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, 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

Semih Cantürk

PhD - Université de Montréal

Joao Felipe Carneiro Barbosa Rocha

Collaborating researcher - Yale University

Co-supervisor :

Collaborating Alumni

PhD - Université de Montréal

Xiao Huang

Master's Research - Concordia University

Principal supervisor :

Collaborating Alumni - Université de Montréal

Paul Janson

PhD - Concordia University

Principal supervisor :

PhD - Université de Montréal

Gabriele Malagoli

Independent visiting researcher - Helmholtz Munich

Philippe Martin

PhD - Université de Montréal

Co-supervisor :

Paul François

Paria Mehrbod

Master's Research - Concordia University

Principal supervisor :

Eugene Belilovsky

Lydia Mezrag

PhD - Université de Montréal

Kevin Moon

Collaborating researcher

Sacha Morin

PhD - Université de Montréal

Co-supervisor :

Postdoctorate - Concordia University

Principal supervisor :

Shuang Ni

PhD - Université de Montréal

Albert Orozco Camacho

PhD - Concordia University

Principal supervisor :

Jake Rhodes

Collaborating researcher - BYU

Thomas Sabourin

Master's Research - Université de Montréal

Matthew Scicluna

PhD - Université de Montréal

Principal supervisor :

PhD - Université de Montréal

Francisco Tellez

Master's Research - Université de Montréal

Jesuino Vieira Filho

Master's Research - Université de Montréal

Postdoctorate - Université de Montréal

Co-supervisor :

Collaborating researcher - McGill University (assistant professor)

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

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

Smita Krishnaswamy

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

2025-10-20

TechRxiv (accepted)

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 (published)

César Miguel Valdez Cordova

Measure Before You Look: Grounding Embeddings Through Manifold Metrics

Simon Gravel

2025-09-22

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

Retro SynFlow: Discrete Flow Matching for Accurate and Diverse Single-Step Retrosynthesis

Robin Yadav

Qi Yan

Avishek Joey Bose

Renjie Liao

A fundamental problem in organic chemistry is identifying and predicting the series of reactions that synthesize a desired target product mo… (see more)lecule. Due to the combinatorial nature of the chemical search space, single-step reactant prediction -- i.e. single-step retrosynthesis -- remains challenging even for existing state-of-the-art template-free generative approaches to produce an accurate yet diverse set of feasible reactions. In this paper, we model single-step retrosynthesis planning and introduce RETRO SYNFLOW (RSF) a discrete flow-matching framework that builds a Markov bridge between the prescribed target product molecule and the reactant molecule. In contrast to past approaches, RSF employs a reaction center identification step to produce intermediate structures known as synthons as a more informative source distribution for the discrete flow. To further enhance diversity and feasibility of generated samples, we employ Feynman-Kac steering with Sequential Monte Carlo based resampling to steer promising generations at inference using a new reward oracle that relies on a forward-synthesis model. Empirically, we demonstrate \nameshort achieves

2025-09-17

NeurIPS.cc/2025/Conference (poster)

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

Smita Krishnaswamy

John A. Lee 0001

Boudewijn P. F. Lelieveldt

Leland McInnes

Ian T. Nabney

Maximilian Noichl

Pavlin G. Polivcar

Bastian Rieck

Gal Mishne … (see 1 more)

Dmitry Kobak

Large collections of high-dimensional data have become nearly ubiquitous across many academic fields and application domains, ranging from b… (see more)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 (preprint)

arxiv.org

Towards a General GNN Framework for Combinatorial Optimization

Michael Perlmutter

2025-07-29

Proceedings of the Third Learning on Graphs Conference (published)

proceedings.mlr.press

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… (see more)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

Eugene Belilovsky

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… (see more)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 (Cornell University) (preprint)

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… (see more)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 (preprint)

Recovering undersampled single-cell transcriptomes with HyperCell

Emmanuel Noutahi

Alisandra K. Denton

Joseph E. Powell

Smita Krishnaswamy

Danilo Bzdok

Abstract

Single-cell transcriptomic technology has now matured, allowing quantification of mRNA transcripts corres… (see more)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 (preprint)

Graph Neural Networks Meet Probabilistic Graphical Models: A Survey

Qian Zhang

2025-04-05

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