Guy Wolf

Biography

Guy Wolf is a Full Professor in the Department of Mathematics and Statistics (DMS) at the Université de Montréal (UdeM), a Canada CIFAR AI Chair & Core Academic Member at Mila (the Quebec AI institute), an Associate Researcher with CRCHUM (the Montreal university hospital research center), and a participating PI in the Helmholtz International Lab for Causal Cell Dynamics.

In 2024 he has been awarded a Humboldt Experienced Research Fellowship, as part of which he was a visiting professor at Heidelberg University (2024) and Helmholtz Munich (2024-2026) in Germany. Prior to joining UdeM and Mila, he was a Gibbs Assistant Professor (2015-2018) in the Applied Math Program and an Associate Research Scientist in the Department of Genetics (2018) at Yale University (CT, USA). Previously, he was a Postdoctoral Researcher (2013-2015) in the Department of Computer Science at École Normale Supérieure in Paris (France). He holds a Ph.D. in Computer Science from Tel Aviv University (Israel), and has five years of prior experience in IT software design & development for data analysis in military settings.

His current research focuses on guided representation learning for data exploration, including methods that leverage manifold learning and geometric deep learning for dimensionality reduction, visualization, denoising, data augmentation, and coarse graining. While relevant for a wide range of applications, he is particularly interested in the intersection of AI & health, including tools supporting exploratory analysis of biomedical data, e.g., in single-cell multiomics, drug discovery, and neuroscience.

Current Students

Adrien Aumon

PhD - Université de Montréal

adrien.aumon@mila.quebec

Semih Cantürk

PhD - Université de Montréal

semihcanturk00@gmail.com

Joao Felipe Carneiro Barbosa Rocha

Collaborating researcher - Yale University

Co-supervisor :

Smita Krishnaswamy

joaofelipe.rocha@mila.quebec

sebastian.damrich@mila.quebec

Sebastian Damrich

Collaborating researcher - University of Tübingen

Maxine Dussault

Master's Research - Université de Montréal

Co-supervisor :

Margarida Carvalho

maxine.dussault@mila.quebec

Stefan Horoi

PhD - Université de Montréal

stefan.horoi@mila.quebec

Master's Research - Concordia University

Principal supervisor :

xiaolong.huang@mila.quebec

Collaborating Alumni - Université de Montréal

guillaume.huguet@mila.quebec

Paul Janson

PhD - Concordia University

Principal supervisor :

myriam.lizotte@mila.quebec

paul.janson@mila.quebec

PhD - Université de Montréal

Gabriele Malagoli

Independent visiting researcher - Helmholtz Munich

gabriele.malagoli@mila.quebec

Philippe Martin

PhD - Université de Montréal

Co-supervisor :

Paul François

philippe.martin@mila.quebec

Paria Mehrbod

Master's Research - Concordia University

Principal supervisor :

paria.mehrbod@mila.quebec

Lydia Mezrag

PhD - Université de Montréal

lydia.mezrag@mila.quebec

Kevin Moon

Collaborating researcher

Sacha Morin

PhD - Université de Montréal

Co-supervisor :

Liam Paull

sacha.morin@mila.quebec

Postdoctorate - Concordia University

Principal supervisor :

geraldin.nanfack@mila.quebec

Shuang Ni

PhD - Université de Montréal

shuang.ni@mila.quebec

Albert Orozco Camacho

PhD - Concordia University

Principal supervisor :

Jake Rhodes

Collaborating researcher - BYU

rhodes@stat.byu.edu

thomas.sabourin@mila.quebec

Thomas Sabourin

PhD - Université de Montréal

johannes.schmidt@mila.quebec

Johannes Schmidt

Independent visiting researcher - University of Fribourg

PhD - Université de Montréal

Principal supervisor :

Julie Hussin

matthew.scicluna@mila.quebec

Ammar Sherif

PhD - Concordia University

Principal supervisor :

francisco.jose.tellez@mila.quebec

ammar.sherif@mila.quebec

Simeon Tchoffo

PhD - Université de Montréal

simeontchoffo@gmail.com

Francisco Tellez

Master's Research - Université de Montréal

César Miguel Valdez Cordova

PhD - McGill University

Principal supervisor :

Mathieu Blanchette

cesar.valdez@mila.quebec

Master's Research - Université de Montréal

Collaborating Alumni - Université de Montréal

Co-supervisor :

yanlei.zhang@mila.quebec

Exploring the COVID-19 Interferon Paradox with Dimensionality Reduction and Clustering

Blog Posts

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

February 19, 2025

Sacha Morin

Elsa Brunet-Ratnasingham

Guy Wolf

Read the article

Publications

Spectral Temporal Contrastive Learning

Sacha Morin

Somjit Nath

Samira Ebrahimi Kahou

Learning useful data representations without requiring labels is a cornerstone of modern deep learning. Self-supervised learning methods, pa… (see more)rticularly contrastive learning (CL), have proven successful by leveraging data augmentations to define positive pairs. This success has prompted a number of theoretical studies to better understand CL and investigate theoretical bounds for downstream linear probing tasks. This work is concerned with the temporal contrastive learning (TCL) setting where the sequential structure of the data is used instead to define positive pairs, which is more commonly used in RL and robotics contexts. In this paper, we adapt recent work on Spectral CL to formulate Spectral Temporal Contrastive Learning (STCL). We discuss a population loss based on a state graph derived from a time-homogeneous reversible Markov chain with uniform stationary distribution. The STCL loss enables to connect the linear probing performance to the spectral properties of the graph, and can be estimated by considering previously observed data sequences as an ensemble of MCMC chains.

2023-11-30

arXiv (preprint)

arxiv.org

Channel Selection for Test-Time Adaptation Under Distribution Shift

Muawiz Sajjad Chaudhary

An Tang

Guy Cloutier

Michael Eickenberg

To ensure robustness and generalization to real-world scenarios, test-time adaptation has been recently studied as an approach to adjust mod… (see more)els to a new data distribution during inference. Test-time batch normalization is a simple and popular method that achieved compelling performance on domain shift benchmarks by recalculating batch normalization statistics on test batches. However, in many practical applications this technique is vulnerable to label distribution shifts. We propose to tackle this challenge by only selectively adapting channels in a deep network, minimizing drastic adaptation that is sensitive to label shifts. We find that adapted models significantly improve the performance compared to the baseline models and counteract unknown label shifts.

2023-10-26

NeurIPS.cc/2023/Workshop/DistShift (poster)

Comparison of Radiologists and Deep Learning for US Grading of Hepatic Steatosis

Sara‐Ivana Calce

Pamela Boustros

Cassandra Larocque-Rigney

Laurent Patry-Beaudoin

Yi Hui Luo

Emre Aslan

John Marinos

Talal Alamri

Kim‐Nhien Vu

Jessica Murphy-Lavallée

Jean-Sébastien Billiard

Emmanuel Montagnon

Hongliang Li

Samuel Kadoury

Bich Nguyen

Shanel Gauthier

Benjamin Therien

Irina Rish

Eugene Belilovsky … (see 4 more)

Michaël Chassé

Guy Cloutier

An Tang

Background Screening for nonalcoholic fatty liver disease (NAFLD) is suboptimal due to the subjective interpretation of US images. Purpose T… (see more)o evaluate the agreement and diagnostic performance of radiologists and a deep learning model in grading hepatic steatosis in NAFLD at US, with biopsy as the reference standard. Materials and Methods This retrospective study included patients with NAFLD and control patients without hepatic steatosis who underwent abdominal US and contemporaneous liver biopsy from September 2010 to October 2019. Six readers visually graded steatosis on US images twice, 2 weeks apart. Reader agreement was assessed with use of κ statistics. Three deep learning techniques applied to B-mode US images were used to classify dichotomized steatosis grades. Classification performance of human radiologists and the deep learning model for dichotomized steatosis grades (S0, S1, S2, and S3) was assessed with area under the receiver operating characteristic curve (AUC) on a separate test set. Results The study included 199 patients (mean age, 53 years ± 13 [SD]; 101 men). On the test set (n = 52), radiologists had fair interreader agreement (0.34 [95% CI: 0.31, 0.37]) for classifying steatosis grades S0 versus S1 or higher, while AUCs were between 0.49 and 0.84 for radiologists and 0.85 (95% CI: 0.83, 0.87) for the deep learning model. For S0 or S1 versus S2 or S3, radiologists had fair interreader agreement (0.30 [95% CI: 0.27, 0.33]), while AUCs were between 0.57 and 0.76 for radiologists and 0.73 (95% CI: 0.71, 0.75) for the deep learning model. For S2 or lower versus S3, radiologists had fair interreader agreement (0.37 [95% CI: 0.33, 0.40]), while AUCs were between 0.52 and 0.81 for radiologists and 0.67 (95% CI: 0.64, 0.69) for the deep learning model. Conclusion Deep learning approaches applied to B-mode US images provided comparable performance with human readers for detection and grading of hepatic steatosis. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Tuthill in this issue.

2023-09-30

Radiology (unknown)

F66. FROM GENE TO COGNITION: MAPPING THE EFFECTS OF GENOMIC DELETIONS AND DUPLICATIONS ON COGNITIVE ABILITY

Sayeh Kazem

Kuldeep Kumar

Thomas Renne

Martineau Jean-Louis

Zohra Saci

Laura Almasy

David Glahn

Guillaume Dumas

Sébastien Jacquemont

2023-09-30

European Neuropsychopharmacology (published)

Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets (Ultra Large Dataset)

Dominique Beaini

Shenyang Huang

Joao Alex Cunha

Zhiyi Li

Gabriela Moisescu-Pareja

Oleksandr Dymov

Samuel Maddrell-Mander

Callum McLean

Ali Parviz

Luis T. Díaz Müller

Jama Hussein Mohamud

Michael Craig

Cristian Gabellini

Jian Tang … (see 8 more)

Christopher G. Morris

Mirco Ravanelli

Prudencio Tossou

Hadrien Mary

Błażej Banaszewski

Chad Martin

Dominic Masters

2023-09-21

Zenodo (unknown)

Automated liver segmentation and steatosis grading using deep learning on B-mode ultrasound images

Merve Kulbay

Pamela Boustros

Sara‐Ivana Calce

Cassandra Larocque-Rigney

Laurent Patry-Beaudoin

Yi Hui Luo

M.A. Chaudary

Samuel Kadoury

Bich Nguyen

Emmanuel Montagnon

Michaël Chassé

An Tang

Guy Cloutier

Early detection of nonalcoholic fatty liver disease (NAFLD) is crucial to avoid further complications. Ultrasound is often used for screenin… (see more)g and monitoring of hepatic steatosis, however it is limited by the subjective interpretation of images. Computer assisted diagnosis could aid radiologists to achieve objective grading, and artificial intelligence approaches have been tested across various medical applications. In this study, we evaluated the performance of a two-stage hepatic steatosis detection deep learning framework, with a first step of liver segmentation and a subsequent step of hepatic steatosis classification. We evaluated the models on internal and external datasets, aiming to understand the generalizability of the framework. In the external dataset, our segmentation model achieved a Dice score of 0.92 (95% CI: 0.78, 1.00), and our classification model achieved an area under the receiver operating characteristic curve of 0.84 (95% CI: 0.79, 0.89). Our findings highlight the potential benefits of applying artificial intelligence models in NAFLD assessment.

2023-09-02

IEEE International Ultrasonics Symposium (published)

Automated liver segmentation and steatosis grading using deep learning on B-mode ultrasound images

Merve Kulbay

Pamela Boustros

Sara-Ivana Calce

Cassandra Larocque-Rigney

Laurent Patry-Beaudoin

Yi Hui Luo

Muawiz Chaudary

Samuel Kadoury

Bich Nguyen

Emmanuel Montagnon

Michaël Chassé

An Tang

Guy Cloutier

2023-09-02

IUS (published)

Neural FIM for learning Fisher information metrics from point cloud data

Oluwadamilola Fasina

Guillaume Huguet

Alexander Tong

Yanlei Zhang

Maximilian Nickel

Ian Adelstein

Smita Krishnaswamy

Although data diffusion embeddings are ubiquitous in unsupervised learning and have proven to be a viable technique for uncovering the under… (see more)lying intrinsic geometry of data, diffusion embeddings are inherently limited due to their discrete nature. To this end, we propose neural FIM, a method for computing the Fisher information metric (FIM) from point cloud data - allowing for a continuous manifold model for the data. Neural FIM creates an extensible metric space from discrete point cloud data such that information from the metric can inform us of manifold characteristics such as volume and geodesics. We demonstrate Neural FIM's utility in selecting parameters for the PHATE visualization method as well as its ability to obtain information pertaining to local volume illuminating branching points and cluster centers embeddings of a toy dataset and two single-cell datasets of IPSC reprogramming and PBMCs (immune cells).

2023-07-24

International Conference on Machine Learning (Accept (Poster))

proceedings.mlr.press

Data Imputation with an Autoencoder and MAGIC

Devin Eddington

Andres Felipe Duque Correa

Kevin R. Moon

Missing data is a common problem in many applications. Imputing missing values is a challenging task, as the imputations need to be accurate… (see more) and robust to avoid introducing bias in downstream analysis. In this paper, we propose an ensemble method that combines the strengths of a manifold learning-based imputation method called MAGIC and an autoencoder deep learning model. We call our method Deep MAGIC. Deep MAGIC is trained on a linear combination of the mean squared error of the original data and the mean squared error of the MAGIC-imputed data. Experimental results on three benchmark datasets show that Deep MAGIC outperforms several state-of-the-art imputation methods, demonstrating its effectiveness and robustness in handling large amounts of missing data.

2023-07-09

International Conference on Sampling Theory and Applications (published)

Graph Fourier MMD for Signals on Graphs

Samuel Leone

Aarthi Venkat

While numerous methods have been proposed for computing distances between probability distributions in Euclidean space, relatively little at… (see more)tention has been given to computing such distances for distributions on graphs. However, there has been a marked increase in data that either lies on graph (such as protein interaction networks) or can be modeled as a graph (single cell data), particularly in the biomedical sciences. Thus, it becomes important to find ways to compare signals defined on such graphs. Here, we propose Graph Fourier MMD (GFMMD), a novel distance between distributions and signals on graphs. GFMMD is defined via an optimal witness function that is both smooth on the graph and maximizes difference in expectation between the pair of distributions on the graph. We find an analytical solution to this optimization problem as well as an embedding of distributions that results from this method. We also prove several properties of this method including scale invariance and applicability to disconnected graphs. We showcase it on graph benchmark datasets as well on single cell RNA-sequencing data analysis. In the latter, we use the GFMMD-based gene embeddings to find meaningful gene clusters. We also propose a novel type of score for gene selection called "gene localization score" which helps select genes for cellular state space characterization.

2023-07-09

2023 International Conference on Sampling Theory and Applications (SampTA) (published)

Manifold Alignment with Label Information

Andres F. Duque Correa

Myriam Lizotte

Kevin R. Moon

Multi-domain data is becoming increasingly common and presents both challenges and opportunities in the data science community. The integrat… (see more)ion of distinct data-views can be used for exploratory data analysis, and benefit downstream analysis including machine learning related tasks. With this in mind, we present a novel manifold alignment method called MALI (Manifold alignment with label information) that learns a correspondence between two distinct domains. MALI belongs to a middle ground between the more commonly addressed semi-supervised manifold alignment, where some known correspondences between the two domains are assumed to be known beforehand, and the purely unsupervised case, where no information linking both domains is available. To do this, MALI learns the manifold structure in both domains via a diffusion process and then leverages discrete class labels to guide the alignment. MALI recovers a pairing and a common representation that reveals related samples in both domains. We show that MALI outperforms the current state-of-the-art manifold alignment methods across multiple datasets.

2023-07-09

2023 International Conference on Sampling Theory and Applications (SampTA) (published)

Pretrained Language Models to Solve Graph Tasks in Natural Language

Frederik Wenkel

Boris Knyazev

Pretrained large language models (LLMs) are powerful learners in a variety of language tasks. We explore if LLMs can learn from graph-struct… (see more)ured data when the graphs are described using natural language. We explore data augmentation and pretraining specific to the graph domain and show that LLMs such as GPT-2 and GPT-3 are promising alternatives to graph neural networks.

2023-06-18

ICML.cc/2023/Workshop/SPIGM (poster)