Portrait of Audrey Durand

Audrey Durand

Associate Academic Member
Canada CIFAR AI Chair
Assistant Professor, Université Laval, Department of Computer Science and Software Engineering

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

Postdoctorate - Université Laval
PhD - Université Laval
Master's Research - Université Laval
Master's Research - Université Laval
Co-supervisor :
Master's Research - Université Laval
PhD - McGill University

Publications

Randomized Confidence Bounds for Stochastic Partial Monitoring
Maxime Heuillet
Ola Ahmad
Development of AI-assisted microscopy frameworks through realistic simulation in 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 the image acquisition… (see more) and analysis phases. Development of artificial intelligence (AI)-assisted super-resolution microscopy is often limited by the access to large biological datasets, as well as by the difficulties to benchmark and compare approaches on heterogeneous samples. We demonstrate the benefits of a realistic STED simulation platform, pySTED, for the development and deployment of AI-strategies for super-resolution microscopy. The simulation environment provided by pySTED allows the augmentation of data for the training of deep neural networks, the development of online optimization strategies, and the training of reinforcement learning models, that can be deployed successfully on a real microscope.
Randomized Confidence Bounds for Stochastic Partial Monitoring
Maxime Heuillet
Ola Ahmad
GrowSpace: A reinforcement learning environment for plant architecture
Yasmeen Hitti
Ionelia Buzatu
Manuel Del Verme
Mark Lefsrud
Florian Golemo
Deep reinforcement learning for continuous wood drying production line control
François-Alexandre Tremblay
Michael Morin
Philippe Marier
Jonathan Gaudreault
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… (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.
Chronic Stress Exposure Alters the Gut Barrier: Sex-Specific Effects on Microbiota and Jejunum Tight Junctions
Ellen Doney
Laurence Dion-Albert
Francois Coulombe-Rozon
Natasha Osborne
Renaud Bernatchez
Sam E.J. Paton
Fernanda Neutzling Kaufmann
Roseline Olory Agomma
José L. Solano
Raphael Gaumond
Katarzyna A. Dudek
Joanna Kasia Szyszkowicz
Manon Lebel
Alain Doyen
Flavie Lavoie-Cardinal
Marie-Claude Audet
Caroline Menard
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
Association Rules Mining with Auto-Encoders
Th'eophile Berteloot
Richard Khoury
Association rule mining is one of the most studied research fields of data mining, with applications ranging from grocery basket problems to… (see more) explainable classification systems. Classical association rule mining algorithms have several limitations, especially with regards to their high execution times and number of rules produced. Over the past decade, neural network solutions have been used to solve various optimization problems, such as classification, regression or clustering. However there are still no efficient way association rules using neural networks. In this paper, we present an auto-encoder solution to mine association rule called ARM-AE. We compare our algorithm to FP-Growth and NSGAII on three categorical datasets, and show that our algorithm discovers high support and confidence rule set and has a better execution time than classical methods while preserving the quality of the rule set produced.
Reducing touching eyes, nose and mouth ('T-zone') to reduce the spread of infectious disease: A prospective study of motivational, volitional and non-reflective predictors.
Mackenzie Wilson
Zachary M. van Allen
Jeremy M. Grimshaw
Jamie C. Brehaut
Jean‐François Lalonde
Douglas G. Manuel
Susan Michie
Robert West
Justin Presseau
BACKGROUND The route into the body for many pathogens is through the eyes, nose and mouth (i.e., the 'T-zone') via inhalation or fomite-base… (see more)d transfer during face touching. It is important to understand factors that are associated with touching the T-zone to inform preventive strategies. PURPOSE To identify theory-informed predictors of intention to reduce facial 'T-zone' touching and self-reported 'T-zone' touching. METHODS We conducted a nationally representative prospective questionnaire study of Canadians. Respondents were randomized to answer questions about touching their eyes, nose, or mouth with a questionnaire assessing 11 factors from an augmented Health Action Process Approach at baseline: intention, outcome expectancies, risk perception, individual severity, self-efficacy, action planning, coping planning, social support, automaticity, goal facilitation and stability of context. At 2-week follow-up, we assessed HAPA-based indicators of self-regulatory activities (awareness of standards, effort, self-monitoring) and self-reported behaviour (primary dependent variable). RESULTS Of 656 Canadian adults recruited, 569 responded to follow-up (87% response rate). Across all areas of the 'T-zone', outcome expectancy was the strongest predictor of intention to reduce facial 'T-zone' touching, while self-efficacy was a significant predictor for only the eyes and mouth. Automaticity was the strongest predictor of behaviour at the 2-week follow-up. No sociodemographic or psychological factors predicted behaviour, with the exception of self-efficacy, which negatively predicted eye touching. CONCLUSION Findings suggest that focusing on reflective processes may increase intention to reduce 'T-zone' touching, while reducing actual 'T-zone' touching may require strategies that address the automatic nature of this behaviour.
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 … (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.
Neural Bandits for Data Mining: Searching for Dangerous Polypharmacy
Alexandre Larouche
Richard Khoury
Caroline Sirois