Portrait of Guy Wolf

Guy Wolf

Core Academic Member
wolfguy@mila.quebec
Canada CIFAR AI Chair
Associate Professor, Université de Montréal, Department of Mathematics and Statistics
Concordia University
CHUM - Montreal University Hospital Center
Research Topics
Data Mining
Deep Learning
Dynamical Systems
Graph Neural Networks
Information Retrieval
Learning on Graphs
Machine Learning Theory
Medical Machine Learning
Molecular Modeling
Multimodal Learning
Representation Learning
Spectral Learning
Website
Google Scholar

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

Ria Arora
Master's Research - Université de Montréal
Co-supervisor :
Liam Paull
Github
Adrien Aumon
PhD - Université de Montréal
Website
Github
Irene Bonafonte Pardàs
Independent visiting researcher - Helmholtz Munich
Github
Google Scholar
Semih Cantürk
PhD - Université de Montréal
semihcanturk00@gmail.com
Website
Github
Google Scholar
Muawiz Chaudhary
Collaborating Alumni
Github
Google Scholar
Enrique Fita Sanmartin
Research Intern - Université de Montréal
Github
Kameron Harris
Collaborating researcher - Western Washington University (faculty; assistant prof))
Co-supervisor :
Guillaume Lajoie
Website
Github
Google Scholar
Stefan Horoi
PhD - Université de Montréal
Website
Github
Google Scholar
Will Hua
Master's Research - McGill University
Principal supervisor :
Doina Precup
Github
Xiaolong Huang
Master's Research - Concordia University
Principal supervisor :
Eugene Belilovsky
Github
Guillaume Huguet
PhD - Université de Montréal
Github
Google Scholar
Paul Janson
PhD - Concordia University
Principal supervisor :
Eugene Belilovsky
Website
Github
Google Scholar
Charles-Etienne Joseph
Master's Research - Université de Montréal
Principal supervisor :
Eugene Belilovsky
Github
Jake Kovalic
Collaborating researcher - Yale
Github
Vincent Létourneau
Postdoctorate - Université de Montréal
Katharina Limbeck
Independent visiting researcher - Helmholtz Munich / TUM
katharina.limbeck@helmholtz-munich.de
Website
Github
Google Scholar
Myriam Lizotte
PhD - Université de Montréal
Gabriele Malagoli
Independent visiting researcher - LMU Munich & 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
Sacha Morin
PhD - Université de Montréal
Co-supervisor :
Liam Paull
Website
Github
Google Scholar
Hasti Nafisi
Master's Research - Université de Montréal
Co-supervisor :
Yashar Hezaveh
Github
Geraldin Nanfack
Postdoctorate - Concordia University
Principal supervisor :
Eugene Belilovsky
geraldin.nanfack@mila.quebec
Website
Github
Google Scholar
Amine Natik
PhD - Université de Montréal
Principal supervisor :
Guillaume Lajoie
Shuang Ni
PhD - Université de Montréal
Website
Github
Albert Orozco Camacho
PhD - Concordia University
Principal supervisor :
Eugene Belilovsky
Website
Github
Google Scholar
Thomas Sabourin
Master's Research - Université de Montréal
Matthew Scicluna
PhD - Université de Montréal
Principal supervisor :
Julie Hussin
Github
Google Scholar
Pedro Vianna
Collaborating researcher - Université de Montréal
Co-supervisor :
Eugene Belilovsky
Github
Google Scholar
Siddharth Viswanath
Collaborating researcher - Yale
Frederik Wenkel
PhD - Université de Montréal
Website
Github
Google Scholar
Vivian White
Research Intern - Western Washington University
Principal supervisor :
Guillaume Lajoie
Website
Github
Google Scholar
Zhang Yanlei
Postdoctorate - Université de Montréal
Website
Google Scholar
Stephanie Zandee
Collaborating researcher - McGill University (assistant professor)
stephanie.zandee@mcgill.ca
Website
Google Scholar

Publications

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… (see more)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) (published)
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 (oral)
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… (see more)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 (preprint)
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… (see more)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 (preprint)
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… (see more)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 (preprint)
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… (see more)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 (preprint)
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… (see more)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 (preprint)
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… (see more)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 (preprint)
doi.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 (preprint)
doi.org
Learning Stochastic Rainbow Networks
Vivian White
Muawiz Sajjad Chaudhary
Guy Wolf
Guillaume Lajoie
Kameron Decker Harris
Random feature models are a popular approach for studying network learning that can capture important behaviors while remaining simpler than… (see more) traditional training. Guth et al. [2024] introduced “rainbow” networks which model the distribution of trained weights as correlated random features conditioned on previous layer activity. Sampling new weights from distributions fit to learned networks led to similar performance in entirely untrained networks, and the observed weight covariance were found to be low rank. This provided evidence that random feature models could be extended to some networks away from initialization, but White et al. [2024] failed to replicate their results in the deeper ResNet18 architecture. Here we ask whether the rainbow formulation can succeed in deeper networks by directly training a stochastic ensemble of random features, which we call stochastic rainbow networks. At every gradient descent iteration, new weights are sampled for all intermediate layers and features aligned layer-wise. We find: (1) this approach scales to deeper models, which outperform shallow networks at large widths; (2) ensembling multiple samples from the stochastic model is better than retraining the classifier head; and (3) low-rank parameterization of the learnable weight covariances can approach the accuracy of full-rank networks. This offers more evidence for rainbow and other structured random feature networks as reduced models of deep learning.
2024-10-10
NeurIPS.cc/2024/Workshop/SciForDL (poster)
openreview.net
openreview.net