Guy Wolf

Joao Felipe Carneiro Barbosa Rocha

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

Doctorat - UdeM

Doctorat - UdeM

Collaborateur·rice de recherche - Yale University

Co-superviseur⋅e :

Maîtrise recherche - UdeM

Co-superviseur⋅e :

Doctorat - UdeM

Xiao Huang

Maîtrise recherche - Concordia

Superviseur⋅e principal⋅e :

Collaborateur·rice alumni - UdeM

Github

Paul Janson

Doctorat - Concordia

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Visiteur de recherche indépendant - Helmholtz Munich

Philippe Martin

Doctorat - UdeM

Co-superviseur⋅e :

Paul François

Paria Mehrbod

Maîtrise recherche - Concordia

Superviseur⋅e principal⋅e :

Eugene Belilovsky

Lydia Mezrag

Doctorat - UdeM

Kevin Moon

Collaborateur·rice de recherche

Github

Sacha Morin

Doctorat - UdeM

Co-superviseur⋅e :

Postdoctorat - Concordia

Superviseur⋅e principal⋅e :

Shuang Ni

Doctorat - UdeM

Github

Albert Orozco Camacho

Doctorat - Concordia

Superviseur⋅e principal⋅e :

Jake Rhodes

Collaborateur·rice de recherche - BYU

Thomas Sabourin

Maîtrise recherche - UdeM

Matthew Scicluna

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Maîtrise recherche - UdeM

Jesuino Vieira Filho

Maîtrise recherche - UdeM

Collaborateur·rice alumni - UdeM

Co-superviseur⋅e :

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

A Heat Diffusion Perspective on Geodesic Preserving Dimensionality Reduction

Guillaume Huguet

Alexander Tong

Edward De Brouwer

Yanlei Zhang

Ian Adelstein

Diffusion-based manifold learning methods have proven useful in representation learning and dimensionality reduction of modern high dimensio… (voir plus)nal, high throughput, noisy datasets. Such datasets are especially present in fields like biology and physics. While it is thought that these methods preserve underlying manifold structure of data by learning a proxy for geodesic distances, no specific theoretical links have been established. Here, we establish such a link via results in Riemannian geometry explicitly connecting heat diffusion to manifold distances. In this process, we also formulate a more general heat kernel based manifold embedding method that we call heat geodesic embeddings. This novel perspective makes clearer the choices available in manifold learning and denoising. Results show that our method outperforms existing state of the art in preserving ground truth manifold distances, and preserving cluster structure in toy datasets. We also showcase our method on single cell RNA-sequencing datasets with both continuum and cluster structure, where our method enables interpolation of withheld timepoints of data. Finally, we show that parameters of our more general method can be configured to give results similar to PHATE (a state-of-the-art diffusion based manifold learning method) as well as SNE (an attraction/repulsion neighborhood based method that forms the basis of t-SNE).

2022-12-31

Advances in Neural Information Processing Systems (publié)

Inferring dynamic regulatory interaction graphs from time series data with perturbations

Dhananjay Bhaskar

Sumner Magruder

Edward De Brouwer

Matheo Morales

Aarthi Venkat

Frederik Wenkel

Complex systems are characterized by intricate interactions between entities that evolve dynamically over time. Accurate inference of these … (voir plus)dynamic relationships is crucial for understanding and predicting system behavior. In this paper, we propose Regulatory Temporal Interaction Network Inference (RiTINI) for inferring time-varying interaction graphs in complex systems using a novel combination of space-and-time graph attentions and graph neural ordinary differential equations (ODEs). RiTINI leverages time-lapse signals on a graph prior, as well as perturbations of signals at various nodes in order to effectively capture the dynamics of the underlying system. This approach is distinct from traditional causal inference networks, which are limited to inferring acyclic and static graphs. In contrast, RiTINI can infer cyclic, directed, and time-varying graphs, providing a more comprehensive and accurate representation of complex systems. The graph attention mechanism in RiTINI allows the model to adaptively focus on the most relevant interactions in time and space, while the graph neural ODEs enable continuous-time modeling of the system's dynamics. We evaluate RiTINI's performance on various simulated and real-world datasets, demonstrating its state-of-the-art capability in inferring interaction graphs compared to previous methods.

2022-12-31

LoG (publié)

proceedings.mlr.press

Multi-view manifold learning of human brain-state trajectories.

Erica L. Busch

Jessie Huang

Andrew Benz

Tom Wallenstein

Guillaume Lajoie

Nicholas B. Turk-Browne

The complexity of the human brain gives the illusion that brain activity is intrinsically high-dimensional. Nonlinear dimensionality-reducti… (voir plus)on methods such as uniform manifold approximation and t-distributed stochastic neighbor embedding have been used for high-throughput biomedical data. However, they have not been used extensively for brain activity data such as those from functional magnetic resonance imaging (fMRI), primarily due to their inability to maintain dynamic structure. Here we introduce a nonlinear manifold learning method for time-series data—including those from fMRI—called temporal potential of heat-diffusion for affinity-based transition embedding (T-PHATE). In addition to recovering a low-dimensional intrinsic manifold geometry from time-series data, T-PHATE exploits the data’s autocorrelative structure to faithfully denoise and unveil dynamic trajectories. We empirically validate T-PHATE on three fMRI datasets, showing that it greatly improves data visualization, classification, and segmentation of the data relative to several other state-of-the-art dimensionality-reduction benchmarks. These improvements suggest many potential applications of T-PHATE to other high-dimensional datasets of temporally diffuse processes.

2022-12-31

Nature Computational Science (publié)

Understanding Graph Neural Networks with Generalized Geometric Scattering Transforms

Michael Perlmutter

Alexander Tong

Feng Gao

Matthew Hirn

The scattering transform is a multilayered wavelet-based deep learning architecture that acts as a model of convolutional neural networks. R… (voir plus)ecently, several works have introduced generalizations of the scattering transform for non-Euclidean settings such as graphs. Our work builds upon these constructions by introducing windowed and non-windowed geometric scattering transforms for graphs based upon a very general class of asymmetric wavelets. We show that these asymmetric graph scattering transforms have many of the same theoretical guarantees as their symmetric counterparts. As a result, the proposed construction unifies and extends known theoretical results for many of the existing graph scattering architectures. In doing so, this work helps bridge the gap between geometric scattering and other graph neural networks by introducing a large family of networks with provable stability and invariance guarantees. These results lay the groundwork for future deep learning architectures for graph-structured data that have learned filters and also provably have desirable theoretical properties.

2022-12-31

SIAM Journal on Mathematics of Data Science (publié)

arxiv.org

Can Hybrid Geometric Scattering Networks Help Solve the Maximum Clique Problem?

Yimeng Min

Frederik Wenkel

Michael A. Perlmutter

We propose a geometric scattering-based graph neural network (GNN) for approximating solutions of the NP-hard maximum clique (MC) problem. W… (voir plus)e construct a loss function with two terms, one which encourages the network to find highly connected nodes and the other which acts as a surrogate for the constraint that the nodes form a clique. We then use this loss to train an efficient GNN architecture that outputs a vector representing the probability for each node to be part of the MC and apply a rule-based decoder to make our final prediction. The incorporation of the scattering transform alleviates the so-called oversmoothing problem that is often encountered in GNNs and would degrade the performance of our proposed setup. Our empirical results demonstrate that our method outperforms representative GNN baselines in terms of solution accuracy and inference speed as well as conventional solvers like Gurobi with limited time budgets. Furthermore, our scattering model is very parameter efficient with only

2022-11-28

Conference on Neural Information Processing Systems (Accept)

Learning Shared Neural Manifolds from Multi-Subject fMRI Data

Jessie Huang

Erica Busch

Tom Wallenstein

Michal Gerasimiuk

Andrew Benz

Guillaume Lajoie

Nicholas Turk-Browne

Functional magnetic resonance imaging (fMRI) is a notoriously noisy measurement of brain activity because of the large variations between in… (voir plus)dividuals, signals marred by environmental differences during collection, and spatiotemporal averaging required by the measurement resolution. In addition, the data is extremely high dimensional, with the space of the activity typically having much lower intrinsic dimension. In order to understand the connection between stimuli of interest and brain activity, and analyze differences and commonalities between subjects, it becomes important to learn a meaningful embedding of the data that denoises, and reveals its intrinsic structure. Specifically, we assume that while noise varies significantly between individuals, true responses to stimuli will share common, low-dimensional features between subjects which are jointly discoverable. Similar approaches have been exploited previously but they have mainly used linear methods such as PCA and shared response modeling (SRM). In contrast, we propose a neural network called MRMD-AE (manifold-regularized multiple decoder, autoencoder), that learns a common embedding from multiple subjects in an experiment while retaining the ability to decode to individual raw fMRI signals. We show that our learned common space represents an extensible manifold (where new points not seen during training can be mapped), improves the classification accuracy of stimulus features of unseen timepoints, as well as improves cross-subject translation of fMRI signals. We believe this framework can be used for many downstream applications such as guided brain-computer interface (BCI) training in the future.

2022-08-21

2022 IEEE 32nd International Workshop on Machine Learning for Signal Processing (MLSP) (publié)

Parametric Scattering Networks

Shanel Gauthier

Benjamin Therien

Laurent Alsène-Racicot

Michael Eickenberg

The wavelet scattering transform creates geometric invariants and deformation stability. In multiple signal domains, it has been shown to yi… (voir plus)eld more discriminative representations 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 transform are typically selected to create a tight frame via a parameterized mother wavelet. In this work, we investigate whether this standard wavelet filterbank construction is optimal. 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 scattering transform. Moreover, our empirical results suggest that traditional filterbank constructions may not always be necessary for scattering transforms to extract effective representations.

2022-05-31

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (publié)

arxiv.org

Embedding Signals on Graphs with Unbalanced Diffusion Earth Mover’s Distance

Dennis Shung

Manik Kuchroo

In modern relational machine learning it is common to encounter large graphs that arise via interactions or similarities between observation… (voir plus)s in many domains. Further, in many cases the target entities for analysis are actually signals on such graphs. We propose to compare and organize such datasets of graph signals by using an earth mover’s distance (EMD) with a geodesic cost over the underlying graph. Typically, EMD is computed by optimizing over the cost of transporting one probability distribution to another over an underlying metric space. However, this is inefficient when computing the EMD between many signals. Here, we propose an unbalanced graph EMD that efficiently embeds the unbalanced EMD on an underlying graph into an L(1) space, whose metric we call unbalanced diffusion earth mover’s distance (UDEMD). Next, we show how this gives distances between graph signals that are robust to noise. Finally, we apply this to organizing patients based on clinical notes, embedding cells modeled as signals on a gene graph, and organizing genes modeled as signals over a large cell graph. In each case, we show that UDEMD-based embeddings find accurate distances that are highly efficient compared to other methods.

2022-04-26

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (publié)

Population Genomics Approaches for Genetic Characterization of SARS-CoV-2 Lineages

Fatima Mostefai

Isabel Gamache

Arnaud N'Guessan

Justin Pelletier

Jessie Huang

Carmen Lia Murall

Ahmad Pesaranghader

Vanda Gaonac’h-Lovejoy

David J. Hamelin

Raphaël Poujol

Jean-Christophe Grenier

Martin Smith

Etienne Caron

Morgan Craig

B. Jesse Shapiro

Julie G. Hussin

The genome of the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), the pathogen that causes coronavirus disease 2019 (COVID-19)… (voir plus), has been sequenced at an unprecedented scale leading to a tremendous amount of viral genome sequencing data. To assist in tracing infection pathways and design preventive strategies, a deep understanding of the viral genetic diversity landscape is needed. We present here a set of genomic surveillance tools from population genetics which can be used to better understand the evolution of this virus in humans. 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. We analyzed 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. This approach enables real-time lineage identification, a clear description of the relationship between variants of concern, and efficient detection of recurrent mutations. Furthermore, time series change of Tajima's D by haplotype provides a powerful metric of lineage expansion. Finally, principal component analysis (PCA) highlights key steps in variant emergence and facilitates the visualization of genomic variation in the context of SARS-CoV-2 diversity. The computational framework presented here is simple to implement and insightful for real-time genomic surveillance of SARS-CoV-2 and could be applied to any pathogen that threatens the health of populations of humans and other organisms.

2022-02-20

Frontiers in Medicine (publié)

Embedding Signals on Graphs with Unbalanced Diffusion Earth Mover's Distance

Dennis Shung

Manik Kuchroo

2021-12-31

IEEE International Conference on Acoustics, Speech and Signal Processing (inconnu)

arxiv.org

Goal-driven optimization of single-neuron properties in artiﬁcial networks reveals regularization role of neural diversity and adaptation in the brain

Neurons in the brain have rich and adaptive input-output properties. Features such as diverse f-I curves and spike frequency adaptation are … (voir plus)known to place single neurons in optimal coding regimes when facing changing stimuli. Yet, it is still unclear how brain circuits exploit single neuron ﬂexibility, and how network-level requirements may have shaped such cellular function. To answer this question, a multi-scaled approach is needed where the computations of single neurons and of neural circuits must be considered as a complete system. In this work, we use artiﬁcial neural networks to systematically investigate single neuron input-output adaptive mechanisms, optimized in an end-to-end fashion. Throughout the optimization process, each neuron has the liberty to modify its nonlinear activation function, parametrized to mimic f-I curves of biological neurons, and to learn adaptation strategies to modify activation functions in real-time during a task. We ﬁnd that such networks show much-improved robustness to noise and changes in input statistics. Importantly, we ﬁnd that this procedure recovers precise coding strategies found in biological neurons, such as gain scaling and fractional order differentiation/integration. Using tools from dynamical systems theory, we analyze the role of these emergent single neuron properties and argue that neural diversity and adaptation plays an active regularization role that enables neural circuits to optimally propagate information across time.

2021-12-31

(publié)

www.semanticscholar.org

Long Range Graph Benchmark

Vijay Prakash Dwivedi

Anh Tuan Luu

Graph Neural Networks (GNNs) that are based on the message passing (MP) paradigm generally exchange information between 1-hop neighbors to b… (voir plus)uild node representations at each layer. In principle, such networks are not able to capture long-range interactions (LRI) that may be desired or necessary for learning a given task on graphs. Recently, there has been an increasing interest in development of Transformer-based methods for graphs that can consider full node connectivity beyond the original sparse structure, thus enabling the modeling of LRI. However, MP-GNNs that simply rely on 1-hop message passing often fare better in several existing graph benchmarks when combined with positional feature representations, among other innovations, hence limiting the perceived utility and ranking of Transformer-like architectures. Here, we present the Long Range Graph Benchmark (LRGB) with 5 graph learning datasets: PascalVOC-SP, COCO-SP, PCQM-Contact, Peptides-func and Peptides-struct that arguably require LRI reasoning to achieve strong performance in a given task. We benchmark both baseline GNNs and Graph Transformer networks to verify that the models which capture long-range dependencies perform significantly better on these tasks. Therefore, these datasets are suitable for benchmarking and exploration of MP-GNNs and Graph Transformer architectures that are intended to capture LRI.

2021-12-31

Advances in Neural Information Processing Systems 35 (NeurIPS 2022) (publié)