Guy Wolf

Biographie

Guy Wolf est professeur titulaire au Département de mathématiques et de statistique (DMS) de l'Université de Montréal (UdeM), titulaire d'une chaire en IA Canada-CIFAR et membre académique principal de Mila (l'Institut québécois d'intelligence artificielle), chercheur associé au CRCHUM (Centre de recherche du Centre hospitalier de l'Université de Montréal) et chercheur principal participant au Laboratoire international Helmholtz pour la dynamique cellulaire causale.

En 2024, il a reçu une bourse de recherche Humboldt pour chercheurs expérimentés, dans le cadre de laquelle il a été professeur invité à l'Université de Heidelberg (2024) et à Helmholtz Munich (2024-2026) en Allemagne. Avant de joindre l'UdeM et Mila, il a été professeur adjoint Gibbs (2015-2018) au sein du programme de mathématiques appliquées, puis chercheur scientifique associé au Département de génétique (2018) de l'Université Yale (Connecticut, États-Unis). Auparavant, il a travaillé comme chercheur postdoctoral (2013-2015) au Département d'informatique de l'École normale supérieure à Paris (France). Il détient un doctorat en informatique de l'Université de Tel-Aviv (Israel) et possède cinq ans d'expérience préalable en conception et développement de logiciels informatiques pour l'analyse de données en contexte militaire.

Ses recherches actuelles portent sur l'apprentissage guidé de représentations pour l'exploration de données, notamment par des méthodes qui exploitent l'apprentissage de variétés (manifold learning) et l'apprentissage profond géométrique pour la réduction de dimensionnalité, la visualisation, le débruitage, l'augmentation de données et la modélisation à gros grains (coarse graining). Bien que ces approches s'appliquent à un large éventail de domaines, il s'intéresse particulièrement à l'intersection de l'IA et de la santé, notamment aux outils facilitant l'analyse exploratoire de données biomédicales, comme dans les domaines de la multiomique sur cellule unique (single-cell multiomics), de la découverte de médicaments et des neurosciences.

Étudiants actuels

Adrien Aumon

Doctorat - UdeM

adrien.aumon@mila.quebec

Semih Cantürk

Doctorat - UdeM

semihcanturk00@gmail.com

Joao Felipe Carneiro Barbosa Rocha

Collaborateur·rice de recherche - Yale University

Co-superviseur⋅e :

joaofelipe.rocha@mila.quebec

sebastian.damrich@mila.quebec

Sebastian Damrich

Collaborateur·rice de recherche - University of Tübingen

Maxine Dussault

Maîtrise recherche - UdeM

Co-superviseur⋅e :

Margarida Carvalho

maxine.dussault@mila.quebec

Stefan Horoi

Doctorat - UdeM

stefan.horoi@mila.quebec

Xiao Huang

Maîtrise recherche - Concordia

Superviseur⋅e principal⋅e :

xiaolong.huang@mila.quebec

Collaborateur·rice alumni - UdeM

guillaume.huguet@mila.quebec

Paul Janson

Doctorat - Concordia

Superviseur⋅e principal⋅e :

myriam.lizotte@mila.quebec

paul.janson@mila.quebec

Doctorat - UdeM

Gabriele Malagoli

Visiteur de recherche indépendant - Helmholtz Munich

gabriele.malagoli@mila.quebec

Philippe Martin

Doctorat - UdeM

Co-superviseur⋅e :

Paul François

philippe.martin@mila.quebec

Paria Mehrbod

Maîtrise recherche - Concordia

Superviseur⋅e principal⋅e :

paria.mehrbod@mila.quebec

Lydia Mezrag

Doctorat - UdeM

lydia.mezrag@mila.quebec

Kevin Moon

Collaborateur·rice de recherche

Sacha Morin

Doctorat - UdeM

Co-superviseur⋅e :

Liam Paull

sacha.morin@mila.quebec

Postdoctorat - Concordia

Superviseur⋅e principal⋅e :

geraldin.nanfack@mila.quebec

Shuang Ni

Doctorat - UdeM

shuang.ni@mila.quebec

Albert Orozco Camacho

Doctorat - Concordia

Superviseur⋅e principal⋅e :

Jake Rhodes

Collaborateur·rice de recherche - BYU

rhodes@stat.byu.edu

thomas.sabourin@mila.quebec

Thomas Sabourin

Maîtrise recherche - UdeM

matthew.scicluna@mila.quebec

Matthew Scicluna

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Julie Hussin

francisco.jose.tellez@mila.quebec

Simeon Tchoffo

Doctorat - UdeM

simeontchoffo@gmail.com

Francisco Tellez

Maîtrise recherche - UdeM

Jesuino Vieira Filho

Maîtrise recherche - UdeM

Collaborateur·rice alumni - UdeM

Co-superviseur⋅e :

yanlei.zhang@mila.quebec

stephanie.zandee@mcgill.ca

Stephanie Zandee

Collaborateur·rice de recherche - McGill (assistant professor)

Analyser le paradoxe des interférons inhérent à la COVID-19 au moyen de la réduction de la dimensionnalité et du regroupement

Billets de blogue

Graph and representation of working methodology, and graph of data on deaths 60 days after onset of symptoms.

19 février 2025

par

Sacha Morin

Elsa Brunet-Ratnasingham

Guy Wolf

Lire l'article

Publications

Diffusion Earth Mover's Distance and Distribution Embeddings

Guillaume Huguet

Amine Natik

Kincaid MacDonald

Manik Kuchroo

Ronald R. Coifman

We propose a new fast method of measuring distances between large numbers of related high dimensional datasets called the Diffusion Earth Mo… (voir plus)ver's Distance (EMD). We model the datasets as distributions supported on common data graph that is derived from the affinity matrix computed on the combined data. In such cases where the graph is a discretization of an underlying Riemannian closed manifold, we prove that Diffusion EMD is topologically equivalent to the standard EMD with a geodesic ground distance. Diffusion EMD can be computed in

2021-07-19

International Conference on Machine Learning (Spotlight)

proceedings.mlr.press

Random Forest-Based Diffusion Information Geometry for Supervised Visualization and Data Exploration

Jake S. Rhodes

Adele Cutler

Kevin R. Moon

Most dimensionality reduction techniques to date are unsupervised; they do not take class labels into account (e.g., PCA, MDS, t-SNE, Isomap… (voir plus)). Such methods require large amounts of data and are often sensitive to noise that may obfuscate important patterns in the data. Various attempts at supervised dimensionality reduction methods that take into account auxiliary annotations (e.g., class labels) have been successfully implemented with goals of increased classification accuracy or improved data visualization. In this paper, we describe a novel supervised visualization technique based on random forest proximities and a diffusion-based information geometry. We show, both qualitatively and quantitatively, the advantages of our approach in retaining local and global structure in data, while demonstrating the spatial relevance of features important for the supervised task. Importantly, our approach is robust to noise and parameter tuning, thus making it simple to use while producing reliable visualizations for data exploration.

2021-07-10

Symposium on Software Performance (publié)

Exploring the Geometry and Topology of Neural Network Loss Landscapes

Bastian Rieck

Recent work has established clear links between the generalization performance of trained neural networks and the geometry of their loss lan… (voir plus)dscape near the local minima to which they converge. This suggests that qualitative and quantitative examination of the loss landscape geometry could yield insights about neural network generalization performance during training. To this end, researchers have proposed visualizing the loss landscape through the use of simple dimensionality reduction techniques. However, such visualization methods have been limited by their linear nature and only capture features in one or two dimensions, thus restricting sampling of the loss landscape to lines or planes. Here, we expand and improve upon these in three ways. First, we present a novel "jump and retrain" procedure for sampling relevant portions of the loss landscape. We show that the resulting sampled data holds more meaningful information about the network's ability to generalize. Next, we show that non-linear dimensionality reduction of the jump and retrain trajectories via PHATE, a trajectory and manifold-preserving method, allows us to visualize differences between networks that are generalizing well vs poorly. Finally, we combine PHATE trajectories with a computational homology characterization to quantify trajectory differences.

2021-01-30

arXiv (prépublication)

Topological analysis of single-cell data reveals shared glial landscape of macular degeneration and neurodegenerative diseases

Manik Kuchroo

Marcello DiStasio

Eda Calapkulu

Maryam Ige

Le Zhang

Amar H. Sheth

Madhvi Menon

Yu Xing

Scott Gigante

Jessie Huang

Rahul M. Dhodapkar

Bastian Rieck

Brian P. Hafler

1 A novel topological machine learning approach applied to single-nucleus RNA sequencing from human retinas… (voir plus) with age-related macular degeneration identifies interacting disease phase-specific glial activation states shared with Alzheimer’s disease and multiple sclerosis. 2 Neurodegeneration occurs in a wide range of diseases, including age-related macular degeneration (AMD), Alzheimer’s disease (AD), and multiple sclerosis (MS), each with distinct inciting events. To determine whether glial transcriptional states are shared across phases of degeneration, we sequenced 50,498 nuclei from the retinas of seven AMD patients and six healthy controls, generating the first single-cell transcriptomic atlas of AMD. We identified groupings of cells implicated in disease pathogenesis by applying a novel topologically-inspired machine learning approach called ‘diffusion condensation.’ By calculating diffusion homology features and performing persistence analysis, diffusion condensation identified activated glial states enriched in the early phases of AMD, AD, and MS as well as an AMD-specific proangiogenic astrocyte state promoting pathogenic neovascularization in advanced AMD. Finally, by mapping the expression of disease-associated genes to glial states, we identified key signaling interactions creating hypotheses for therapeutic intervention. Our topological analysis identified an integrated disease-phase specific glial landscape that is shared across neurodegenerative conditions affecting the central nervous system.

2021-01-19

bioRxiv (prépublication)

Finding Archetypal Spaces Using Neural Networks

David van Dijk

Daniel B. Burkhardt

Matthew Amodio

Archetypal analysis is a data decomposition method that describes each observation in a dataset as a convex combination of "pure types" or a… (voir plus)rchetypes. These archetypes represent extrema of a data space in which there is a trade-off between features, such as in biology where different combinations of traits provide optimal fitness for different environments. Existing methods for archetypal analysis work well when a linear relationship exists between the feature space and the archetypal space. However, such methods are not applicable to systems where the feature space is generated non-linearly from the combination of archetypes, such as in biological systems or image transformations. Here, we propose a reformulation of the problem such that the goal is to learn a non-linear transformation of the data into a latent archetypal space. To solve this problem, we introduce Archetypal Analysis network (AAnet), which is a deep neural network framework for learning and generating from a latent archetypal representation of data. We demonstrate state-of-the-art recovery of ground-truth archetypes in non-linear data domains, show AAnet can generate from data geometry rather than from data density, and use AAnet to identify biologically meaningful archetypes in single-cell gene expression data.

2020-12-31

IEEE International Conference on Big Data (inconnu)

Hierarchical Graph Neural Nets Can Capture Long-Range Interactions

Ladislav Rampasek

Graph neural networks (GNNs) based on message passing between neighboring nodes are known to be insufficient for capturing long-range intera… (voir plus)ctions in graphs. In this project we study hierarchical message passing models that leverage a multi-resolution representation of a given graph. This facilitates learning of features that span large receptive fields without loss of local information, an aspect not studied in preceding work on hierarchical GNNs. We introduce Hierarchical Graph Net (HGNet), which for any two connected nodes guarantees existence of message-passing paths of at most logarithmic length w.r.t. the input graph size. Yet, under mild assumptions, its internal hierarchy maintains asymptotic size equivalent to that of the input graph. We observe that our HGNet outperforms conventional stacking of GCN layers particularly in molecular property prediction benchmarks. Finally, we propose two benchmarking tasks designed to elucidate capability of GNNs to leverage long-range interactions in graphs.

2020-12-31

arXiv (prépublication)

MURAL: An Unsupervised Random Forest-Based Embedding for Electronic Health Record Data

Michal Gerasimiuk

Dennis Shung

Adrian Stanley

Michael Schultz

Jeffrey Ngu

Loren Laine

A major challenge in embedding or visualizing clinical patient data is the heterogeneity of variable types including continuous lab values, … (voir plus)categorical diagnostic codes, as well as missing or incomplete data. In particular, in EHR data, some variables are {\em missing not at random (MNAR)} but deliberately not collected and thus are a source of information. For example, lab tests may be deemed necessary for some patients on the basis of suspected diagnosis, but not for others. Here we present the MURAL forest -- an unsupervised random forest for representing data with disparate variable types (e.g., categorical, continuous, MNAR). MURAL forests consist of a set of decision trees where node-splitting variables are chosen at random, such that the marginal entropy of all other variables is minimized by the split. This allows us to also split on MNAR variables and discrete variables in a way that is consistent with the continuous variables. The end goal is to learn the MURAL embedding of patients using average tree distances between those patients. These distances can be fed to nonlinear dimensionality reduction method like PHATE to derive visualizable embeddings. While such methods are ubiquitous in continuous-valued datasets (like single cell RNA-sequencing) they have not been used extensively in mixed variable data. We showcase the use of our method on one artificial and two clinical datasets. We show that using our approach, we can visualize and classify data more accurately than competing approaches. Finally, we show that MURAL can also be used to compare cohorts of patients via the recently proposed tree-sliced Wasserstein distances.

2020-12-31

IEEE International Conference on Big Data (inconnu)

Topological Analysis of Single-Cell Hierarchy Reveals Inflammatory Glial Landscape of Macular Degeneration

Manik Kuchroo

Marcello DiStasio

Eric Song

Eda Calapkulu

Le Zhang

Maryam Ige

Amar H. Sheth

Madhvi Menon

Abhinav Godavarthi

Yu Xing

Scott Gigante

Holly Steach

Jessie Huang

Guillaume Huguet

Janhavi Narain

George Mourgkos

Rahul M. Dhodapkar

Matthew J. Hirn

Bastian Rieck … (voir 3 de plus)

Brian P. Hafler

2020-12-31

SSRN Electronic Journal (inconnu)

Topological Analysis of Single-Cell Hierarchy Reveals Inflammatory Glial Landscape of Macular Degeneration

Manik Kuchroo

Marcello DiStasio

Eric Song

Eda Calapkulu

Le Zhang

Maryam Ige

Amar H. Sheth

Madhvi Menon

Abhinav Godavarthi

Yu Xing

Scott Gigante

Holly Steach

Jessie Huang

Guillaume Huguet

Janhavi Narain

George Mourgkos

Rahul M. Dhodapkar

Matthew J. Hirn

Bastian Rieck … (voir 3 de plus)

Brian P. Hafler

2020-12-31

SSRN Electronic Journal (publié)

Uncovering the Folding Landscape of RNA Secondary Structure Using Deep Graph Embeddings

Egbert Castro

Andrew Benz

Biomolecular graph analysis has recently gained much attention in the emerging field of geometric deep learning. Here we focus on organizing… (voir plus) biomolecular graphs in ways that expose meaningful relations and variations between them. We propose a geometric scattering autoencoder (GSAE) network for learning such graph embeddings. Our embedding network first extracts rich graph features using the recently proposed geometric scattering transform. Then, it leverages a semi-supervised variational autoencoder to extract a low-dimensional embedding that retains the information in these features that enable prediction of molecular properties as well as characterize graphs. We show that GSAE organizes RNA graphs both by structure and energy, accurately reflecting bistable RNA structures. Also, the model is generative and can sample new folding trajectories.

2020-12-31

IEEE International Conference on Big Data (inconnu)

Extendable and invertible manifold learning with geometry regularized autoencoders

Andrés F. Duque

Sacha Morin

Kevin Moon

A fundamental task in data exploration is to extract simplified low dimensional representations that capture intrinsic geometry in data, esp… (voir plus)ecially for faithfully visualizing data in two or three dimensions. Common approaches to this task use kernel methods for manifold learning. However, these methods typically only provide an embedding of fixed input data and cannot extend to new data points. Autoencoders have also recently become popular for representation learning. But while they naturally compute feature extractors that are both extendable to new data and invertible (i.e., reconstructing original features from latent representation), they have limited capabilities to follow global intrinsic geometry compared to kernel-based manifold learning. We present a new method for integrating both approaches by incorporating a geometric regularization term in the bottleneck of the autoencoder. Our regularization, based on the diffusion potential distances from the recently-proposed PHATE visualization method, encourages the learned latent representation to follow intrinsic data geometry, similar to manifold learning algorithms, while still enabling faithful extension to new data and reconstruction of data in the original feature space from latent coordinates. We compare our approach with leading kernel methods and autoencoder models for manifold learning to provide qualitative and quantitative evidence of our advantages in preserving intrinsic structure, out of sample extension, and reconstruction. Our method is easily implemented for big-data applications, whereas other methods are limited in this regard.

2020-12-09

2020 IEEE International Conference on Big Data (Big Data) (publié)

Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networks

Yimeng Min

Frederik Wenkel

Graph convolutional networks (GCNs) have shown promising results in processing graph data by extracting structure-aware features. This gave … (voir plus)rise to extensive work in geometric deep learning, focusing on designing network architectures that ensure neuron activations conform to regularity patterns within the input graph. However, in most cases the graph structure is only accounted for by considering the similarity of activations between adjacent nodes, which limits the capabilities of such methods to discriminate between nodes in a graph. Here, we propose to augment conventional GCNs with geometric scattering transforms and residual convolutions. The former enables band-pass filtering of graph signals, thus alleviating the so-called oversmoothing often encountered in GCNs, while the latter is introduced to clear the resulting features of high-frequency noise. We establish the advantages of the presented Scattering GCN with both theoretical results establishing the complementary benefits of scattering and GCN features, as well as experimental results showing the benefits of our method compared to leading graph neural networks for semi-supervised node classification, including the recently proposed GAT network that typically alleviates oversmoothing using graph attention mechanisms.

2020-12-06

Conference on Neural Information Processing Systems (Accept (poster))