Portrait of Guy Wolf

Guy Wolf

Core Academic Member
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

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

Independent visiting researcher - University of Lorraine
Master's Research - Université de Montréal
Co-supervisor :
Collaborating Alumni
Principal supervisor :
PhD - Université de Montréal
Collaborating Alumni
Collaborating researcher - Western Washington University (faculty; assistant prof))
Co-supervisor :
PhD - Université de Montréal
Master's Research - McGill University
Principal supervisor :
PhD - Université de Montréal
Master's Research - Concordia University
Principal supervisor :
Master's Research - Université de Montréal
Principal supervisor :
Collaborating researcher - Yale
Postdoctorate - Université de Montréal
PhD - Université de Montréal
PhD - Université de Montréal
Co-supervisor :
Master's Research - Concordia University
Principal supervisor :
PhD - Université de Montréal
PhD - Université de Montréal
Co-supervisor :
Master's Research - Université de Montréal
Co-supervisor :
Postdoctorate - Concordia University
Principal supervisor :
PhD - Université de Montréal
Principal supervisor :
PhD - Université de Montréal
PhD - Concordia University
Principal supervisor :
Independent visiting researcher
Master's Research - Université de Montréal
Collaborating researcher - Concordia University
Principal supervisor :
Collaborating researcher - Université de Montréal
Co-supervisor :
Collaborating researcher - Yale
PhD - Université de Montréal
Research Intern - Western Washington University
Principal supervisor :
Postdoctorate - Université de Montréal
Collaborating researcher - McGill University (assistant professor)

Publications

Fixing Bias in Reconstruction-based Anomaly Detection with Lipschitz Discriminators
Alexander Tong
Smita Krishnaswamy
Anomaly detection is of great interest in fields where abnormalities need to be identified and corrected (e.g., medicine and finance). Deep … (see more)learning methods for this task often rely on autoencoder reconstruction error, sometimes in conjunction with other penalties. We show that this approach exhibits intrinsic biases that lead to undesirable results. Reconstruction-based methods can sometimes show low error on simple-to-reconstruct points that are not part of the training data, for example the all black image. Instead, we introduce a new unsupervised Lipschitz anomaly discriminator (LAD) that does not suffer from these biases. Our anomaly discriminator is trained, similar to the discriminator of a GAN, to detect the difference between the training data and corruptions of the training data. We show that this procedure successfully detects unseen anomalies with guarantees on those that have a certain Wasserstein distance from the data or corrupted training set. These additions allow us to show improved performance on MNIST, CIFAR10, and health record data. Further, LAD does not require decoding back to the original data space, which makes anomaly detection possible in domains where it is difficult to define a decoder, such as in irregular graph structured data. Empirically, we show this framework leads to improved performance on image, health record, and graph data.