Publications

Carriers of LRRK2 pathogenic variants show a milder, anatomically distinct brain signature of Parkinson's disease
Jakub Kopal
Andrew Vo
Qin Tao
Tanya Simuni
Lana M. Chahine
Danilo Bzdok
Alain Dagher
Pathogenic LRRK2 gene variants are a major genetic risk factor for both familial and sporadic Pa… (see more)rkinson’s dissease (PD), opening an unattended window into disease mechanisms and potential therapies. Investigating the influence of pathogenic variants in LRRK2 gene on brain structure is a crucial step toward enabling early diagnosis and personalized treatment. Yet, despite its significance, the ways in which LRRK2 genotype affects brain structure remain largely unexplored. Work in this domain is plagued by small sample sizes and differences in cohort composition, which can obscure genuine distinctions among clinical subgroups. In this study, we overcome such important limitations by combining explicit modeling of population background variation and pattern matching. Specifically, we leverage a cohort of 603 participants (including 370 with a PD diagnosis) to examine MRI-detectable cortical atrophy patterns associated with the LRRK2 pathogenic variants in people with PD and carriers without Parkinson’s symptoms. LRRK2 PD patients exhibit milder cortical thinning compared to sporadic PD, with notable preservation in temporal and occipital regions, suggesting a distinct pattern of neurodegeneration. Non-manifesting LRRK2 carriers show no significant cortical atrophy, indicating no structural signs of subclinical PD. We further analyze the relationship between aggregated alpha-synuclein in cerebrospinal fluid and atrophy. We find that those with evidence of aggregated alpha-synuclein experienced pronounced neurodegeneration and increased cortical thinning, possibly defining another aggressive PD subtype. Our findings highlight genetic avenues for distinguishing PD subtypes, which could lead to more targeted treatment approaches and a more complete understanding of Parkinson’s disease progression.
2026-01-02
Communications Medicine (published)
doi.org
RetINaBox: A Hands-On Learning Tool for Experimental Neuroscience
Brune Bettler
Flavia Arias Armas
Erica Cianfarano
Vanessa Bordonaro
Megan Q. Liu
Matthew Loukine
Mingyu Wan
Aude Villemain
Blake A. Richards
Stuart Trenholm
An exciting aspect of neuroscience is developing and testing hypotheses via experimentation. However, due to logistical and financial hurdle… (see more)s, the experiment and discovery component of neuroscience is generally lacking in classroom and outreach settings. To address this issue, here we introduce RetINaBox: a low-cost open–source electronic visual system simulator that provides users with a hands-on tool to discover how the visual system builds feature detectors. RetINaBox includes an LED array for generating visual stimuli and photodiodes that act as an array of model photoreceptors. Custom software on a Raspberry Pi computer reads out responses from model photoreceptors and allows users to control the polarity and delay of the signal transfer from model photoreceptors to model retinal ganglion cells. Interactive lesson plans are provided, guiding users to discover different types of visual feature detectors—including ON/OFF, center-surround, orientation-selective, and direction-selective receptive fields—as well as their underlying circuit computations.
2026-01-01
eNeuro (published)
doi.org
Accelerated and Stable Convergence with Anchored Generalized Optimistic Method
Motahareh Sohrabi
Jianxin You
Simon Lacoste-Julien
Eduard Gorbunov
Gauthier Gidel
We study first-order methods for solving monotone variational inequalities arising in min-max optimization. Classical approaches such as the… (see more) extragradient method rely on two gradient queries per iteration, which limits their analysis and applicability in the online and stochastic settings. We propose a family of Generalized Optimistic Methods with Anchoring (GOMA), which combine two time-scale optimistic updates with an anchoring term inspired by Halpern iteration. In particular, we show that for monotone Lipschitz operators, GOMA achieves an accelerated last-iterate convergence rate of
2025-12-31
International Conference on Machine Learning (Accept (regular))
openreview.net
openreview.net
Active Learning with Non-Uniform Costs for African Natural Language Processing
Bonaventure F. P. Dossou
Ines Arous
Audrey Durand
Jackie Chi Kit Cheung
2025-12-31
Conference of the European Chapter of the Association for Computational Linguistics (published)
doi.org
Additional methodological and statistical details from Testing the scale dependence of plant community assembly processes using imaging spectroscopy
Anna L. Crofts
J. Pablo Arroyo-Mora
Margaret Kalacska
Étienne Laliberté
Mark Vellend
Additional methodological details (including, Figure S1-S2) and detailed statistical results (Figure S3-S4 and Table S1-S3).
2025-12-31
Figshare (unknown)
doi.org
Algorithmic Fairness Across Alignment Procedures and Agentic Systems
Zeyu Tang
Prakhar Ganesh
Awa Dieng
Miriam Rateike
Jamelle Watson-Daniels
Golnoosh Farnadi
Jessica Schrouff
Sanmi Koyejo
AI has transitioned from predictive models to interactive, autonomous agents capable of reasoning, planning, and executing complex goals. As… (see more) the systems increasingly influence social, economic, and scientific decisions, they determine whose interests are represented and whose opportunities are constrained. Ensuring fairness, therefore, is no longer an ethical preference but a practical imperative. As the fairness challenges are fundamentally transformed by advanced AI systems, traditional algorithmic fairness frameworks developed primarily for prediction and/or prediction-based decision-making no longer suffice. This workshop, _Algorithmic Fairness Across Alignment Procedures and Agentic Systems_ (AFAA), emerges at this pivotal moment as a timely forum for rethinking fairness in AI alignment processes and agentic system development. By examining fairness across alignment procedures and agentic systems, this workshop creates a crucial platform for bridging the gap between rapid technical advances in model capabilities and the equally important advances needed in frameworks of algorithmic fairness to govern these powerful systems.
2025-12-31
Workshop Proposals @ International Conference on Learning Representations (published)
openreview.net
openreview.net
An Area-Efficient Routing Solution for Automorphism Ensemble Decoding of Polar Codes
Jiajie Li
Huayi Zhou
Ryan Seah
Marwan Jalaleddine
Warren J. Gross
2025-12-31
IEEE Transactions on Communications (published)
doi.org
Bayesian Deep Learning for Remaining Useful Life Estimation via Stein Variational Gradient Descent
Luca Della Libera
Jacopo Andreoli
Davide Dalle Pezze
Mirco Ravanaelli
Gian Antonio Susto
A crucial task in predictive maintenance is estimating the remaining useful life of physical systems. In the last decade, deep learning has … (see more)improved considerably upon traditional model-based and statistical approaches in terms of predictive performance. However, in order to optimally plan maintenance operations, it is also important to quantify the uncertainty inherent to the predictions. This issue can be addressed by turning standard frequentist neural networks into Bayesian neural networks, which are naturally capable of providing confidence intervals around the estimates. Several methods exist for training those models. Researchers have focused mostly on parametric variational inference and sampling-based techniques, which notoriously suffer from limited approximation power and large computational burden, respectively. In this work, we use Stein variational gradient descent, a recently proposed algorithm for approximating intractable distributions that overcomes the drawbacks of the aforementioned techniques. In particular, we show through experimental studies on simulated run-to-failure turbofan engine degradation data that Bayesian deep learning models trained via Stein variational gradient descent consistently outperform with respect to convergence speed and predictive performance both the same models trained via parametric variational inference and their frequentist counterparts trained via backpropagation. Furthermore, we propose a method to enhance performance based on the uncertainty information provided by the Bayesian models. We release the source code at https://github.com/lucadellalib/bdl-rul-svgd.
2025-12-31
IEEE Trans Autom. Sci. Eng. (published)
doi.org
arxiv.org
Beyond Multi-Token Prediction: Pretraining LLMs with Future Summaries
Divyat Mahajan
Sachin Goyal
Badr Youbi Idrissi
Mohammad Pezeshki
Ioannis Mitliagkas
David Lopez-Paz
Kartik Ahuja
Next-token prediction (NTP) has driven the success of large language models (LLMs), but it struggles with long-horizon reasoning, planning, … (see more)and creative writing, with these limitations largely attributed to teacher-forced training. Multi-token prediction (MTP) partially mitigates these issues by predicting several future tokens at once, but it mostly captures short-range dependencies and offers limited improvement. We propose future summary prediction (FSP), which trains an auxiliary head to predict a compact representation of the long-term future, preserving information relevant for long-form generations. We explore two variants of FSP: handcrafted summaries, for example, a bag of words summary of the future of the sequence, and learned summaries, which use embeddings produced by a reverse language model trained from right to left. Large-scale pretraining experiments (3B and 8B-parameter models) demonstrate that FSP provides improvements over both NTP and MTP across math, reasoning, and coding benchmarks.
2025-12-31
International Conference on Learning Representations (Accept (Poster))
doi.org
openreview.net
A Capacitated Collection-and-Delivery-Point Location Problem with Random Utility Maximizing Customers
David Pinzon Ulloa
Ammar Metnani
Emma Frejinger
2025-12-31
Comput. Oper. Res. (published)
doi.org
arxiv.org
Clinicians' ethical considerations on use of AI-enabled technologies for primary prevention of cardiovascular disease in female patients
Ting Wang
Amrita Sandhu
Kyle Vamvakas
Howard Bergman
Roland Grad
Isabelle Vedel
Marie-Pierre Gagnon
Shahram Yousefi
Samira Abbasgholizadeh-Rahimi
Artificial intelligence (AI) is increasingly used in healthcare to support the prevention and management of cardiovascular disease (CVD); ho… (see more)wever, its ethical implications in clinical practice, particularly for female patients, remain insufficiently explored. This study aimed to explore clinicians' perspectives on the ethical use of AI for preventing and managing cardiovascular disease (CVD) in female patients. A qualitative descriptive design was employed using semi-structured interviews with clinicians practicing in Montreal, Canada. Interviews were conducted online, audio-recorded with participants’ consent, and transcribed for analysis. Data were analyzed using deductive thematic analysis informed by ethical domains in the established AI frameworks. Ethical approval was obtained from McGill University’s Research Ethics Board. The study adhered to the Consolidated Criteria for Reporting Qualitative Research (COREQ) guidelines. A final sample of twelve clinicians was interviewed, with each interview lasting approximately 60 minutes. Four key themes emerged: fairness, privacy and security, explainability, and data integrity. Clinicians expressed concerns that AI-enabled technologies may introduce or reinforce biases affecting certain populations, including older adults, individuals with limited digital literacy, and those lacking reliable internet access or access to digital technologies. Participants also raised concerns regarding data integrity, privacy and security, and emphasized the importance of transparent and understandable AI outputs to support clinical decision-making. Ethical considerations are fundamental to the responsible integration of AI in cardiovascular care. Addressing concerns related to fairness, privacy and security, explainability, and data integrity may strengthen clinician trust and support the implementation of AI-enabled technologies in clinical practice. Future research should explore practical approaches to address these concerns and assess how ethically informed AI systems can be implemented effectively in clinical practice.
2025-12-31
FACETS (published)
doi.org
Clinicians' needs and perspectives on use of AI-enabled technologies for primary prevention of cardiovascular disease in female patients
Ting Wang
Amrita Sandhu
Kyle Vamvakas
Howard Bergman
Roland Grad
Isabelle Vedel
Marie-Pierre Gagnon
Shahram Yousefi
Samira Abbasgholizadeh-Rahimi
This study aimed to (1) explore clinicians’ perspectives of cardiovascular disease (CVD) and risk management in female patients and (2) de… (see more)scribe clinicians’ needs and desired features in AI-enabled tools for primary prevention and management of CVD among female patients. This work employed a qualitative description design. We conducted semi-structured interviews with 12 clinicians in Montreal, Canada. We used inductive thematic analysis to interpret the data. Seven themes emerged from the analysis. Three themes were related to the first objective: complexity in clinical decision-making, limitations of CVD risk assessment tools, and resources and health literacy. Four themes were related to the second objective: AI efficiency, multilingual design, electronic medical record integration, and ease of use. Clinicians reported challenges in supporting female patients at higher risk for CVD and expressed concerns about existing decision support tools. They showed openness to AI-enabled tools like Xi-Care and provided input on desired features to ensure their usability and effectiveness. There is a demand to support clinicians in the primary prevention and management of CVD among female patients. AI-enabled tools could effectively address this demand, provided their development prioritizes clinicians' needs and perspectives to ensure safe and effective implementation.
2025-12-31
FACETS (published)
doi.org