Publications

Pierre Cauchy

2025-04-06

Proceedings of the International Conference on Industrial Engineering and Operations Management (publié)

A Comparative Analysis of AI Models for Short-Term Solar Irradiance Forecasting

Saad Benbrahim

Abdelaziz Berrado

Hanane Dagdougui

2025-04-06

Proceedings of the International Conference on Industrial Engineering and Operations Management (publié)

ECLARE: multi-teacher contrastive learning via ensemble distillation for diagonal integration of single-cell multi-omic data

Dylan Mann-Krzisnik

Anjali Chawla

Gustavo Turecki

Corina Nagy

Yue Li

Integrating multimodal single-cell data such as scRNA-seq with scATAC-seq is essential for decoding gene regulatory networks, but remains di… (voir plus)fficult due to feature harmonization and limited paired multiome data. We introduce ECLARE, a framework that uses multi-teacher ensemble knowledge distillation with contrastive learning and optimal-transport alignment to integrate unpaired single-cell multi-omic datasets. Across benchmarks, ECLARE achieves competitive performance for multimodal integration and biological structure preservation. We further demonstrate utility in a major depressive disorder case study using unpaired snRNA-seq and snATAC-seq, identifying transcription factor–target gene programs that are differentially regulated with sex- and cell-type specificity. Finally, ECLARE learns continuous representations that capture longitudinal structure, highlighting altered neurodevelopmental programs associated with depression in female subjects. Altogether, ECLARE expands the practical reach of multimodal single-cell analysis by enabling diagonal integration of unpaired data with strong biological preservation, facilitating integrative regulatory studies across diverse cohorts and conditions.

2025-04-06

bioRxiv (prépublication)

Enhancing Hybrid Model for Photovoltaic Power Prediction: A Case Study of Morocco

Samira Abousaid

Abdelaziz Berrado

Hanane Dagdougui

2025-04-06

Proceedings of the International Conference on Industrial Engineering and Operations Management (publié)

Predicting greenhouse gas Emissions in Shipping: A Case Study Of Canada

Abdelhak El Aissi

Abdelaziz Berrado

Stephane Carron

2025-04-06

Proceedings of the International Conference on Industrial Engineering and Operations Management (publié)

DiffKillR: Killing and Recreating Diffeomorphisms for Cell Annotation in Dense Microscopy Images

Chen Liu

Danqi Liao

Alejandro Parada-Mayorga

Alejandro Ribeiro

Marcello DiStasio

The proliferation of digital microscopy images, driven by advances in automated whole slide scanning, presents significant opportunities for… (voir plus) biomedical research and clinical diagnostics. However, accurately annotating densely packed information in these images remains a major challenge. To address this, we introduce DiffKillR, a novel framework that reframes cell annotation as the combination of archetype matching and image registration tasks. DiffKillR employs two complementary neural networks: one that learns a diffeomorphism-invariant feature space for robust cell matching and another that computes the precise warping field between cells for annotation mapping. Using a small set of annotated archetypes, DiffKillR efficiently propagates annotations across large microscopy images, reducing the need for extensive manual labeling. More importantly, it is suitable for any type of pixel-level annotation. We will discuss the theoretical properties of DiffKillR and validate it on three microscopy tasks, demonstrating its advantages over existing supervised, semi-supervised, and unsupervised methods.

2025-04-05

ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (publié)

Graph Neural Networks Meet Probabilistic Graphical Models: A Survey

Qian Zhang

2025-04-05

IEEE International Conference on Acoustics, Speech, and Signal Processing (publié)

Hyperedge Representations with Hypergraph Wavelets: Applications to Spatial Transcriptomics

Xingzhi Sun

Charles Xu

João Felipe Rocha

Chen Liu

Benjamin Hollander-Bodie

Laney Goldman

Marcello DiStasio

Michael Perlmutter

In many data-driven applications, higher-order relationships among multiple objects are essential in capturing complex interactions. Hypergr… (voir plus)aphs, which generalize graphs by allowing edges to connect any number of nodes, provide a flexible and powerful framework for modeling such higher-order relationships. In this work, we introduce hypergraph diffusion wavelets and describe their favorable spectral and spatial properties. We demonstrate their utility for biomedical discovery in spatially resolved transcriptomics by applying the method to represent disease-relevant cellular niches for Alzheimer’s disease.

2025-04-05

ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (publié)

Investigating the Effectiveness of Explainability Methods in Parkinson's Detection from Speech

Paolo Torroni

Speech impairments in Parkinson's disease (PD) provide significant early indicators for diagnosis. While models for speech-based PD detectio… (voir plus)n have shown strong performance, their interpretability remains underexplored. This study systematically evaluates several explainability methods to identify PD-specific speech features, aiming to support the development of accurate, interpretable models for clinical decision-making in PD diagnosis and monitoring. Our methodology involves (i) obtaining attributions and saliency maps using mainstream interpretability techniques, (ii) quantitatively evaluating the faithfulness of these maps and their combinations obtained via union and intersection through a range of established metrics, and (iii) assessing the information conveyed by the saliency maps for PD detection from an auxiliary classifier. Our results reveal that, while explanations are aligned with the classifier, they often fail to provide valuable information for domain experts.

2025-04-05

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) (publié)

Latent Representation Learning for Multimodal Brain Activity Translation

Arman Afrasiyabi

Dhananjay Bhaskar

Erica Lindsey Busch

Laurent Caplette

Rahul Singh

Guillaume Lajoie

Nicholas B Turk-Browne

Neuroscience employs diverse neuroimaging techniques, each offering distinct insights into brain activity, from electrophysiological recordi… (voir plus)ngs such as EEG, which have high temporal resolution, to hemodynamic modalities such as fMRI, which have increased spatial precision. However, integrating these heterogeneous data sources remains a challenge, which limits a comprehensive understanding of brain function. We present the Spatiotemporal Alignment of Multimodal Brain Activity (SAMBA) framework, which bridges the spatial and temporal resolution gaps across modalities by learning a unified latent space free of modality-specific biases. SAMBA introduces a novel attention-based wavelet decomposition for spectral filtering of electrophysiological recordings, graph attention networks to model functional connectivity between functional brain units, and recurrent layers to capture temporal autocorrelations in brain signal. We show that the training of SAMBA, aside from achieving translation, also learns a rich representation of brain information processing. We showcase this classify external stimuli driving brain activity from the representation learned in hidden layers of SAMBA, paving the way for broad downstream applications in neuroscience research and clinical contexts.

2025-04-05

ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (publié)

LMAC-TD: Producing Time Domain Explanations for Audio Classifiers

Neural networks are typically black-boxes that remain opaque with regards to their decision mechanisms. Several works in the literature have… (voir plus) proposed post-hoc explanation methods to alleviate this issue. This paper proposes LMAC-TD, a post-hoc explanation method that trains a decoder to produce explanations directly in the time domain. This methodology builds upon the foundation of L-MAC, Listenable Maps for Audio Classifiers, a method that produces faithful and listenable explanations. We incorporate SepFormer, a popular transformer-based time-domain source separation architecture. We show through a user study that LMAC-TD significantly improves the audio quality of the produced explanations while not sacrificing from faithfulness.

2025-04-05

ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (publié)

Principal Curvatures Estimation with Applications to Single Cell Data

Yanlei Zhang

Lydia Mezrag

Xingzhi Sun

Charles Xu

Kincaid MacDonald

Dhananjay Bhaskar