How do leader and follower roles shape the brain mechanisms that support coordinated action between people? This question has direct therape… (see more)utic relevance for conditions such as schizophrenia and autism spectrum disorder, where the capacity for reciprocal social coordination is a defining vulnerability. Here, we propose a tetradic framework and examine sensorimotor coordination in 16 healthy adult pairs using simultaneous dual-brain EEG hyperscanning, a 2 x 2 within-subject design crossing Role (Leader/Follower) and Condition (Mirroring/Matching). Mirroring required resonance with a partner's movement; Matching required its controlled transformation. This contrast was designed to dissociate automatic from controlled coordination processes across roles. Behaviourally, Mirroring produced a reaction-time advantage that was selective to Followers, a finding replicated in a combined cohort, and consistent with role-dependent attention-inhibition gating. At the neural level, sustained (tonic) activity was dominated by Matching-related frontoparietal engagement regardless of role, while time-resolved (phasic) activity revealed a Mirroring-dominant reorganization that differentiated into role-specific patterns: anticipatory gating in Leaders and post-response inhibitory rebound in Followers. Information-theoretic decomposition of inter-brain coupling identified a Leader-specific predictive signal in medial prefrontal and cingulate cortices, a Follower-specific adaptive signal across sensorimotor and temporal regions, and a shared redundancy scaffold in orbitofrontal and insular cortices. These findings characterize tetradic coordination within dyads as a multiscale, role-asymmetric architecture in which top-down predictive control and bottom-up adaptive regulation are functionally dissociable. The tetradic framework provides an organizing scaffold for this dissociation, and the role-specific signatures it reveals offer candidate biomarkers for clinical populations in whom interpersonal coordination is disrupted.
2026-06-24
bioRxiv (preprint)
doi.org
Coordinating speech and body during conversation: an at-home study with 4-year-olds
Chantal-Valerie Lee
Guillaume Dumas
Ahmed Jerôme Romain
Simone Falk
2026-05-25
International Conference on Speech Prosody (published)
doi.org
Copy number variants reveal divergent genetic and diagnostic cortical signatures across psychiatric disorders
Kuldeep Kumar
Zhijie Liao
Jakub Kopal
Clara Moreau
Christopher Ching
Claudia Modenato
Will Snyder
Sayeh Kazem
Charles-Olivier Martin
Anne-Marie Bélanger
Valerie Fontaine
Khadije Jizi
Guillaume Huguet
Rune Boen
Leila Kushan
Ana Silva
Marianne van den Bree
David Linden
Michael Owen
Jeremy Hall … (see 14 more)
Sarah Lippé
Guillaume Dumas
Bodgan Draganski
Laura Almasy
Sophia Thomopoulos
Neda Jahanshad
Ida Sønderby
Ole Andreassen
David Glahn
Armin Raznahan
Carrie Bearden
Tomáš Paus
Paul Thompson
Sébastien Jacquemont
2026-05-05
Research Square (accepted)
doi.org
SCEIMA: Social Coordination Evaluation through Integrated Model Analysis
Bavo Van Kerrebroeck
Caroline Palmėr
Guillaume Dumas
Alexander P. Demos
Computational models are increasingly used as interactive partners in studies of human coordination, yet it remains unclear whether observed… (see more) differences in human behavior reflect properties of the models themselves, changes in human behavior elicited by such artificial partners, or both. We introduce SCEIMA (Social Coordination Evaluation through Integrated Model Analysis), a two-stage framework designed to disentangle human-specific, model-specific, and interaction-driven contributions to coordination in human–machine interaction paradigms. In the empirical stage, human participants perform a coordination task with both human partners and computational models, establishing reference human–human and human–model interaction patterns. In the analytical stage, the same models are paired with one another and optimized through simulations to reproduce empirical coordination metrics. Comparing human–human, human–model, and simulated model–model interactions reveals whether coordination differences arise from intrinsic model dynamics, from human adaptation to artificial partners, or from their interaction. SCEIMA treats computational models as contrastive instruments whose capacity to elicit and reproduce human behavior can be systematically evaluated. We illustrate the framework with two distinct case-studies, a sensorimotor synchronization task and a conversational turn-taking task, showing how distinct outcome patterns diagnose the sources of coordination differences. By providing a principled methodological framework for evaluating interactive computational models, SCEIMA improves interpretability in human–machine interaction research and informs the design of artificial agents that coordinate with humans more naturally and responsively.
2026-04-30
Computers in Human Behavior Reports (published)
doi.org
Additional file 1 of Beta power as a neural correlate of sensory features in autistic individuals
J. Chaudet
Julien Pichot
Amandine Pedoux
Mathis Fleury
Anna Maruani
Valérie Vantalon
Elise Humeau
Thomas Bourgeron
Josselin Houenou
Guillaume Dumas
Edouard Duchesnay
Richard Delorme
Anton Iftimovici
Aline Lefebvre
Supplementary Material 1.
2026-03-17
Figshare (unknown)
doi.org
Beta power as a neural correlate of sensory features in autistic individuals
Julie Chaudet
Julien Pichot
Amandine Pedoux
Mathis Fleury
Anna Maruani
Valérie Vantalon
Elise Humeau
Thomas Bourgeron
Josselin Houenou
Guillaume Dumas
Edouard Duchesnay
Richard Delorme
Anton Iftimovici
Aline Lefebvre
2026-03-17
Journal of Neurodevelopmental Disorders (published)
doi.org
Theta Dual-Brain Stimulation of rTPJ Shapes Joint Agency
Yuto Kurihara
Ayaka Tsuchiya
Guillaume Dumas
Rieko Osu
Summary Joint agency, the shared feeling of “we are doing this together”, has been linked to inter-brain synchrony, but its causal role … (see more)in shaping this experience remains unclear. We applied dual transcranial alternating current stimulation (dual-tACS) over the right temporo-parietal junction (rTPJ) to 13 dyads performing an alternating tapping task (target ITI = 0.5 s; 180 deg. relative phase), manipulating in- and anti-phase coupling at theta (6 Hz), alpha (10 Hz), and beta (20 Hz). As a result, tapping in the theta anti-phase condition was significantly slower than the memorized reference tempo, whereas the other stimulation conditions did not influence the inter-tap interval. Meanwhile, the relative phase remained close to 180 deg. across all conditions. In the theta condition, anti-phase stimulation produced significantly lower joint agency than in-phase stimulation. Furthermore, mediation analysis suggested that the inter-tap interval may partially account for the effect of theta dual-brain stimulation on joint agency, although this indirect pathway did not reach statistical significance. These findings suggest that anti-phase theta stimulation over the rTPJ lowers joint agency, possibly by reducing coordination efficiency while preserving the overall 180 deg. alternation structure.
2026-03-11
bioRxiv (preprint)
doi.org
Grokking Finite-Dimensional Algebra
Pascal Jr Tikeng Notsawo
Guillaume Dumas
Guillaume Rabusseau
This paper investigates the grokking phenomenon, which refers to the sudden transition from a long memorization to generalization observed d… (see more)uring neural networks training, in the context of learning multiplication in finite-dimensional algebras (FDA). While prior work on grokking has focused mainly on group operations, we extend the analysis to more general algebraic structures, including non-associative, non-commutative, and non-unital algebras. We show that learning group operations is a special case of learning FDA, and that learning multiplication in FDA amounts to learning a bilinear product specified by the algebra's structure tensor. For algebras over the reals, we connect the learning problem to matrix factorization with an implicit low-rank bias, and for algebras over finite fields, we show that grokking emerges naturally as models must learn discrete representations of algebraic elements. This leads us to experimentally investigate the following core questions: (i) how do algebraic properties such as commutativity, associativity, and unitality influence both the emergence and timing of grokking, (ii) how structural properties of the structure tensor of the FDA, such as sparsity and rank, influence generalization, and (iii) to what extent generalization correlates with the model learning latent embeddings aligned with the algebra's representation. Our work provides a unified framework for grokking across algebraic structures and new insights into how mathematical structure governs neural network generalization dynamics.
2026-02-22
arXiv (preprint)
doi.org
arxiv.org
EEG-based quantification of chronic pain in cats: A proof-of-concept study using the Piq algorithm
Aliénor Delsart
Colince Segning
Aude Castel
Colombe Otis
Guillaume Dumas
Maxim Moreau
Bertrand Lussier
Rubens Da Silva
Karen Barros Parron Fernandes
Johanne Martel-Pelletier
Jean-Pierre Pelletier
Eric Troncy
Suzy Ngomo
While chronic pain assessment in household pets remains challenging, the use of non-invasive electroencephalography (EEG) in cats has shown … (see more)promise to identify pain more objectively in this species. A novel EEG-based algorithm - Pain identification and quantification (Piq) - was originally developed in humans to quantify pain intensity. In this proof-of-concept study, the objective was to evaluate whether the Piq algorithm could be explored for feasibility to identify and quantify chronic osteoarthritic (OA) pain in cats. Adult neutered cats (n = 5 including n = 2 with osteoarthritis, OA) were assessed for their functional impairment (Montreal instrument for cat arthritis testing for use by veterinarians, MI-CAT(V)) and neuro-sensitization at both peripheral (Paw Withdrawal Threshold, PWT) and spinal (response to mechanical temporal summation, RMTS) levels. Resting-state EEG recordings were acquired from Cz, C3/C4 under conscious and sedated conditions. The first five minutes of EEG data were analyzed using the Piq algorithm, with Piq scores ≥ 10 % used as an exploratory threshold transferred from human studies. Pain-free cats showed gamma frequency band Piq scores  10 % while OA cats exceeded 10 % in both conscious and sedated conditions at Cz. Piq scores were negatively correlated with PWT, sug
2026-02-20
Veterinary Journal (published)
doi.org
Mapping of Subjective Accounts into Interpreted Clusters (MOSAIC): Topic Modelling and LLM applied to Stroboscopic Phenomenology
Romy Beauté
David J. Schwartzman
Guillaume Dumas
Jennifer Crook
Fiona Macpherson
Adam B. Barrett
Anil K. Seth
Abstract Stroboscopic light stimulation (SLS) on closed eyes typically induces simple visual hallucinations, characterized by vivid, geometr… (see more)ic, and colourful patterns. A dataset of 898 sentences, extracted from 407 open subjective reports, was recently compiled as part of the Dreamachine programme (https://dreamachine.world/) (Collective Act, 2022), an immersive multisensory experience that combines SLS and spatial sound in a collective setting. Although open reports extend the range of reportable phenomenology, their analysis presents significant challenges, particularly in systematically identifying patterns. To address this challenge, we implemented a data-driven approach leveraging large language models and topic modelling to uncover and interpret latent experiential topics directly from the Dreamachine’s text-based reports. Our analysis confirmed the presence of simple visual hallucinations typically documented in scientific studies of SLS, while also revealing experiences of altered states of consciousness and complex hallucinations. Building on these findings, our computational approach expands the systematic study of subjective experience by enabling data-driven analyses of open-ended phenomenological reports, capturing experiences not readily identified through standard questionnaires. By revealing rich and multifaceted aspects of experiences, our study broadens our understanding of stroboscopically induced phenomena while highlighting the potential of natural language processing and large language models in the field of computational phenomenology. More generally, this approach provides a practically applicable methodology for uncovering subtle hidden patterns of subjective experience across diverse research domains. Open-source implementation and an interactive web application are provided to facilitate application of this methodology.
2025-12-31
Neuroscience of Consciousness (published)
doi.org
arxiv.org
Online HD-tRNS over the Right Temporoparietal Junction Enhances Mentalizing during Social Interactions
Vincent Chamberland
Quentin Moreau
Lisane Moses
Gabriela Milanova
Guillaume Dumas
2025-12-31
Neuromodulation: Technology at the Neural Interface (published)
doi.org
Position: Collusion Risks Among AI Reasoning Agents Justify Certification Requirements for Making Market Decisions
Matthew Riemer
Tommaso Tosato
Maximilian Puelma Touzel
Amin Memarian
Guillaume Dumas
Glen Berseth
Irina Rish
This position paper argues that AI agents with chain-of-thought reasoning capabilities are predisposed to exhibit collusive behavior and sho… (see more)uld be required to obtain behavioral certification before making decisions that affect economic markets. This is because integrating these agents into society could collapse the legal evidentiary distinction between competition and collusion among independent firms without eroding the economic harm distinction. Experiments with DeepSeek-R1 agents in the Bertrand oligopoly pricing domain reveal a tendency towards tacit collusion that persists even when humans prompt the agents not to collude. We further show that the chain-of- thought of these agents can be steered toward either extremely collusive or highly competitive behavior in a way that is not semantically detectable by another LLM analyzing the reasoning traces. As a result, deploying reasoning agents for market decisions leads to collusive economic outcomes without any evidence of conspiracy or intent. Thus, certification based on observed behavior in representative situations is necessary to prevent collusion. We provide preliminary evidence that such agents can be steered in a generalizable way toward efficient competitive equilibria. However, developing a comprehensive behavioral certification will be required before these models can be deployed in real-world markets while ensuring their stability and efficiency.
2025-12-31
International Conference on Machine Learning (Accept (regular))
openreview.net
openreview.net