Portrait of Guy Wolf

Guy Wolf

Core Academic Member
wolfguy@mila.quebec
Canada CIFAR AI Chair
Associate Professor, Université de Montréal, Department of Mathematics and Statistics
Concordia University
CHUM - Montreal University Hospital Center
Website
Google Scholar

Biography

Guy Wolf is an associate professor in the Department of Mathematics and Statistics at Université de Montréal.

His research interests lie at the intersection of machine learning, data science and applied mathematics. He is particularly interested in data mining methods that use manifold learning and deep geometric learning, as well as applications for the exploratory analysis of biomedical data.

Wolf’s research focuses on exploratory data analysis and its applications in bioinformatics. His approaches are multidisciplinary and bring together machine learning, signal processing and applied math tools. His recent work has used a combination of diffusion geometries and deep learning to find emergent patterns, dynamics, and structure in big high dimensional- data (e.g., in single-cell genomics and proteomics).

Current Students

Adrien Aumon
PhD - Université de Montréal
adrien.aumon@mila.quebec
Albert Orozco Camacho
PhD - Concordia University
Principal supervisor :
Eugene Belilovsky
orozcoca@mila.quebec
Website
Github
Google Scholar
Amine Natik
PhD - Université de Montréal
Principal supervisor :
Guillaume Lajoie
natikami@mila.quebec
Charles-Etienne Joseph
Master's Research - Université de Montréal
Principal supervisor :
Eugene Belilovsky
charles-etienne.joseph@mila.quebec
Github
Will Hua
Master's Research - McGill University
Principal supervisor :
Doina Precup
chenqing.hua@mila.quebec
Donald Shenaj
Collaborating researcher - Concordia University
Principal supervisor :
Eugene Belilovsky
donald.shenaj@mila.quebec
Website
Github
Google Scholar
Edouard Oyallon
Independent visiting researcher
edouard.oyallon@mila.quebec
Website
Github
Google Scholar
Geraldin Nanfack
Postdoctorate - Concordia University
Principal supervisor :
Eugene Belilovsky
geraldin.nanfack@mila.quebec
Guillaume Huguet
PhD - Université de Montréal
guillaume.huguet@mila.quebec
Github
Google Scholar
Hasti Nafisi
Master's Research - Université de Montréal
Co-supervisor :
Yashar Hezaveh
hasti.nafisi@mila.quebec
Jake Kovalic
Collaborating researcher - Yale
jake.kovalic@mila.quebec
Github
Frederik Wenkel
PhD - Université de Montréal
frederik.wenkel@mila.quebec
Website
Github
Google Scholar
Lydia Mezrag
PhD - Université de Montréal
lydia.mezrag@mila.quebec
Mehrbod Paria
Master's Research - Concordia University
Principal supervisor :
Eugene Belilovsky
paria.mehrbod@mila.quebec
Muawiz Chaudhary
Collaborating Alumni
muawiz.chaudhary@mila.quebec
Github
Google Scholar
Myriam Lizotte
PhD - Université de Montréal
myriam.lizotte@mila.quebec
Nader Asadi
Collaborating Alumni
Principal supervisor :
Eugene Belilovsky
nader.asadi@mila.quebec
Website
Github
Google Scholar
Pedro Vianna
Collaborating researcher - Université de Montréal
Co-supervisor :
Eugene Belilovsky
pedro.vianna@mila.quebec
Github
Google Scholar
Ria Arora
Master's Research - Université de Montréal
Co-supervisor :
Liam Paull
ria.arora@mila.quebec
Github
Sacha Morin
PhD - Université de Montréal
Co-supervisor :
Liam Paull
sacha.morin@mila.quebec
Semih Cantürk
PhD - Université de Montréal
semih.canturk@mila.quebec
Website
Github
Google Scholar
Shuang Ni
PhD - Université de Montréal
shuang.ni@mila.quebec
Website
Github
Siddharth Viswanath
Collaborating researcher - Yale
siddarth.viswanath@mila.quebec
Stefan Horoi
PhD - Université de Montréal
stefan.horoi@mila.quebec
Website
Github
Google Scholar
Vincent Létourneau
Postdoctorate - Université de Montréal
letournv@mila.quebec
Vivian White
Research Intern - Western Washington University
Principal supervisor :
Guillaume Lajoie
vivian.white@mila.quebec
Github
Zhang Yanlei
Postdoctorate - Université de Montréal
yanlei.zhang@mila.quebec
Website
Google Scholar

Publications

Genomic epidemiology and associated clinical outcomes of a SARS-CoV-2 outbreak in a general adult hospital in Quebec
Bastien Paré
Marieke Rozendaal
Sacha Morin
Raphael Poujol
Fatima Mostefai
Shawn M. Simpson
Jean-Christophe Grenier
Léa Kaufmann
Henry Xing
Miguelle Sanchez
Ariane Yechouron
Ronald Racette
Julie Hussin
Guy Wolf
Ivan Pavlov
Martin Smith
2021-12-02
PLoS ONE (published)
doi.org
Patient health records and whole viral genomes from an early SARS-CoV-2 outbreak in a Quebec hospital reveal features associated with favorable outcomes
Bastien Paré
Marieke Rozendaal
Sacha Morin
Léa Kaufmann
Shawn M. Simpson
Raphael Poujol
Fatima Mostefai
Jean-Christophe Grenier
Henry Xing
Miguelle Sanchez
Ariane Yechouron
Ronald Racette
Julie Hussin
Guy Wolf
Ivan Pavlov
Martin Smith
2021-12-02
PLoS ONE (published)
doi.org
Fixing Bias in Reconstruction-based Anomaly Detection with Lipschitz Discriminators
Alexander Tong
Guy Wolf
Smita Krishnaswamy
Anomaly detection is of great interest in fields where abnormalities need to be identified and corrected (e.g., medicine and finance). Deep … (see more)learning methods for this task often rely on autoencoder reconstruction error, sometimes in conjunction with other penalties. We show that this approach exhibits intrinsic biases that lead to undesirable results. Reconstruction-based methods can sometimes show low error on simple-to-reconstruct points that are not part of the training data, for example the all black image. Instead, we introduce a new unsupervised Lipschitz anomaly discriminator (LAD) that does not suffer from these biases. Our anomaly discriminator is trained, similar to the discriminator of a GAN, to detect the difference between the training data and corruptions of the training data. We show that this procedure successfully detects unseen anomalies with guarantees on those that have a certain Wasserstein distance from the data or corrupted training set. These additions allow us to show improved performance on MNIST, CIFAR10, and health record data. Further, LAD does not require decoding back to the original data space, which makes anomaly detection possible in domains where it is difficult to define a decoder, such as in irregular graph structured data. Empirically, we show this framework leads to improved performance on image, health record, and graph data.
2021-11-28
Journal of Signal Processing Systems (published)
doi.org
Data-driven approaches for genetic characterization of SARS-CoV-2 lineages
Fatima Mostefai
Isabel Gamache
Jessie Huang
Arnaud N’Guessan
Justin Pelletier
Ahmad Pesaranghader
David J. Hamelin
Carmen Lia Murall
Raphael Poujol
Jean-Christophe Grenier
Martin Smith
Etienne Caron
Morgan Craig
Jesse Shapiro
Guy Wolf
Smita Krishnaswamy
Julie Hussin
The genome of the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), the pathogen that causes coronavirus disease 2019 (COVID-19)… (see more), has been sequenced at an unprecedented scale, leading to a tremendous amount of viral genome sequencing data. To understand the evolution of this virus in humans, and to assist in tracing infection pathways and designing preventive strategies, we present a set of computational tools that span phylogenomics, population genetics and machine learning approaches. To illustrate the utility of this toolbox, we detail an in depth analysis of the genetic diversity of SARS-CoV-2 in first year of the COVID-19 pandemic, using 329,854 high-quality consensus sequences published in the GISAID database during the pre-vaccination phase. We demonstrate that, compared to standard phylogenetic approaches, haplotype networks can be computed efficiently on much larger datasets, enabling real-time analyses. Furthermore, time series change of Tajima’s D provides a powerful metric of population expansion. Unsupervised learning techniques further highlight key steps in variant detection and facilitate the study of the role of this genomic variation in the context of SARS-CoV-2 infection, with Multiscale PHATE methodology identifying fine-scale structure in the SARS-CoV-2 genetic data that underlies the emergence of key lineages. The computational framework presented here is useful for real-time genomic surveillance of SARS-CoV-2 and could be applied to any pathogen that threatens the health of worldwide populations of humans and other organisms.
2021-09-29
bioRxiv (preprint)
doi.org
Parametric Scattering Networks
Shanel Gauthier
Benjamin Th'erien
Laurent Alséne-Racicot
Irina Rish
Eugene Belilovsky
Michael Eickenberg
Guy Wolf
The wavelet scattering transform creates geometric in-variants and deformation stability. In multiple signal do-mains, it has been shown to … (see more)yield more discriminative rep-resentations compared to other non-learned representations and to outperform learned representations in certain tasks, particularly on limited labeled data and highly structured signals. The wavelet filters used in the scattering trans-form are typically selected to create a tight frame via a pa-rameterized mother wavelet. In this work, we investigate whether this standard wavelet filterbank construction is op-timal. Focusing on Morlet wavelets, we propose to learn the scales, orientations, and aspect ratios of the filters to produce problem-specific parameterizations of the scattering transform. We show that our learned versions of the scattering transform yield significant performance gains in small-sample classification settings over the standard scat-tering transform. Moreover, our empirical results suggest that traditional filterbank constructions may not always be necessary for scattering transforms to extract effective rep-resentations.
2021-07-20
ArXiv (preprint)
doi.org
arxiv.org
Embedding Signals on Knowledge Graphs with Unbalanced Diffusion Earth Mover's Distance
Alexander Tong
Guillaume Huguet
Dennis L. Shung
Amine Natik
Manik Kuchroo
Guillaume Lajoie
Guy Wolf
Smita Krishnaswamy
In modern relational machine learning it is common to encounter large graphs that arise via interactions or similarities between observation… (see more)s in many domains. Further
2021-01-01
arXiv.org (preprint)
dblp.uni-trier.de
Embedding Signals on Knowledge Graphs with Unbalanced Diffusion Earth Mover's Distance
Alexander Tong
Guillaume Huguet
Dennis Shung
Amine Natik
Manik Kuchroo
Guillaume Lajoie
Guy Wolf
Smita Krishnaswamy
In modern relational machine learning it is common to encounter large graphs that arise via interactions or similarities between observation… (see more)s in many domains. Further
2021-01-01
arXiv.org (preprint)
dblp.uni-trier.de
Extendable and invertible manifold learning with geometry regularized autoencoders
Andres F. Duque Correa
Sacha Morin
Guy Wolf
Kevin R. Moon
A fundamental task in data exploration is to extract simplified low dimensional representations that capture intrinsic geometry in data, esp… (see more)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.
2020-12-10
2020 IEEE International Conference on Big Data (Big Data) (published)
doi.org
arxiv.org
Multiscale PHATE Exploration of SARS-CoV-2 Data Reveals Multimodal Signatures of Disease
Manik Kuchroo
Jessie Huang
Patrick Wong
Jean-Christophe Grenier
Dennis Shung
Alexander Tong
Carolina Lucas
Jon Klein
Daniel B. Burkhardt
Scott Gigante
Abhinav Godavarthi
Benjamin Israelow
Tianyang Mao
Ji Eun Oh
Julio Silva
Takehiro Takahashi
Camila D. Odio
Arnau Casanovas-Massana
John Fournier
Shelli Farhadian … (see 7 more)
Charles S. Dela Cruz
Albert I. Ko
F. Perry Wilson
Julie Hussin
Guy Wolf
Akiko Iwasaki
Smita Krishnaswamy
2020-11-17
bioRxiv (preprint)
doi.org
Extendable and invertible manifold learning with geometry regularized autoencoders
Andres F. Duque Correa
Sacha Morin
Guy Wolf
Kevin R. Moon
A fundamental task in data exploration is to extract simplified low dimensional representations that capture intrinsic geometry in data, esp… (see more)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.
2020-07-14
ArXiv (preprint)
doi.org
arxiv.org
Fixing Bias in Reconstruction-based Anomaly Detection with Lipschitz Discriminators
Alexander Tong
Guy Wolf
Smita Krishnaswamy
Anomaly detection is of great interest in fields where abnormalities need to be identified and corrected (e.g., medicine and finance). Deep … (see more)learning methods for this task often rely on autoencoder reconstruction error, sometimes in conjunction with other penalties. We show that this approach exhibits intrinsic biases that lead to undesirable results. Reconstruction-based methods can sometimes show low error on simple-to-reconstruct points that are not part of the training data, for example the all black image. Instead, we introduce a new unsupervised Lipschitz anomaly discriminator (LAD) that does not suffer from these biases. Our anomaly discriminator is trained, similar to the discriminator of a GAN, to detect the difference between the training data and corruptions of the training data. We show that this procedure successfully detects unseen anomalies with guarantees on those that have a certain Wasserstein distance from the data or corrupted training set. These additions allow us to show improved performance on MNIST, CIFAR10, and health record data. Further, LAD does not require decoding back to the original data space, which makes anomaly detection possible in domains where it is difficult to define a decoder, such as in irregular graph structured data. Empirically, we show this framework leads to improved performance on image, health record, and graph data.
2019-05-26
Journal of Signal Processing Systems (published)
doi.org