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

Xiao Huang

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

Master's Research - Université de Montréal

matthew.scicluna@mila.quebec

Matthew Scicluna

PhD - Université de Montréal

Principal supervisor :

Julie Hussin

francisco.jose.tellez@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

stephanie.zandee@mcgill.ca

Stephanie Zandee

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

Position: Message-passing and spectral GNNs are two sides of the same coin

Antonis Vasileiou

Juan Cervino

Pascal Frossard

Charilaos I. Kanatsoulis

Christopher Morris

Michael T. Schaub

Pierre Vandergheynst

Zhiyang Wang

Ron Levie

Graph neural networks (GNNs) are commonly divided into message-passing neural networks (MPNNs) and spectral graph neural networks, reflectin… (see more)g two largely separate research traditions in machine learning and signal processing. This paper argues that this divide is mostly artificial, hindering progress in the field. We propose a viewpoint in which both MPNNs and spectral GNNs are understood as different parametrizations of permutation-equivariant operators acting on graph signals. From this perspective, many popular architectures are equivalent in expressive power, while genuine gaps arise only in specific regimes. We further argue that MPNNs and spectral GNNs offer complementary strengths. That is, MPNNs provide a natural language for discrete structure and expressivity analysis using tools from logic and graph isomorphism research, while the spectral perspective provides principled tools for understanding smoothing, bottlenecks, stability, and community structure. Overall, we posit that progress in graph learning will be accelerated by clearly understanding the key similarities and differences between these two types of GNNs, and by working towards unifying these perspectives within a common theoretical and conceptual framework rather than treating them as competing paradigms.

2026-02-09

arXiv (preprint)

Forest-Guided Semantic Transport for Label-Supervised Manifold Alignment

Adrien Aumon

Myriam Lizotte

Kevin R. Moon

Jake S. Rhodes

2026-01-31

ArXiv (preprint)

GraIP: A Benchmarking Framework For Neural Graph Inverse Problems

Semih Cantürk

Andrei Manolache

Arman Mielke

Chendi Qian

Antoine Siraudin

Christopher Morris

Mathias Niepert

A wide range of graph learning tasks, such as structure discovery, temporal graph analysis, and combinatorial optimization, focus on inferri… (see more)ng graph structures from data, rather than making predictions on given graphs. However, the respective methods to solve such problems are often developed in an isolated, task-specific manner and thus lack a unifying theoretical foundation. Here, we provide a stepping stone towards the formation of such a foundation and further development by introducing the Neural Graph Inverse Problem (GraIP) conceptual framework, which formalizes and reframes a broad class of graph learning tasks as inverse problems. Unlike discriminative approaches that directly predict target variables from given graph inputs, the GraIP paradigm addresses inverse problems, i.e., it relies on observational data and aims to recover the underlying graph structure by reversing the forward process, such as message passing or network dynamics, that produced the observed outputs. We demonstrate the versatility of GraIP across various graph learning tasks, including rewiring, causal discovery, and neural relational inference. We also propose benchmark datasets and metrics for each GraIP domain considered, and characterize and empirically evaluate existing baseline methods used to solve them. Overall, our unifying perspective bridges seemingly disparate applications and provides a principled approach to structural learning in constrained and combinatorial settings while encouraging cross-pollination of existing methods across graph inverse problems.

2026-01-25

ArXiv (preprint)

Scalable Tree Ensemble Proximities in Python

Adrien Aumon

Kevin R. Moon

Jake S. Rhodes

2026-01-05

ArXiv (preprint)

From Memorization to Parameter Interference: How Overtraining Experts Harms Model Merging

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… (see more)s. This has led to a proliferation of expert models and adapters, often shared via platforms like HuggingFace and AdapterHub. Model merging has recently emerged as an effective way to leverage these existing resources, enabling the composition of capabilities from different model checkpoints. 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 multiple checkpoints are merged to obtain a more capable model. 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 merging. We show that long fine-tuning of experts that optimizes for their individual performance leads to degraded merging performance across vision and language modalities, multiple model scales, and both fully fine-tuned and LoRA-adapted models. We trace this degradation to the memorization of a small set of difficult examples that dominate late fine-tuning steps. This causes negative parameter interference and encodes knowledge that is forgotten during merging. Finally, we demonstrate that task-dependent aggressive early stopping strategies can significantly improve model merging performance.

2025-12-31

International Conference on Machine Learning (Accept (regular))

Geometry-Aware Edge Pooling for Graph Neural Networks

Katharina Limbeck

Lydia Mezrag

Bastian Rieck

Graph Neural Networks (GNNs) have shown significant success for graph-based tasks. Motivated by the prevalence of large datasets in real-wor… (see more)ld applications, pooling layers are crucial components of GNNs. By reducing the size of input graphs, pooling enables faster training and potentially better generalisation. However, existing pooling operations often optimise for the learning task at the expense of discarding fundamental graph structures, thus reducing interpretability. This leads to unreliable performance across dataset types, downstream tasks and pooling ratios. Addressing these concerns, we propose novel graph pooling layers for structure-aware pooling via edge collapses. Our methods leverage diffusion geometry and iteratively reduce a graph's size while preserving both its metric structure and its structural diversity. We guide pooling using magnitude, an isometry-invariant diversity measure, which permits us to control the fidelity of the pooling process. Further, we use the spread of a metric space as a faster and more stable alternative ensuring computational efficiency. Empirical results demonstrate that our methods (i) achieve top performance compared to alternative pooling layers across a range of diverse graph classification tasks, (ii) preserve key spectral properties of the input graphs, and (iii) retain high accuracy across varying pooling ratios.

2025-12-02

Conference on Neural Information Processing Systems (Accept (poster))

Freeze, Diffuse, Decode: Geometry-Aware Adaptation of Pretrained Transformer Embeddings for Antimicrobial Peptide Design

Pankhil Gawade

Adam Izdebski

Myriam Lizotte

Kevin R. Moon

Jake S. Rhodes

Ewa Szczurek

2025-11-27

ArXiv (preprint)

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

Smita Krishnaswamy

2025-11-12

TAG-DS/2025/Conference (spotlight)

Random Forest Autoencoders for Guided Representation Learning

Kevin R. Moon

Jake S. Rhodes

Extensive research has produced robust methods for unsupervised data visualization. Yet supervised visualization…

2025-10-21

logconference.io/LOG/2025/Conference (poster)

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