Publications

Accelerated green material and solvent discovery with chemistry- and physics-guided generative AI
Eslam G. Al-Sakkari
Ahmed Ragab
Marzouk Benali
Olumoye Ajao
Daria C. Boffito
Hanane Dagdougui
2026-05-31
Artificial Intelligence Chemistry (publié)
doi.org
Automated diagnosis of usual interstitial pneumonia on chest CT via the mean curvature of isophotes
Peter Savadjiev
Morteza Rezanejad
Sahir Bhatnagar
David Camirand
Claude Kauffmann
Kaleem Siddiqi
Ronald J. Dandurand
Patrick Bourgouin
Carl Chartrand-Lefebvre
Alexandre Semionov
To test whether the mean curvature of isophotes (MCI), a geometric image transformation, can be used to improve automatic detection on chest… (voir plus) CT of Usual Interstitial Pneumonia (UIP), a determining radiological pattern in the diagnosis of Interstitial Lung Diseases (ILD). This retrospective study included chest CT scans from 234 patients (123 female,111 male; mean age: 61.6 years; age range: 18-90 years) obtained at two independent institutions between 2007 and 2024. Three different classification models were trained on the original CT images and separately on MCI-transformed CT images: (1) a previously published deep learning model for classifying fibrotic lung disease on chest CT, (2) a classification pipeline based on the EfficientNet-V2 convolutional neural network architecture, and (3) a non-deep-learning model based on the functional principal component analysis (FPCA) of density functions of voxel intensity. All models were trained on data from the first institution and evaluated on data from the second institution with the recall-macro, precision-macro and F1-macro scores. Performance difference between classifier pairs was tested with the Stuart-Maxwell marginal homogeneity test. For a fixed model architecture and training algorithm, MCI-transformed images yield comparable or better classification performance than the original CT images. The best performance improvement achieved with MCI compared to CT was: recall-macro 0.83 vs 0.57, precision-macro 0.81 vs 0.50, F1-macro 0.80 vs 0.49, p=4.2e-5. MCI may be a valuable addition to existing AI systems for screening for UIP on chest CT. Machine learning methods for identifying usual interstitial pneumonia on chest CT perform better when the input CT images are transformed via the mean curvature of isophotes (MCI), a geometric transformation method known from classical computer vision. Three machine learning models were trained on a dataset of 158 patients from one institution and tested on another dataset of 76 patients from an independent institution to discriminate for usual interstitial pneumonia (UIP) on chest CT in a 3-group classification task. When keeping the network architecture and parameters fixed, changing the input image domain from the original CT to MCI-transformed images improved classification performance (Stuart-Maxwell test, p < 5e-3) MCI may be a valuable addition to existing machine learning systems for screening for UIP on chest CT, whether based on deep learning or on simpler shallow classifiers.
2026-05-31
European Journal of Radiology Open (publié)
doi.org
Automated robust segmentation of the spinal canal on MRI
Abel Salmona
Sandrine Bédard
Jan Valosek
Kalum Ost
Maxime Bouthillier
Gergely David
Maryam Seif
Armin Curt
Nikolai Pfender
Markus Hupp
Patrick Freund
Tomáš Horák
Petr Kudlička
Josef Bednařík
Fauziyya Muhammad
Zachary A. Smith
Julien Cohen-Adad
2026-05-31
European Journal of Radiology Artificial Intelligence (publié)
doi.org
Refining the construct of direct verbal suggestibility: Evidence for a hybrid dimensional–typological latent structure
Jérémy Brunel
Audrey Vanhaudenhuyse
Julie Delage
Karim Jerbi CoCo Lab
Pierre Rainville
David Ogez
Mathieu Landry
2026-04-30
Personality and Individual Differences (publié)
doi.org
AI Methods for Implementation Science (AIM-IS): developing a framework, toolkit, and reporting standard for the responsible use of AI in implementation practice and research
Guillaume Fontaine
Susan Michie
Rinad S. Beidas
Elvin Geng
Christine Fahim
Byron J. Powell
Vivian Welch
James Thomas
J. Chan
Samira Abbasgholizadeh-Rahimi
France Légaré
Janna Hastings
Sylvie D. Lambert
Justin Presseau
Sharon E. Straus
Ruopeng An
Ashrita Saran
Natalie Taylor
Open Science Framework, March 15, 2026: https://doi.org/10.17605/OSF.IO/BX35K.
2026-04-13
Implementation Science (publié)
doi.org
Active search generation for nanophotonic design in the small data regime
Vincent Létourneau
Yuri Grinberg
Dan Kushnir
Yanlei Zhang
Dan-Xia Xu
Guy Wolf
2026-04-09
Machine Learning in Photonics (publié)
doi.org
Risk-seeking conservative policy iteration with agent-state based policies for Dec-POMDPs with guaranteed convergence
Amit Sinha
Matthieu Geist
Aditya Mahajan
Optimally solving decentralized decision-making problems modeled as Dec-POMDPs is known to be NEXP-complete. These optimal solutions are pol… (voir plus)icies based on the entire history of observations and actions of an agent. However, some applications may require more compact policies because of limited compute capabilities, which can be modeled by considering a limited number of memory states (or agent states). While such an agent-state based policy class may not contain the optimal solution, it is still of practical interest to find the best agent-state policy within the class. We focus on an iterated best response style algorithm which guarantees monotonic improvements and convergence to a local optimum in polynomial runtime in the Dec-POMDP model size. In order to obtain a better local optimum, we use a modified objective which incentivizes risk-seeking alongside a conservative policy iteration update. Our empirical results show that our approach performs as well as state-of-the-art approaches on several benchmark Dec-POMDPs, achieving near-optimal performance while having polynomial runtime despite the limited memory. We also show that using more agent states (a larger memory) leads to greater performance. Our approach provides a novel way of incorporating memory constraints on the agents in the Dec-POMDP problem.
2026-04-09
arXiv (prépublication)
doi.org
arxiv.org
Multi-Modal Learning meets Genetic Programming: Analyzing Alignment in Latent Space Optimization
Benjamin Léger
Kazem Meidani
Christian Gagné
Symbolic regression (SR) aims to discover mathematical expressions from data, a task traditionally tackled using Genetic Programming (GP) th… (voir plus)rough combinatorial search over symbolic structures. Latent Space Optimization (LSO) methods use neural encoders to map symbolic expressions into continuous spaces, transforming the combinatorial search into continuous optimization. SNIP (Meidani et al., 2024), a contrastive pre-training model inspired by CLIP, advances LSO by introducing a multi-modal approach: aligning symbolic and numeric encoders in a shared latent space to learn the phenotype-genotype mapping, enabling optimization in the numeric space to implicitly guide symbolic search. However, this relies on fine-grained cross-modal alignment, whereas literature on similar models like CLIP reveals that such an alignment is typically coarse-grained. In this paper, we investigate whether SNIP delivers on its promise of effective bi-modal optimization for SR. Our experiments show that: (1) cross-modal alignment does not improve during optimization, even as fitness increases, and (2) the alignment learned by SNIP is too coarse to efficiently conduct principled search in the symbolic space. These findings reveal that while multi-modal LSO holds significant potential for SR, effective alignment-guided optimization remains unrealized in practice, highlighting fine-grained alignment as a critical direction for future work.
2026-04-08
arXiv (prépublication)
doi.org
arxiv.org
Toward Hardware-Agnostic Quadrupedal World Models via Morphology Conditioning
Mohamad H. Danesh
Chenhao Li
Amin Abyaneh
Anas Houssaini
Kirsty Ellis
Glen Berseth
Marco Hutter
Hsiu-Chin Lin
World models promise a paradigm shift in robotics, where an agent learns the underlying physics of its environment once to enable efficient … (voir plus)planning and behavior learning. However, current world models are often hardware-locked specialists: a model trained on a Boston Dynamics Spot robot fails catastrophically on a Unitree Go1 due to the mismatch in kinematic and dynamic properties, as the model overfits to specific embodiment constraints rather than capturing the universal locomotion dynamics. Consequently, a slight change in actuator dynamics or limb length necessitates training a new model from scratch. In this work, we take a step towards a framework for training a generalizable Quadrupedal World Model (QWM) that disentangles environmental dynamics from robot morphology. We address the limitations of implicit system identification, where treating static physical properties (like mass or limb length) as latent variables to be inferred from motion history creates an adaptation lag that can compromise zero-shot safety and efficiency. Instead, we explicitly condition the generative dynamics on the robot's engineering specifications. By integrating a physical morphology encoder and a reward normalizer, we enable the model to serve as a neural simulator capable of generalizing across morphologies. This capability unlocks zero-shot control across a range of embodiments. We introduce, for the first time, a world model that enables zero-shot generalization to new morphologies for locomotion. While we carefully study the limitations of our method, QWM operates as a distribution-bounded interpolator within the quadrupedal morphology family rather than a universal physics engine, this work represents a significant step toward morphology-conditioned world models for legged locomotion.
2026-04-08
arXiv (prépublication)
doi.org
arxiv.org
Epistemic Robust Offline Reinforcement Learning
Abhilash Reddy Chenreddy
Erick Delage
Offline reinforcement learning learns policies from fixed datasets without further environment interaction. A key challenge in this setting … (voir plus)is epistemic uncertainty, arising from limited or biased data coverage, particularly when the behavior policy systematically avoids certain actions. This can lead to inaccurate value estimates and unreliable generalization. Ensemble-based methods like SAC-N mitigate this by conservatively estimating Q-values using the ensemble minimum, but they require large ensembles and often conflate epistemic with aleatoric uncertainty. To address these limitations, we propose a unified and generalizable framework that replaces discrete ensembles with compact uncertainty sets over Q-values. %We further introduce an Epinet based model that directly shapes the uncertainty sets to optimize the cumulative reward under the robust Bellman objective without relying on ensembles. We also introduce a benchmark for evaluating offline RL algorithms under risk-sensitive behavior policies, and demonstrate that our method achieves improved robustness and generalization over ensemble-based baselines across both tabular and continuous state domains.
2026-04-07
arXiv (prépublication)
doi.org
arxiv.org
LSST Strong Lensing Systems Dark Matter Sensitivity Analysis with Neural Ratio Estimators
Andreas Filipp
Yashar Hezaveh
Laurence Perreault-Levasseur
Daniel Gilman
LSST Dark Energy Science Collaboration
Strong gravitational lensing offers a unique probe of dark matter (DM) on sub-galactic scales, where the abundance and distribution of low-m… (voir plus)ass halos are highly sensitive to the underlying properties of DM particles. In this work, we forecast LSST's sensitivity to DM substructure in galaxy-galaxy strong lenses using simulated samples and neural ratio estimators (NREs). Our simulations include both subhalos within the main deflector and line-of-sight (LOS) halos, with halo masses down to
2026-04-07
arXiv (prépublication)
doi.org
arxiv.org
Robust Mendelian Randomization Estimation using Weighted Quantile Regression
Julien St-Pierre
Archer Y. Yang
Mireille E. Schnitzer
Marc-André Legault
In Mendelian randomization (MR) studies, genetic variants are used as instrumental variables (IVs) to investigate causal relationships betwe… (voir plus)en exposures and outcomes based on observational data. However, numerous genetic studies have shown the pervasive pleiotropy of genetic variants, meaning that many, if not most, variants are associated with multiple traits, potentially violating the core assumptions of IV estimation. Uncorrelated pleiotropy occurs when genetic variants have a direct effect on the outcome that is not mediated by the exposure, while correlated pleiotropy occurs when genetic variants affect the exposure and outcome via shared heritable confounders. In this work, we propose a novel MR method, called MR-Quantile, based on weighted quantile regression (WQR) that is robust to both correlated and uncorrelated pleiotropy. We propose a procedure for selecting the optimal quantile of the ratio estimates through a likelihood-based formulation of WQR using the asymmetric Laplace distribution. Monte Carlo simulations demonstrate the empirical performance of the proposed method, especially in settings with many invalid IVs with weak pleiotropic effects. Finally, we apply our method to study the causal effect of resting heart rate on atrial fibrillation. Genetic variants associated with heart rate were identified in a genome-wide association study of 425,748 individuals from the VA Million Veteran Program, and used as instruments in a two-sample MR analysis with summary statistics from a genetic meta-analysis of 228,926 AF cases across eight studies.
2026-04-07
arXiv (prépublication)
doi.org
arxiv.org