Guy Wolf

Biographie

Guy Wolf est professeur agrégé au Département de mathématiques et de statistique de l'Université de Montréal. Ses intérêts de recherche se situent au carrefour de l'apprentissage automatique, de la science des données et des mathématiques appliquées. Il s'intéresse particulièrement aux méthodes d'exploration de données qui utilisent l'apprentissage multiple et l'apprentissage géométrique profond, ainsi qu'aux applications pour l'analyse exploratoire des données biomédicales.

Ses recherches portent sur l'analyse exploratoire des données, avec des applications en bio-informatique. Ses approches sont multidisciplinaires et combinent l'apprentissage automatique, le traitement du signal et les outils mathématiques appliqués. En particulier, ses travaux récents utilisent une combinaison de géométries de diffusion et d'apprentissage profond pour trouver des modèles émergents, des dynamiques et des structures dans les mégadonnées à grande dimension (par exemple, dans la génomique et la protéomique de la cellule unique).

Étudiants actuels

Ria Arora

Maîtrise recherche - UdeM

Co-superviseur⋅e :

Doctorat - UdeM

Doctorat - UdeM

semihcanturk00@gmail.com

Collaborateur·rice alumni

Enrique Fita Sanmartin

Collaborateur·rice alumni - UdeM

Kameron Harris

Collaborateur·rice de recherche - Western Washington University (faculty; assistant prof))

Co-superviseur⋅e :

Doctorat - UdeM

Will Hua

Collaborateur·rice alumni - McGill

Xiaolong Huang

Maîtrise recherche - Concordia

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Paul Janson

Doctorat - Concordia

Superviseur⋅e principal⋅e :

Charles-Etienne Joseph

Maîtrise recherche - UdeM

Superviseur⋅e principal⋅e :

M. Elyes Kanoun

Stagiaire de recherche - UdeM

Vincent Létourneau

Postdoctorat - UdeM

Myriam Lizotte

Doctorat - UdeM

Philippe Martin

Doctorat - UdeM

Co-superviseur⋅e :

Paul François

Paria Mehrbod

Maîtrise recherche - Concordia

Superviseur⋅e principal⋅e :

Lydia Mezrag

Doctorat - UdeM

Sacha Morin

Doctorat - UdeM

Co-superviseur⋅e :

Postdoctorat - Concordia

Superviseur⋅e principal⋅e :

geraldin.nanfack@mila.quebec

Amine Natik

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Guillaume Lajoie

Shuang Ni

Doctorat - UdeM

stephanie.zandee@mcgill.ca

Albert Orozco Camacho

Doctorat - Concordia

Superviseur⋅e principal⋅e :

Maîtrise recherche - UdeM

Matthew Scicluna

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Collaborateur·rice de recherche - UdeM

Co-superviseur⋅e :

Doctorat - UdeM

Stagiaire de recherche - Western Washington University

Superviseur⋅e principal⋅e :

Postdoctorat - UdeM

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

Non-Uniform Parameter-Wise Model Merging

Albert Manuel Orozco Camacho

Stefan Horoi

Combining multiple machine learning models has long been a technique for enhancing performance, particularly in distributed settings. Tradit… (voir plus)ional approaches, such as model ensembles, work well, but are expensive in terms of memory and compute. Recently, methods based on averaging model parameters have achieved good results in some settings and have gained popularity. However, merging models initialized differently that do not share a part of their training trajectories can yield worse results than simply using the base models, even after aligning their neurons. In this paper, we introduce a novel approach, Non-uniform Parameter-wise Model Merging, or NP Merge, which merges models by learning the contribution of each parameter to the final model using gradient-based optimization. We empirically demonstrate the effectiveness of our method for merging models of various architectures in multiple settings, outperforming past methods. We also extend NP Merge to handle the merging of multiple models, showcasing its scalability and robustness.

2024-12-20

ArXiv (prépublication)

Non-Uniform Parameter-Wise Model Merging

Albert M. Orozco Camacho

Stefan Horoi

2024-12-15

ArXiv (prépublication)

doi.org

Non-Uniform Parameter-Wise Model Merging

Albert Manuel Orozco Camacho

Stefan Horoi

2024-12-15

2024 IEEE International Conference on Big Data (BigData) (publié)

doi.org

Neural networks with optimized single-neuron adaptation uncover biologically plausible regularization

Victor Geadah

Stefan Horoi

Giancarlo Kerg

Guillaume Lajoie

Neurons in the brain have rich and adaptive input-output properties. Features such as heterogeneous f-I curves and spike frequency adaptatio… (voir plus)n are 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 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 play an active regularization role, enabling neural circuits to optimally propagate information across time.

2024-12-13

PLOS Computational Biology (publié)

doi.org

Towards a General Recipe for Combinatorial Optimization with Multi-Filter GNNs

Frederik Wenkel

Semih Cantürk

Stefan Horoi

Michael Perlmutter

2024-11-16

logconference.io/LOG/2024/Conference (présentation orale)

openreview.net

Reaction-conditioned De Novo Enzyme Design with GENzyme

Chenqing Hua

Jiarui Lu

Yong Liu

Odin Zhang

Jian Tang

Rex Ying

Wengong Jin

Doina Precup

Shuangjia Zheng

The introduction of models like RFDiffusionAA, AlphaFold3, AlphaProteo, and Chai1 has revolutionized protein structure modeling and interact… (voir plus)ion prediction, primarily from a binding perspective, focusing on creating ideal lock-and-key models. However, these methods can fall short for enzyme-substrate interactions, where perfect binding models are rare, and induced fit states are more common. To address this, we shift to a functional perspective for enzyme design, where the enzyme function is defined by the reaction it catalyzes. Here, we introduce \textsc{GENzyme}, a \textit{de novo} enzyme design model that takes a catalytic reaction as input and generates the catalytic pocket, full enzyme structure, and enzyme-substrate binding complex. \textsc{GENzyme} is an end-to-end, three-staged model that integrates (1) a catalytic pocket generation and sequence co-design module, (2) a pocket inpainting and enzyme inverse folding module, and (3) a binding and screening module to optimize and predict enzyme-substrate complexes. The entire design process is driven by the catalytic reaction being targeted. This reaction-first approach allows for more accurate and biologically relevant enzyme design, potentially surpassing structure-based and binding-focused models in creating enzymes capable of catalyzing specific reactions. We provide \textsc{GENzyme} code at https://github.com/WillHua127/GENzyme.

2024-11-10

ArXiv (prépublication)