Guy Wolf

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

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 :

PhD - Université de Montréal

Paul Janson

PhD - Concordia University

Principal supervisor :

PhD - Université de Montréal

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 :

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

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

2025-12-31

arXiv (published)

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

Decades of research have produced robust methods for unsupervised data visualization, yet supervised visualization…

2025-10-22

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

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)

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

Robin Yadav

Qi Yan

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 A. 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.

2025-08-21

ArXiv (preprint)