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
Website
Google Scholar
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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

Germán Abrevaya
Independent visiting researcher - Université de Montréal
Principal supervisor :
Irina Rish
Google Scholar
Juan David Vargas
Master's Research - Université de Montréal
Kastel Natalie
PhD - Université de Montréal
Mahta Ramezanian
Master's Research - Université de Montréal
Principal supervisor :
Irina Rish
Zahra Sheikhbahaee
Postdoctorate - Université de Montréal
Co-supervisor :
Irina Rish
Github
Pascal Tikeng Notsawo
PhD - Université de Montréal
Principal supervisor :
Guillaume Rabusseau
Website
Github
Google Scholar

Publications

Introducing Brain Foundation Models
Mohammad Javad Darvishi Bayazi
Hena Ghonia
Roland Riachi
Bruno Aristimunha
Arian Khorasani
Md Rifat Arefin
Amin Darabi
Sylvain Chevallier
Guillaume Dumas
Irina Rish
Brain function represents one of the most complex systems driving our world. Decoding its signals poses significant challenges, particularly… (see more) 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.
2024-10-10
NeurIPS.cc/2024/Workshop/TSALM (published)
openreview.net
openreview.net
LLMs and Personalities: Inconsistencies Across Scales
Tosato Tommaso
Mahmood Hegazy
David Lemay
Mohammed Abukalam
Irina Rish
Guillaume Dumas
This study investigates the application of human psychometric assessments to large language models (LLMs) to examine their consistency and m… (see more)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.
2024-10-09
NeurIPS.cc/2024/Workshop/Behavioral_ML (oral)
openreview.net
openreview.net
Scalable Approaches for a Theory of Many Minds
Maximilian Puelma Touzel
Amin Memarian
Matthew D Riemer
Andrei Mircea
Andrew Robert Williams
Elin Ahlstrand
Lucas Lehnert
Rupali Bhati
Guillaume Dumas
Irina Rish
A major challenge as we move towards building agents for real-world problems, which could involve a massive number of human and/or machine a… (see more)gents, is that we must learn to reason about the behavior of these many other agents. In this paper, we consider the problem of scaling a predictive Theory of Mind (ToM) model to a very large number of interacting agents with a fixed computational budget. Motivated by the limited diversity of agent types, existing approaches to scalable TOM learn versatile single-agent representations for quickly adapting to new agents encountered sequentially. We consider the more general setting that many agents are observed in parallel and formulate the corresponding Theory of Many Minds (ToMM) problem of estimating the joint policy. We frame the scaling behavior of solutions in terms of parameter sharing schemes and in particular propose two parameter-free architectural features that endow models with the ability to exploit action correlations: encoding a multi-agent context, and decoding through an abstracted joint action space. The increased predictive capabilities that have come with foundation models have made it easier to imagine the possibility of using these models to make simulations that imitate the behavior of many agents within complex real-world systems. Being able to perform these simulations in a general-purpose way would not only help make more capable agents, it also would be a very useful capability for applications in social science, political science, and economics.
2024-06-18
ICML.cc/2024/Workshop/Agentic_Markets (poster)
openreview.net
openreview.net
Lost in Translation: The Algorithmic Gap Between LMs and the Brain
Tosato Tommaso
Tikeng Notsawo Pascal Junior
Helbling Saskia
Irina Rish
Guillaume Dumas
Language Models (LMs) have achieved impressive performance on various linguistic tasks, but their relationship to human language processing … (see more)in the brain remains unclear. This paper examines the gaps and overlaps between LMs and the brain at different levels of analysis, emphasizing the importance of looking beyond input-output behavior to examine and compare the internal processes of these systems. We discuss how insights from neuroscience, such as sparsity, modularity, internal states, and interactive learning, can inform the development of more biologically plausible language models. Furthermore, we explore the role of scaling laws in bridging the gap between LMs and human cognition, highlighting the need for efficiency constraints analogous to those in biological systems. By developing LMs that more closely mimic brain function, we aim to advance both artificial intelligence and our understanding of human cognition.
2024-06-17
ICML.cc/2024/Workshop/LLMs_and_Cognition (poster)
openreview.net
openreview.net
295. Rare Variant Genetic Architecture of the Human Cortical MRI Phenotypes in General Population
Kuldeep Kumar
Sayeh Kazem
Zhijie Liao
Jakub Kopal
Guillaume Huguet
Thomas Renne
Martineau Jean-Louis
Zhe Xie
Zohra Saci
Laura Almasy
David C. Glahn
Tomas Paus
Guillaume Dumas
Carrie Bearden
Paul Thompson
Richard A.I. Bethlehem
Varun Warrier
Sébastien Jacquemont
2024-05-01
Biological Psychiatry (published)
doi.org
Effects of gene dosage on cognitive ability: A function-based association study across brain and non-brain processes
Guillaume Huguet
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 … (see 10 more)
Gunter Schumann
Guillaume Dumas
Aurélie Labbe
Zdenka Pausova
Tomas Paus
Stephen W Scherer
Jonathan Sebat
Laura Almasy
David C. Glahn
Sébastien Jacquemont
2024-04-17
bioRxiv (preprint)
doi.org
Predicting Grokking Long Before it Happens: A look into the loss landscape of models which grok
Tikeng Notsawo Pascal Junior
Pascal Notsawo
Hattie Zhou
Mohammad Pezeshki
Irina Rish
Guillaume Dumas
2024-03-04
ICLR.cc/2024/Workshop/ME-FoMo (poster)
doi.org
openreview.net
Sources of richness and ineffability for phenomenally conscious states
Xu Ji
Eric Elmoznino
George Deane
Axel Constant
Guillaume Dumas
Guillaume Lajoie
Jonathan Simon
Yoshua Bengio
2024-03-01
Neuroscience of Consciousness (published)
doi.org
arxiv.org
The « jingle-jangle fallacy » of empathy: Delineating affective, cognitive and motor components of empathy from behavioral synchrony using a virtual agent
Julia Ayache
Guillaume Dumas
Alexander Sumich
D. Kuss
Darren Rhodes
Nadja Heym
2024-03-01
Personality and Individual Differences (published)
doi.org
Effective Latent Differential Equation Models via Attention and Multiple Shooting
Germán Abrevaya
Mahta Ramezanian-Panahi
Jean-Christophe Gagnon-Audet
Pablo Polosecki
Irina Rish
Silvina Ponce Dawson
Guillermo Cecchi
Guillaume Dumas
2024-02-26
TMLR (accepted)
openreview.net
openreview.net
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
Guillaume Dumas
2023-12-01
Physics of Life Reviews (published)
doi.org
arxiv.org
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
Guillaume Dumas
Clara A. Moreau
Richard Delorme
2023-11-27
Molecular Autism (published)
doi.org