Audrey Durand

julien.armand@mila.quebec

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

baptiste.bonin@mila.quebec

Hugo Delhaye

Maîtrise recherche - UdeM

Superviseur⋅e principal⋅e :

Lune Bellec

hugo.delhaye@mila.quebec

Erinn Grigsby

Postdoctorat - UdeM

erinn.grigsby@mila.quebec

Site web

Github

alexandre.larouche@mila.quebec

Bruno Joyal

Maîtrise recherche - Université Laval

bruno.joyal@mila.quebec

Alexandre Larouche

Doctorat - Université Laval

Github

randy.lefebvre@mila.quebec

Randy Lefebvre

Doctorat - Université Laval

Hadi Moazen

Doctorat - Université Laval

hadi.moazen@mila.quebec

Caro Strickland

Postdoctorat - Université Laval

caro.strickland@mila.quebec

Site web

Github

Publications

Knowledge by omission: the significance of omissions in the 5-choice serial reaction time task

Caroline Vouillac-Mendoza

Serge H. Ahmed

Karine Guillem

The 5-choice serial reaction time task (5-CSRTT) is commonly used to assess attention in rodents. Manipulation of this task by decreasing th… (voir plus)e light stimulus duration is often used to probe attentional capacity and causes a decrease in accuracy and an increase in omissions. However, although a decrease in response accuracy is commonly interpreted as a decrease in attention, it is more difficult to interpret an increase in omissions in terms of attentional performance. Here we present a series of experiments in rats that seeks to investigate the origins of these key behavioral measures of attention in the 5-CSRTT. After an initial training in the 5-CSRTT, rats were tested in a variable stimulus duration procedure to increase task difficulty and probe visual attentional capacity under several specific controlled conditions. We found that response accuracy reflects visuospatial sustained attentional processing, as commonly interpreted, while response omission reflects rats’ ignorance about the stimulus location, presumably due to failure to pay attention to the curved wall during its presentation. Moreover, when rats lack of relevant information, they choose not to respond instead of responding randomly. Overall, our results indicate that response accuracy and response omission thus correspond to two distinct attentional states.

2024-03-05

Psychopharmacology (publié)

GrowSpace: A reinforcement learning environment for plant architecture

Mark Lefsrud

2024-01-15

Computers and Electronics in Agriculture (publié)

Deep reinforcement learning for continuous wood drying production line control

François-Alexandre Tremblay

Michael Morin

Philippe Marier

Jonathan Gaudreault

2023-12-31

Computers in Industry (publié)

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

Anthony Bilodeau

Albert Michaud-Gagnon

Julia Chabbert

Benoit Turcotte

Jörn Heine

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)

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

Charles Bourbeau

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)

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

Holly O Witteman

2023-04-26

JMIR Diabetes (publié)

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

Rupali Bhati

Jennifer Jones

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)

arxiv.org

Neural Bandits for Data Mining: Searching for Dangerous Polypharmacy

Alexandre Larouche

Richard Khoury

Caroline Sirois

2022-12-09

ArXiv (prépublication)

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

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)

Performative Prediction in Time Series: A Case Study

Rupali Bhati

Jennifer Jones

David Langelier

Anthony Reiman

Jonathan Greenland

Kristin Campbell

2022-12-01

NeurIPS.cc/2022/Workshop/TS4H (poster)

openreview.net

Annotation Cost-Sensitive Deep Active Learning with Limited Data (Student Abstract)

Renaud Bernatchez

Flavie Lavoie-Cardinal

2022-06-27

Proceedings of the AAAI Conference on Artificial Intelligence (publié)

Biomedical Research and Informatics Living Laboratory for Innovative Advances of New Technologies in Community Mobility Rehabilitation: Protocol for Evaluation and Rehabilitation of Mobility Across Continuums of Care

Sara Ahmed

Philippe Archambault

Claudine Auger

Joyce Fung

Eva Kehayia

Anouk Lamontagne

Annette Majnemer

Sylvie Nadeau

Joelle Pineau

Alain Ptito

Bonnie Swaine

Rapid advances in technologies over the past 10 years have enabled large-scale biomedical and psychosocial rehabilitation research to improv… (voir plus)e the function and social integration of persons with physical impairments across the lifespan. The Biomedical Research and Informatics Living Laboratory for Innovative Advances of New Technologies (BRILLIANT) in community mobility rehabilitation aims to generate evidence-based research to improve rehabilitation for individuals with acquired brain injury (ABI). This study aims to (1) identify the factors limiting or enhancing mobility in real-world community environments (public spaces, including the mall, home, and outdoors) and understand their complex interplay in individuals of all ages with ABI and (2) customize community environment mobility training by identifying, on a continuous basis, the specific rehabilitation strategies and interventions that patient subgroups benefit from most. Here, we present the research and technology plan for the BRILLIANT initiative. A cohort of individuals, adults and children, with ABI (N=1500) will be recruited. Patients will be recruited from the acute care and rehabilitation partner centers within 4 health regions (living labs) and followed throughout the continuum of rehabilitation. Participants will also be recruited from the community. Biomedical, clinician-reported, patient-reported, and brain imaging data will be collected. Theme 1 will implement and evaluate the feasibility of collecting data across BRILLIANT living labs and conduct predictive analyses and artificial intelligence (AI) to identify mobility subgroups. Theme 2 will implement, evaluate, and identify community mobility interventions that optimize outcomes for mobility subgroups of patients with ABI. The biomedical infrastructure and equipment have been established across the living labs, and development of the clinician- and patient-reported outcome digital solutions is underway. Recruitment is expected to begin in May 2022. The program will develop and deploy a comprehensive clinical and community-based mobility-monitoring system to evaluate the factors that result in poor mobility, and develop personalized mobility interventions that are optimized for specific patient subgroups. Technology solutions will be designed to support clinicians and patients to deliver cost-effective care and the right intervention to the right person at the right time to optimize long-term functional potential and meaningful participation in the community. PRR1-10.2196/12506

2022-05-31

JMIR Research Protocols (publié)