Portrait de Guy Wolf

Guy Wolf

Membre académique principal
wolfguy@mila.quebec
Chaire en IA Canada-CIFAR
Professeur agrégé, Université de Montréal, Département de mathématiques et statistiques
Concordia University
CHUM - Montreal University Hospital Center
Sujets de recherche
Apprentissage automatique médical
Apprentissage de représentations
Apprentissage multimodal
Apprentissage profond
Apprentissage spectral
Apprentissage sur graphes
Exploration des données
Modélisation moléculaire
Recherche d'information
Réseaux de neurones en graphes
Systèmes dynamiques
Théorie de l'apprentissage automatique
Site web
Google Scholar

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 :
Liam Paull
Github
Adrien Aumon
Doctorat - UdeM
Site web
Github
Irene Bonafonte Pardàs
Visiteur de recherche indépendant - Helmholtz Munich
Github
Google Scholar
Semih Cantürk
Doctorat - UdeM
semihcanturk00@gmail.com
Site web
Github
Google Scholar
Muawiz Chaudhary
Collaborateur·rice alumni
Github
Google Scholar
Enrique Fita Sanmartin
Stagiaire de recherche - UdeM
Github
Kameron Harris
Collaborateur·rice de recherche - Western Washington University (faculty; assistant prof))
Co-superviseur⋅e :
Guillaume Lajoie
Site web
Github
Google Scholar
Stefan Horoi
Doctorat - UdeM
Site web
Github
Google Scholar
Will Hua
Maîtrise recherche - McGill
Superviseur⋅e principal⋅e :
Doina Precup
Github
Xiaolong Huang
Maîtrise recherche - Concordia
Superviseur⋅e principal⋅e :
Eugene Belilovsky
Github
Guillaume Huguet
Doctorat - UdeM
Github
Google Scholar
Paul Janson
Doctorat - Concordia
Superviseur⋅e principal⋅e :
Eugene Belilovsky
Site web
Github
Google Scholar
Charles-Etienne Joseph
Maîtrise recherche - UdeM
Superviseur⋅e principal⋅e :
Eugene Belilovsky
Github
Jake Kovalic
Collaborateur·rice de recherche - Yale
Github
Vincent Létourneau
Postdoctorat - UdeM
Katharina Limbeck
Visiteur de recherche indépendant - Helmholtz Munich / TUM
katharina.limbeck@helmholtz-munich.de
Site web
Github
Google Scholar
Myriam Lizotte
Doctorat - UdeM
Gabriele Malagoli
Visiteur de recherche indépendant - LMU Munich & Helmholtz Munich
Philippe Martin
Doctorat - UdeM
Co-superviseur⋅e :
Paul François
Paria Mehrbod
Maîtrise recherche - Concordia
Superviseur⋅e principal⋅e :
Eugene Belilovsky
Lydia Mezrag
Doctorat - UdeM
Sacha Morin
Doctorat - UdeM
Co-superviseur⋅e :
Liam Paull
Site web
Github
Google Scholar
Hasti Nafisi
Maîtrise recherche - UdeM
Co-superviseur⋅e :
Yashar Hezaveh
Github
Geraldin Nanfack
Postdoctorat - Concordia
Superviseur⋅e principal⋅e :
Eugene Belilovsky
geraldin.nanfack@mila.quebec
Site web
Github
Google Scholar
Amine Natik
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Guillaume Lajoie
Shuang Ni
Doctorat - UdeM
Site web
Github
Albert Orozco Camacho
Doctorat - Concordia
Superviseur⋅e principal⋅e :
Eugene Belilovsky
Site web
Github
Google Scholar
Thomas Sabourin
Maîtrise recherche - UdeM
Matthew Scicluna
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Julie Hussin
Github
Google Scholar
Pedro Vianna
Collaborateur·rice de recherche - UdeM
Co-superviseur⋅e :
Eugene Belilovsky
Github
Google Scholar
Siddharth Viswanath
Collaborateur·rice de recherche - Yale
Frederik Wenkel
Doctorat - UdeM
Site web
Github
Google Scholar
Vivian White
Stagiaire de recherche - Western Washington University
Superviseur⋅e principal⋅e :
Guillaume Lajoie
Site web
Github
Google Scholar
Zhang Yanlei
Postdoctorat - UdeM
Site web
Google Scholar
Stephanie Zandee
Collaborateur·rice de recherche - McGill (assistant professor)
stephanie.zandee@mcgill.ca
Site web
Google Scholar

Publications

Non-Uniform Parameter-Wise Model Merging
Albert M. Orozco Camacho
Stefan Horoi
Guy Wolf
Eugene Belilovsky
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.
2024-12-15
ArXiv (prépublication)
doi.org
arxiv.org
Non-Uniform Parameter-Wise Model Merging
Albert Manuel Orozco Camacho
Stefan Horoi
Guy Wolf
Eugene Belilovsky
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.
2024-12-15
2024 IEEE International Conference on Big Data (BigData) (publié)
doi.org
arxiv.org
Towards a General Recipe for Combinatorial Optimization with Multi-Filter GNNs
Frederik Wenkel
Semih Cantürk
Stefan Horoi
Michael Perlmutter
Guy Wolf
2024-11-16
logconference.io/LOG/2024/Conference (présentation orale)
openreview.net
openreview.net
Reaction-conditioned De Novo Enzyme Design with GENzyme
Chenqing Hua
Jiarui Lu
Yong Liu
Odin Zhang
Jian Tang
Rex Ying
Wengong Jin
Guy Wolf
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)
doi.org
arxiv.org
Reaction-conditioned De Novo Enzyme Design with GENzyme
Chenqing Hua
Jiarui Lu
Yong Liu
Odin Zhang
Jian Tang
Rex Ying
Wengong Jin
Guy Wolf
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)
doi.org
arxiv.org
Reaction-conditioned De Novo Enzyme Design with GENzyme
Chenqing Hua
Jiarui Lu
Yong Liu
Odin Zhang
Jian Tang
Rex Ying
Wengong Jin
Guy Wolf
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)
arxiv.org
arxiv.org
Reaction-conditioned De Novo Enzyme Design with GENzyme
Chenqing Hua
Jiarui Lu
Yong Liu
Odin Zhang
Jian Tang
Rex Ying
Wengong Jin
Guy Wolf
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)
arxiv.org
arxiv.org
Reaction-conditioned De Novo Enzyme Design with GENzyme
Chenqing Hua
Jiarui Lu
Yong Liu
Odin Zhang
Jian Tang
Rex Ying
Wengong Jin
Guy Wolf
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)
arxiv.org
arxiv.org
Reaction-conditioned De Novo Enzyme Design with GENzyme
Chenqing Hua
Jiarui Lu
Yong Liu
Odin Zhang
Jian Tang
Rex Ying
Wengong Jin
Guy Wolf
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)
arxiv.org
arxiv.org
Effective Protein-Protein Interaction Exploration with PPIretrieval
Chenqing Hua
Connor W. Coley
Guy Wolf
Doina Precup
Shuangjia Zheng
2024-10-13
NeurIPS.cc/2024/Workshop/AIDrugX (poster)
doi.org
openreview.net
EnzymeFlow: Generating Reaction-specific Enzyme Catalytic Pockets through Flow Matching and Co-Evolutionary Dynamics
Chenqing Hua
Yong Liu
Dinghuai Zhang
Odin Zhang
Sitao Luan
Kevin K Yang
Guy Wolf
Doina Precup
Shuangjia Zheng
2024-10-13
NeurIPS.cc/2024/Workshop/AIDrugX (poster)
doi.org
openreview.net
Neuro-GSTH: A Geometric Scattering and Persistent Homology Framework for Uncovering Spatiotemporal Signatures in Neural Activity
Dhananjay Bhaskar
Jessica Moore
Yanlei Zhang
Feng Gao
Bastian Rieck
Guy Wolf
Helen Pushkarskaya
Firas Khasawneh
Elizabeth Munch
Valentina Greco
Christopher Pittenger
Smita Krishnaswamy
2024-10-13
bioRxiv (prépublication)
doi.org