Audrey Durand

Associate Academic Member

audrey.durand@ift.ulaval.ca

Canada CIFAR AI Chair

Associate professor, Université Laval, Department of Computer Science and Software Engineering

Research Topics

AI for Science

Online Learning

Reinforcement Learning

Website

Google Scholar

Biography

Audrey Durand is an assistant professor in the Department of Computer Science and Software Engineering and in the Department of Electrical and Computer Engineering at Université Laval.

She specializes in algorithms that learn through interaction with their environment using reinforcement learning, and is particularly interested in leveraging these approaches in health-related applications.

Current Students

Julien Armand

Master's Research - Université Laval

julien.armand@mila.quebec

Yohaï-Eliel Berreby

PhD - McGill University

Master's Research - Université Laval

baptiste.bonin@mila.quebec

Hugo Delhaye

Master's Research - Université de Montréal

Principal supervisor :

Lune Bellec

hugo.delhaye@mila.quebec

Erinn Grigsby

Postdoctorate - Université de Montréal

erinn.grigsby@mila.quebec

PhD - Université Laval

maxime.heuillet@mila.quebec

Bruno Joyal

Master's Research - Université Laval

bruno.joyal@mila.quebec

Alexandre Larouche

PhD - Université Laval

alexandre.larouche@mila.quebec

Github

Google Scholar

Randy Lefebvre

PhD - Université Laval

randy.lefebvre@mila.quebec

Hadi Moazen

PhD - Université Laval

hadi.moazen@mila.quebec

Caro Strickland

Postdoctorate - Université Laval

caro.strickland@mila.quebec

Website

Github

Google Scholar

Publications

GrowSpace: A reinforcement learning environment for plant architecture

Mark Lefsrud

2024-01-15

Computers and Electronics in Agriculture (published)

doi.org

Deep reinforcement learning for continuous wood drying production line control

François-Alexandre Tremblay

Audrey Durand

Michael Morin

Philippe Marier

Jonathan Gaudreault

2023-12-31

Computers in Industry (published)

doi.org

Development of AI-assisted microscopy frameworks through realistic simulation with pySTED

Anthony Bilodeau

Albert Michaud-Gagnon

Julia Chabbert

Benoit Turcotte

Jörn Heine

Audrey Durand

Flavie Lavoie-Cardinal

The integration of artificial intelligence into microscopy systems significantly enhances performance, optimizing both image acquisition and… (see more) analysis phases. Development of artificial intelligence-assisted super-resolution microscopy is often limited by access to large biological datasets, as well as by difficulties to benchmark and compare approaches on heterogeneous samples. We demonstrate the benefits of a realistic stimulated emission depletion microscopy simulation platform, pySTED, for the development and deployment of artificial intelligence strategies for super-resolution microscopy. pySTED integrates theoretically and empirically validated models for photobleaching and point spread function generation in stimulated emission depletion microscopy, as well as simulating realistic point-scanning dynamics and using a deep learning model to replicate the underlying structures of real images. This simulation environment can be used for data augmentation to train deep neural networks, for the development of online optimization strategies and to train reinforcement learning models. Using pySTED as a training environment allows the reinforcement learning models to bridge the gap between simulation and reality, as showcased by its successful deployment on a real microscope system without fine tuning.

2023-12-31

Nature Machine Intelligence (published)

doi.org

Latent Space Evolution under Incremental Learning with Concept Drift (Student Abstract)

Charles Bourbeau

Audrey Durand

This work investigates the evolution of latent space when deep learning models are trained incrementally in non-stationary environments that… (see more) stem from concept drift. We propose a methodology for visualizing the incurred change in latent representations. We further show that classes not targeted by concept drift can be negatively affected, suggesting that the observation of all classes during learning may regularize the latent space.

2023-06-25

Proceedings of the AAAI Conference on Artificial Intelligence (published)

doi.org

The Influence of Age, Sex, and Socioeconomic Status on Glycemic Control Among People With Type 1 and Type 2 Diabetes in Canada: Patient-Led Longitudinal Retrospective Cross-sectional Study With Multiple Time Points of Measurement

Seyedmostafa Mousavi

Dana Tannenbaum Greenberg

Ruth Ndjaboué

Michelle Greiver

Olivia Drescher

Selma Chipenda Dansokho

Denis Boutin

Jean-Marc Chouinard

Sylvie Dostie

Robert Fenton

Marley Greenberg

Jonathan McGavock

Adhiyat Najam

Monia Rekik

Tom Weisz

Donald J Willison

Audrey Durand

Holly O Witteman

2023-04-26

JMIR Diabetes (published)

doi.org

Interpret Your Care: Predicting the Evolution of Symptoms for Cancer Patients

Rupali Bhati

Jennifer Jones

Audrey Durand

Cancer treatment is an arduous process for patients and causes many side-effects during and post-treatment. The treatment can affect almost … (see more)all body systems and result in pain, fatigue, sleep disturbances, cognitive impairments, etc. These conditions are often under-diagnosed or under-treated. In this paper, we use patient data to predict the evolution of their symptoms such that treatment-related impairments can be prevented or effects meaningfully ameliorated. The focus of this study is on predicting the pain and tiredness level of a patient post their diagnosis. We implement an interpretable decision tree based model called LightGBM on real-world patient data consisting of 20163 patients. There exists a class imbalance problem in the dataset which we resolve using the oversampling technique of SMOTE. Our empirical results show that the value of the previous level of a symptom is a key indicator for prediction and the weighted average deviation in prediction of pain level is 3.52 and of tiredness level is 2.27.

2023-02-18

ArXiv (preprint)

doi.org

arxiv.org

Neural Bandits for Data Mining: Searching for Dangerous Polypharmacy

Alexandre Larouche

Audrey Durand

Richard Khoury

Caroline Sirois

2022-12-09

ArXiv (preprint)

doi.org

arxiv.org

Informing the development of an outcome set and banks of items to measure mobility among individuals with acquired brain injury using natural language processing

Rehab Alhasani

Mathieu Godbout

Audrey Durand

Claudine Auger

Anouk Lamontagne

Sara Ahmed

The banks of items of mobility domains represent a first step toward establishing a comprehensive outcome set and a common language of mobil… (see more)ity to develop the ontology. It enables researchers and healthcare professionals to begin exposing the content of mobility measures as a way to assess mobility comprehensively.

2022-12-08

BMC Neurology (published)

doi.org

Performative Prediction in Time Series: A Case Study

Rupali Bhati

Jennifer Jones

David Langelier

Anthony Reiman

Jonathan Greenland

Kristin Campbell

Audrey Durand

2022-12-01