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

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

Doctorat - UdeM

johannes.schmidt@mila.quebec

Johannes Schmidt

Visiteur de recherche indépendant - University of Fribourg

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Julie Hussin

matthew.scicluna@mila.quebec

Ammar Sherif

Doctorat - Concordia

Superviseur⋅e principal⋅e :

francisco.jose.tellez@mila.quebec

ammar.sherif@mila.quebec

Simeon Tchoffo

Doctorat - UdeM

simeontchoffo@gmail.com

Francisco Tellez

Maîtrise recherche - UdeM

César Miguel Valdez Cordova

Doctorat - McGill

Superviseur⋅e principal⋅e :

Mathieu Blanchette

cesar.valdez@mila.quebec

Maîtrise recherche - UdeM

Collaborateur·rice alumni - UdeM

Co-superviseur⋅e :

yanlei.zhang@mila.quebec

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

Graph topological property recovery with heat and wave dynamics-based features on graphs

Dhananjay Bhaskar

Yanlei Zhang

Charles Xu

Xingzhi Sun

Oluwadamilola Fasina

Arman Afrasiyabi

Siddharth Viswanath

Maximilian Nickel

Michael Perlmutter

2025-11-12

TAG-DS/2025/Conference (spotlight)

Neural FIM: Bridging Statistical Manifolds and Generative Modeling through Fisher Geometry

Yanlei Zhang

Guillaume Huguet

Edward De Brouwer

Danqi Liao

Oluwadamilola Fasina

Alexander Tong

Ricky T. Q. Chen

Maximilian Nickel

Ian Adelstein

While data diffusion-based embeddings are widely used in unsupervised learning to reveal the intrinsic geometry of data, they are fundamenta… (voir plus)lly constrained by their discrete nature and inability to generalize beyond training points. This limitation ob

2025-10-20

TechRxiv (accepté)

Leveraging Parameter Space Symmetries for Reasoning Skill Transfer in LLMs

Stefan Horoi

Sangwoo Cho

Supriyo Chakraborty

Shi-Xiong Zhang

Sambit Sahu

Genta Indra Winata

2025-09-22

NeurIPS.cc/2025/Workshop/UniReps (publié)

César Miguel Valdez Cordova

Measure Before You Look: Grounding Embeddings Through Manifold Metrics

Simon Gravel

2025-09-22

NeurIPS.cc/2025/Workshop/UniReps (publié)

Low-dimensional embeddings of high-dimensional data

Cyril de Bodt

Alex Diaz-Papkovich

Michael Bleher

Kerstin Bunte

Corinna Coupette

Sebastian Damrich

Enrique Fita Sanmartin

Fred Hamprecht

EmHoke-'Agnes Horv'at

Dhruv Kohli

John A. Lee 0001

Boudewijn P. F. Lelieveldt

Leland McInnes

Ian T. Nabney

Maximilian Noichl

Pavlin G. Polivcar

Bastian Rieck

Gal Mishne … (voir 1 de plus)

Dmitry Kobak

Large collections of high-dimensional data have become nearly ubiquitous across many academic fields and application domains, ranging from b… (voir plus)iology to the humanities. Since working directly with high-dimensional data poses challenges, the demand for algorithms that create low-dimensional representations, or embeddings, for data visualization, exploration, and analysis is now greater than ever. In recent years, numerous embedding algorithms have been developed, and their usage has become widespread in research and industry. This surge of interest has resulted in a large and fragmented research field that faces technical challenges alongside fundamental debates, and it has left practitioners without clear guidance on how to effectively employ existing methods. Aiming to increase coherence and facilitate future work, in this review we provide a detailed and critical overview of recent developments, derive a list of best practices for creating and using low-dimensional embeddings, evaluate popular approaches on a variety of datasets, and discuss the remaining challenges and open problems in the field.

2025-08-20

ArXiv (prépublication)

arxiv.org

Data Visualization using Functional Data Analysis

Haozhe Chen

Andres Duque Correa

Kevin R. Moon

Data visualization via dimensionality reduction is an important tool in exploratory data analysis. However, when the data are noisy, many ex… (voir plus)isting methods fail to capture the underlying structure of the data. Furthermore, existing methods that can theoretically eliminate all noise are difficult to implement in high dimensions. Here we propose a new data visualization method called Functional Information Geometry (FIG) for dynamical processes that denoises the data by leveraging time information and mitigates the curse of dimensionality using approaches from functional data analysis. We experimentally demonstrate that FIG outperforms other methods in terms of capturing the true structure, hyperparameter robustness, and computational speed. We then use our method to visualize EEG brain measurements of sleep activity.

2025-07-31

2025 International Conference on Sampling Theory and Applications (SampTA) (publié)

Circuit Discovery Helps To Detect LLM Jailbreaking

Despite extensive safety alignment, large language models (LLMs) remain vulnerable to jailbreak attacks that bypass safeguards to elicit har… (voir plus)mful content. While prior work attributes this vulnerability to safety training limitations, the internal mechanisms by which LLMs process adversarial prompts remain poorly understood. We present a mechanistic analysis of the jailbreaking behavior in a large-scale, safety-aligned LLM, focusing on LLaMA-2-7B-chat-hf. Leveraging edge attribution patching and subnetwork probing, we systematically identify computational circuits responsible for generating affirmative responses to jailbreak prompts. Ablating these circuits during the first token prediction can reduce attack success rates by up to 80\%, demonstrating its critical role in safety bypass. Our analysis uncovers key attention heads and MLP pathways that mediate adversarial prompt exploitation, revealing how important tokens propagate through these components to override safety constraints. These findings advance the understanding of adversarial vulnerabilities in aligned LLMs and pave the way for targeted, interpretable defenses mechanisms based on mechanistic interpretability.

2025-06-29

ICML.cc/2025/Workshop/R2-FM (poster)

Less is More: Undertraining Experts Improves Model Upcycling

Stefan Horoi

Gintare Karolina Dziugaite

Modern deep learning is increasingly characterized by the use of open-weight foundation models that can be fine-tuned on specialized dataset… (voir plus)s. This has led to a proliferation of expert models and adapters, often shared via platforms like HuggingFace and AdapterHub. To leverage these resources, numerous model upcycling methods have emerged, enabling the reuse of fine-tuned models in multi-task systems. A natural pipeline has thus formed to harness the benefits of transfer learning and amortize sunk training costs: models are pre-trained on general data, fine-tuned on specific tasks, and then upcycled into more general-purpose systems. A prevailing assumption is that improvements at one stage of this pipeline propagate downstream, leading to gains at subsequent steps. In this work, we challenge that assumption by examining how expert fine-tuning affects model upcycling. We show that long fine-tuning of experts that optimizes for their individual performance leads to degraded merging performance, both for fully fine-tuned and LoRA-adapted models, and to worse downstream results when LoRA adapters are upcycled into MoE layers. We trace this degradation to the memorization of a small set of difficult examples that dominate late fine-tuning steps and are subsequently forgotten during merging. Finally, we demonstrate that a task-dependent aggressive early stopping strategy can significantly improve upcycling performance.

2025-06-16

arXiv (prépublication)

arxiv.org

Test Time Adaptation Using Adaptive Quantile Recalibration

2025-06-09

ICML.cc/2025/Workshop/PUT (poster)

Geometry aware graph attention networks to explain single-cell chromatin state and gene expression

Gabriele Malagoli

Patrick Hanel

Anna Danese

Maria Colomé-Tatché

High-throughput measurements that profile the transcriptome or the epigenome of single-cells are becoming a common way to study cell identit… (voir plus)y. These data are high dimensional, sparse and non linear. Here we present SEAGALL (Single-cell Explainable Geometry-Aware Graph Attention Learning pipeLine), a hypothesis free method to extract biologically relevant features from single-cell experiments based on geometry regularised autoencoders (GRAE) and explainable graph attention networks (GAT). We use a GRAE to embed the data into a latent space preserving the data geometry and we construct a cell-to-cell graph computing distances in the GRAE bottleneck. Exploiting the attention mechanism to dynamically learn the relevant edges, we use GATs to classify the cells and we explain the predictions of the model with XAI methods to unravel the features which are driving cell identity beyond marker genes. We apply our method to data sets from scRNA-seq, scATAC-seq and scChIP-seq experiments. SEAGALL can extract cell type specific and stable signatures which not only differ from the ones found in classical linear approaches but are less biassed by coverage and high expression.

2025-05-31

bioRxiv (prépublication)

AdaFisher: Adaptive Second Order Optimization via Fisher Information

Damien Martins Gomes

Yanlei Zhang

Mahdi S. Hosseini

First-order optimization methods are currently the mainstream in training deep neural networks (DNNs). Optimizers like Adam incorporate limi… (voir plus)ted curvature information by employing the diagonal matrix preconditioning of the stochastic gradient during the training. Despite their widespread, second-order optimization algorithms exhibit superior convergence properties compared to their first-order counterparts e.g. Adam and SGD. However, their practicality in training DNNs is still limited due to increased per-iteration computations compared to the first-order methods. We present \emph{AdaFisher}--an adaptive second-order optimizer that leverages a \emph{diagonal block-Kronecker} approximation of the Fisher information matrix for adaptive gradient preconditioning. AdaFisher aims to bridge the gap between enhanced \emph{convergence/generalization} capabilities and computational efficiency in second-order optimization framework for training DNNs. Despite the slow pace of second-order optimizers, we showcase that AdaFisher can be reliably adopted for image classification, language modeling and stands out for its stability and robustness in hyper-parameter tuning. We demonstrate that AdaFisher \textbf{outperforms the SOTA optimizers} in terms of both accuracy and convergence speed. Code is available from https://github.com/AtlasAnalyticsLab/AdaFisher.

2025-04-22

International Conference on Learning Representations (Accept (Poster))

Recovering undersampled single-cell transcriptomes with HyperCell

Emmanuel Noutahi

Alisandra K. Denton

Joseph E. Powell

Danilo Bzdok

Abstract

Single-cell transcriptomic technology has now matured, allowing quantification of mRNA transcripts corres… (voir plus)ponding to tens of thousands of genes within a cell. However, still only a small fraction of these mRNA is captured and measured by today’s single-cell assays. There are likely hundreds of thousands of mRNA copies present within a typical human cell, yet these assays omit a majority of the transcripts that are actually present. This introduces technical noise, especially non-biological variability and excessive sparsity, which frustrates downstream analysis and potentially skews biological conclusions. To overcome these challenges, we here develop HyperCell, a probabilistic deep learning approach that explicitly models this undersampling to produce estimates of each cell’s original gene transcript abundances across the whole transcriptome. We demonstrate that our framework offers benefits in various mRNA modeling settings, by i) correctly differentiating between spurious sampling-induced and real biological zeros, outperforming existing approaches, ii) estimating the total mRNA content of cells across states to reduce contamination due to background transcripts, iii) reducing contamination due to background transcripts, and iv) helping to counteract biases that may appear during typical differential gene expression analyses using widespread normalization approaches. Our approach to correcting for the technical noise introduced by the single-cell experimental process brings us closer to studying biology, starting from the true transcriptome of cells.

2025-04-22

Research Square (prépublication)