Portrait de Danilo Bzdok

Danilo Bzdok

Membre académique principal
bzdokdan@mila.quebec
Chaire en IA Canada-CIFAR
Professeur agrégé, McGill University, Département de génie biomédicale
Sujets de recherche
Apprentissage profond
Biologie computationnelle
Grands modèles de langage (LLM)
Traitement du langage naturel

Biographie

Danilo Bzdok est informaticien et médecin de formation. Il possède une double formation unique en neurosciences systémiques et en algorithmes d'apprentissage automatique. Après une formation à l'Université d'Aix-la-Chapelle (RWTH) (Allemagne), à l'Université de Lausanne (Suisse) et à la Harvard Medical School (États-Unis), il a obtenu un doctorat en neurosciences du Centre de recherche de Jülich (Allemagne) et un doctorat en informatique dans le domaine des statistiques d'apprentissage automatique à l'INRIA Saclay et à NeuroSpin (Paris, France). Il est actuellement professeur agrégé à la Faculté de médecine de l'Université McGill et titulaire d’une chaire en IA Canada-CIFAR à Mila – Institut québécois d'intelligence artificielle. Son activité de recherche interdisciplinaire est centrée sur la réduction des lacunes dans la connaissance des bases cérébrales des types de pensée qui définissent l'être humain, afin de découvrir les principes clés de conception computationnelle qui sous-tendent l'intelligence humaine.

Étudiants actuels

Shambhavi Aggarwal
Maîtrise recherche - McGill
Github
Google Scholar
Badr Ait Hammou
Postdoctorat - McGill
Gregory Bell
Doctorat - McGill
Anwesha Bhattacharya
Doctorat - McGill
Liam Hodgson
Doctorat - McGill
Ryan McPhedrain McPhedrain
Postdoctorat - McGill
ryan.mcphedrain@mail.mcgill.ca
David Payette
Maîtrise recherche - McGill
Github
Sepehr Radmannia
Visiteur de recherche indépendant - McGill
sradmannia@gmail.com
Github
Karin Saltoun
Doctorat - McGill
Chloé Savignac
Doctorat - McGill
Github
Google Scholar
Jack Stanley
Doctorat - McGill
Site web
Github
Praneet Suresh
Doctorat - McGill
Zilong Wang
Maîtrise recherche - McGill
Github
Google Scholar
Le Zhou
Doctorat - McGill

Publications

Estimating Unknown Population Sizes Using the Hypergeometric Distribution
Liam Hodgson
Danilo Bzdok
The multivariate hypergeometric distribution describes sampling without replacement from a discrete population of elements divided into mult… (voir plus)iple categories. Addressing a gap in the literature, we tackle the challenge of estimating discrete distributions when both the total population size and the category sizes are unknown. Here, we propose a novel solution using the hypergeometric likelihood to solve this estimation problem, even in the presence of severe under-sampling. Our approach accounts for a data generating process where the ground-truth is a mixture of distributions conditional on a continuous latent variable, as seen in collaborative filtering, using the variational autoencoder framework. Empirical data simulation demonstrates that our method outperforms other likelihood functions used to model count data, both in terms of accuracy of population size estimate and learning an informative latent space. We showcase our method’s versatility through applications in NLP, by inferring and estimating the complexity of latent vocabularies in reading passage excerpts, and in biology, by accurately recovering the true number of gene transcripts from sparse single-cell genomics data.
2024-07-08
Proceedings of the 41st International Conference on Machine Learning (publié)
proceedings.mlr.press
Supervised latent factor modeling isolates cell-type-specific transcriptomic modules that underlie Alzheimer’s disease progression
Liam Hodgson
Yue Li
Yasser Iturria-Medina
Jo Anne Stratton
Guy Wolf
Smita Krishnaswamy
David A. Bennett
Danilo Bzdok
2024-05-17
Communications Biology (publié)
doi.org
Data science opportunities of large language models for neuroscience and biomedicine
Danilo Bzdok
Andrew Thieme
Oleksiy Levkovskyy
Paul Wren
Thomas Ray
Siva Reddy
2024-02-01
Neuron (publié)
doi.org
Aberrant functional brain network organization is associated with relapse during 1‐year follow‐up in alcohol‐dependent patients
Justin Böhmer
Pablo Reinhardt
Maria Garbusow
Michael Marxen
Michael N. Smolka
Ulrich S. Zimmermann
Andreas Heinz
Danilo Bzdok
Eva Friedel
Johann D. Kruschwitz
Henrik Walter
2023-10-02
Addiction Biology (publié)
doi.org
Bayesian modeling disentangles language versus executive control disruption in stroke
Gesa Hartwigsen
Jae‐Sung Lim
Hee-Joon Bae
Kyung‐Ho Yu
Hugo J. Kuijf
Nick A. Weaver
J. Matthijs Biesbroek
Jakub Kopal
Danilo Bzdok
2023-08-07
bioRxiv (prépublication)
doi.org
Multivariate analytical approaches for investigating brain-behavior relationships
E. Leighton Durham
Karam Ghanem
Andrew J. Stier
Carlos Cardenas-Iniguez
Gabrielle E. Reimann
Hee Jung Jeong
Randolph M. Dupont
Xiaoyu Dong
Tyler M. Moore
Marc G. Berman
Benjamin B. Lahey
Danilo Bzdok
Antonia N. Kaczkurkin
2023-07-31
Frontiers in Neuroscience (publié)
doi.org
The default network dominates neural responses to evolving movie stories
Enning Yang
Filip Milisav
Jakub Kopal
Avram J. Holmes
Georgios D. Mitsis
Bratislav Mišić
Emily S. Finn
Danilo Bzdok
2023-07-14
Nature Communications (publié)
doi.org
Distinctive whole-brain cell types predict tissue damage patterns in thirteen neurodegenerative conditions
Veronika Pak
Quadri Adewale
Danilo Bzdok
Mahsa Dadar
Yashar Zeighami
Yasser Iturria-Medina
For over a century, brain research narrative has mainly centered on neuron cells. Accordingly, most neurodegenerative studies focus on neuro… (voir plus)nal dysfunction and their selective vulnerability, while we lack comprehensive analyses of other major cell types’ contribution. By unifying spatial gene expression, structural MRI, and cell deconvolution, here we describe how the human brain distribution of canonical cell types extensively predicts tissue damage in thirteen neurodegenerative conditions, including early-and late-onset Alzheimer’s disease, Parkinson’s disease, dementia with Lewy bodies, amyotrophic lateral sclerosis, mutations in presenilin-1, and three clinical variants of frontotemporal lobar degeneration (behavioural variant, semantic and non-fluent primary progressive aphasia) along with associated 3-repeat and 4-repeat tauopathies and TDP43 proteinopathies types A and C. We reconstructed comprehensive whole-brain reference maps of cellular abundance for six major cell types and identified characteristic axes of spatial overlapping with atrophy. Our results support the strong mediating role of non-neuronal cells, primarily microglia and astrocytes, in spatial vulnerability to tissue loss in neurodegeneration, with distinct and shared across-disorders pathomechanisms. These observations provide critical insights into the multicellular pathophysiology underlying spatiotemporal advance in neurodegeneration. Notably, they also emphasize the need to exceed the current neuro-centric view of brain diseases, supporting the imperative for cell-specific therapeutic targets in neurodegeneration.
2023-06-09
bioRxiv (prépublication)
doi.org
A hierarchical Bayesian brain parcellation framework for fusion of functional imaging datasets
Da Zhi
Ladan Shahshahani
Caroline Nettekoven
Ana Lúısa Pinho
Danilo Bzdok
Jörn Diedrichsen
2023-05-24
bioRxiv (prépublication)
doi.org
156. Modeling Eye Gaze to Videos Using Dynamic Trajectory Variability Analysis
Qianying Wu
Na Yeon Kim
Jasmin Turner
Umit Keles
Lynn Paul
Danilo Bzdok
Ralph Adolphs
2023-05-01
Biological Psychiatry (publié)
doi.org
Using rare genetic mutations to revisit structural brain asymmetry
Jakub Kopal
Kuldeep Kumar
Kimia Shafighi
Karin Saltoun
Claudia Modenato
Clara A. Moreau
Guillaume Huguet
Martineau Jean-Louis
Charles-Olivier Martin
C.O. Martin
Zohra Saci
Nadine Younis
Elise Douard
Khadije Jizi
Alexis Beauchamp-Chatel
Leila Kushan
Ana I. Silva
Marianne B.M. van den Bree
David E.J. Linden
M. J. Owen … (voir 11 de plus)
Jeremy Hall
Sarah Lippé
Bogdan Draganski
Ida E. Sønderby
Ole A. Andreassen
David C. Glahn
Paul M. Thompson
Carrie E. Bearden
Robert Zatorre
Sébastien Jacquemont
Danilo Bzdok
2023-04-18
bioRxiv (prépublication)
doi.org
Genesis, modelling and methodological remedies to autism heterogeneity
Juliette Rabot
Eya‐mist Rødgaard
Ridha Joober
Guillaume Dumas
Danilo Bzdok
Boris C Bernhardt
Sébastien Jacquemont
Laurent Mottron
2023-04-01
Neuroscience and Biobehavioral Reviews (publié)
doi.org