Portrait de Audrey Durand

Audrey Durand

Membre académique associé
audrey.durand@ift.ulaval.ca
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
Site web
Google Scholar
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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

Yohaï-Eliel Berreby
Doctorat - McGill
me@yberreby.com
Site web
Github
Google Scholar
Baptiste Bonin
Maîtrise recherche - Université Laval
Hugo Delhaye
Maîtrise recherche - UdeM
Superviseur⋅e principal⋅e :
Lune Bellec
Mathieu Godbout
Doctorat - Université Laval
Github
Maxime Heuillet
Doctorat - Université Laval
Yasmeen Hitti
Doctorat - McGill
Co-superviseur⋅e :
Gauthier Gidel
Bruno Joyal
Maîtrise recherche - Université Laval
Alexandre Larouche
Doctorat - Université Laval
Github
Google Scholar
Randy Lefebvre
Maîtrise recherche - Université Laval
Kaoutar Mimouni
Maîtrise recherche - Université Laval
Hadi Moazen
Doctorat - Université Laval

Publications

Platform-based Adaptive Experimental Research in Education: Lessons Learned from The Digital Learning Challenge
Ilya Musabirov
Mohi Reza
Haochen Song
Steven Moore
Pan Chen
Harsh Kumar
Tong Li
John Stamper
Norman Bier
Anna Rafferty
Thomas Price
Nina Deliu
Audrey Durand
Michael Liut
Joseph Jay Williams
: We report on our experience with a real-world, multi-experimental evaluation of an adaptive experimentation platform within the XPRIZE Dig… (voir plus)ital Learning Challenge framework. We showcase how EASI (Experiment as a Service) cross-platform software supports quick integration and deployment of adaptive experiments as well as five systematic replications within a 30-day timeframe. The outline the key scenarios of the applicability of platform-supported experiments and reflect on lessons learned from this two-year project that can help researchers and practitioners to integrate adaptive experiments in real-world courses
2025-03-03
Proceedings of the 15th International Learning Analytics and Knowledge Conference (publié)
doi.org
Adaptive Experiments Under High-Dimensional and Data Sparse Settings: Applications for Educational Platforms
Haochen Song
Ilya Musabirov
Ananya Bhattacharjee
Audrey Durand
Meredith Franklin
Anna Rafferty
Joseph Jay Williams
In online educational platforms, adaptive experiment designs play a critical role in personalizing learning pathways, instructional sequenci… (voir plus)ng, and content recommendations. Traditional adaptive policies, such as Thompson Sampling, struggle with scalability in high-dimensional and sparse settings such as when there are large amount of treatments (arms) and limited resources such as funding and time to conduct to a classroom constraint student size. Furthermore, the issue of under-exploration in large-scale educational interventions can lead to suboptimal learning recommendations. To address these challenges, we build upon the concept of lenient regret, which tolerates limited suboptimal selections to enhance exploratory learning, and propose a framework for determining the feasible number of treatments given a sample size. We illustrate these ideas with a case study in online educational learnersourcing examples, where adaptive algorithms dynamically allocate peer-crafted interventions to other students under active recall exercise. Our proposed Weighted Allocation Probability Adjusted Thompson Sampling (WAPTS) algorithm enhances the efficiency of treatment allocation by adjusting sampling weights to balance exploration and exploitation in data-sparse environments. We present comparative evaluations of WAPTS across various sample sizes (N=50, 300, 1000) and treatment conditions, demonstrating its ability to mitigate under-exploration while optimizing learning outcomes.
2025-01-07
ArXiv (prépublication)
arxiv.org
arxiv.org
Adaptive Experiments Under High-Dimensional and Data Sparse Settings: Applications for Educational Platforms
Haochen Song
Ilya Musabirov
Ananya Bhattacharjee
Audrey Durand
Meredith Franklin
Anna Rafferty
Joseph Jay Williams
2025-01-07
ArXiv (prépublication)
arxiv.org
arxiv.org
Development of AI-assisted microscopy frameworks through realistic simulation in 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 the image acquisition… (voir plus) 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.
2024-09-26
Nature Machine Intelligence (publié)
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
2024-07-29
bioRxiv (prépublication)
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
2024-07-29
bioRxiv (prépublication)
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 (AI) into microscopy systems significantly enhances performance, optimizing both the image acquis… (voir plus)ition and analysis phases. Development of 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.
2024-07-29
bioRxiv (prépublication)
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
2024-07-29
bioRxiv (prépublication)
doi.org
Randomized Confidence Bounds for Stochastic Partial Monitoring
Maxime Heuillet
Ola Ahmad
Audrey Durand
2024-07-08
Proceedings of the 41st International Conference on Machine Learning (publié)
doi.org
openreview.net
Data harmonization for Advancing research on Personalized Rehabilitation Interventions for Patients with Traumatic Brain Injury and Stroke: A proof of concept
Dorra Rakia Allegue
Despoina Petsani
Nathalie Ponthon
Evdokimos Konstantinidis
Panagiotis Bamidis
Eva Kehayia
Audrey Durand
Sara Ahmed
Stroke and traumatic brain injury (TBI) are leading causes of morbidity and mortality, affecting survivors’ mobility and social participat… (voir plus)ion. Although personalized interventions could positively impact survivors' recovery, the effectiveness of such interventions remains unclear. Open-access data repositories can provide access to multiple shared data which could help uncover new evidence of effective interventions; however, harmonizing data between different studies requires many steps to make it possible given the various methods of data collection, intervention characteristics and population sociodemographic profile. This proof-of-concept study aimed to describe the steps and anchors that contributed to the development of guiding frameworks to harmonize data across different studies. Data were extracted from the Federal Interagency Traumatic Brain Injury Research (FITBIR) repository and stored on an online cloud platform. The outcome measures were mapped to mobility determinants using the International Classification of Functioning, Disability, and Health (ICF) and Webber framework. The intervention's effect was categorized according to the Minimal Clinically Important Difference (MCID)s of the measures administered. The study proposed a novel framework for intervention features, which aims to enhance our understanding of the mechanisms of action and potential impact of rehabilitation interventions. The framework classified interventions based on their nature, context, specific body systems, dosage, caregiver assistance, and behaviour change strategies. In conclusion, this study demonstrated the feasibility of harmonizing data extracted from different sources in the FITBIR repository. Leveraging existing open databases offers tremendous opportunities to advance research on personalized interventions for patients with TBI and stroke and inform decision-making during transitions.
2024-06-26
Petra (publié)
doi.org
On shallow planning under partial observability
Randy Lefebvre
Audrey Durand
2024-05-31
rl-conference.cc/RLC/2024/Workshop/Deployable_RL (publié)
openreview.net
openreview.net
Neural Active Learning Meets the Partial Monitoring Framework
Maxime Heuillet
Ola Ahmad
Audrey Durand
We focus on the online-based active learning (OAL) setting where an agent operates over a stream of observations and trades-off between the … (voir plus)costly acquisition of information (labelled observations) and the cost of prediction errors. We propose a novel foundation for OAL tasks based on partial monitoring, a theoretical framework specialized in online learning from partially informative actions. We show that previously studied binary and multi-class OAL tasks are instances of partial monitoring. We expand the real-world potential of OAL by introducing a new class of cost-sensitive OAL tasks. We propose NeuralCBP, the first PM strategy that accounts for predictive uncertainty with deep neural networks. Our extensive empirical evaluation on open source datasets shows that NeuralCBP has favorable performance against state-of-the-art baselines on multiple binary, multi-class and cost-sensitive OAL tasks.
2024-04-26
auai.org/UAI/2024/Conference (poster)
doi.org
openreview.net