Audrey Durand

Membre académique associé

Chaire en IA Canada-CIFAR

Professeure adjointe, Université Laval, Département d'informatique et de génie logiciel

Sujets de recherche

Apprentissage en ligne

Apprentissage par renforcement

IA pour la science

Site web

Google Scholar

Biographie

Audrey Durand est professeure adjointe au Département d’informatique et de génie logiciel ainsi qu’au Département de génie électrique et de génie informatique de l’Université Laval. Elle se spécialise dans les algorithmes qui apprennent par l’interaction avec leur environnement, soit l’apprentissage par renforcement, et s’intéresse particulièrement à l’application de ces approches au domaine de la santé.

Étudiants actuels

Julien Armand

Maîtrise recherche - Université Laval

Doctorat - McGill

Maîtrise recherche - Université Laval

Hugo Delhaye

Maîtrise recherche - UdeM

Superviseur⋅e principal⋅e :

Postdoctorat - UdeM

Doctorat - Université Laval

Bruno Joyal

Maîtrise recherche - Université Laval

Alexandre Larouche

Doctorat - Université Laval

Github

Google Scholar

Randy Lefebvre

Doctorat - Université Laval

Hadi Moazen

Doctorat - Université Laval

Caro Strickland

Postdoctorat - Université Laval

Site web

Github

Google Scholar

Publications

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… (voir plus) 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 (publié)

doi.org

GrowSpace: A reinforcement learning environment for plant architecture

Mark Lefsrud

2023-12-31

Computers and Electronics in Agriculture (publié)

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… (voir plus) 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 (publié)

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 (publié)

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 … (voir plus)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 (prépublication)

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 (prépublication)

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… (voir plus)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 (publié)

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