Publications

A Comparative Analysis of AI Models for Short-Term Solar Irradiance Forecasting
Saad Benbrahim
Abdelaziz Berrado
Hanane Dagdougui
Loubna Benabbou
2025-04-07
Proceedings of the International Conference on Industrial Engineering and Operations Management (published)
doi.org
Echoes in the Noise: Posterior Samples of Faint Galaxy Surface Brightness Profiles with Score-based Likelihoods and Priors
Alexandre Adam
Connor Stone
Connor Bottrell
Ronan Legin
Yashar Hezaveh
Laurence Perreaul-Levasseur
Examining the detailed structure of galaxy populations provides valuable insights into their formation and evolution mechanisms. Significant… (see more) barriers to such analysis are the nontrivial noise properties of real astronomical images and the point-spread function, which blurs structure. Here we present a framework which combines recent advances in score-based likelihood characterization and diffusion model priors to perform a Bayesian analysis of image deconvolution. The method, when applied to minimally processed Hubble Space Telescope data, recovers structures which have otherwise only become visible in next-generation James Webb Space Telescope imaging.
2025-04-07
Astronomical Journal (published)
doi.org
arxiv.org
Enhancing Hybrid Model for Photovoltaic Power Prediction: A Case Study of Morocco
Samira Abousaid
Abdelaziz Berrado
Hanane Dagdougui
Loubna Benabbou
2025-04-07
Proceedings of the International Conference on Industrial Engineering and Operations Management (published)
doi.org
InfoGain Wavelets: Furthering the Design of Diffusion Wavelets for Graph-Structured Data
David R. Johnson
Smita Krishnaswamy
Michael Perlmutter
Diffusion wavelets extract information from graph signals at different scales of resolution by utilizing graph diffusion operators raised to… (see more) various powers, known as diffusion scales. Traditionally, the diffusion scales are chosen to be dyadic integers,
2025-04-07
ArXiv (preprint)
doi.org
arxiv.org
Predicting greenhouse gas Emissions in Shipping: A Case Study Of Canada
Abdelhak El Aissi
Loubna Benabbou
Abdelaziz Berrado
Stephane Carron
2025-04-07
Proceedings of the International Conference on Industrial Engineering and Operations Management (published)
doi.org
TAPNext: Tracking Any Point (TAP) as Next Token Prediction
Artem Zholus
Carl Doersch
Yi Yang
Skanda Koppula
Viorica Patraucean
Xu Owen He
Ignacio Rocco
Mehdi S. M. Sajjadi
A. Chandar
Ross Goroshin
2025-04-07
ArXiv (preprint)
doi.org
arxiv.org
The role of AI for MRI-analysis in multiple sclerosis—A brief overview
Jean-Pierre R. Falet
Steven Nobile
Aliya Szpindel
Berardino Barile
Amar Kumar
Joshua D. Durso-Finley
Tal Arbel
Douglas Arnold
2025-04-07
Frontiers Artif. Intell. (published)
doi.org
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… (see more)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 (preprint)
doi.org
Prism: Dynamic and Flexible Benchmarking of LLMs Code Generation with Monte Carlo Tree Search
Vahid Majdinasab
Amin Nikanjam
Foutse Khomh
2025-04-06
ArXiv (preprint)
doi.org
arxiv.org
View-Dependent Deformation Fields for 2D Editing of 3D Models
Martin El Mqirmi
Noam Aigerman
2025-04-06
ArXiv (preprint)
doi.org
arxiv.org
DiffKillR: Killing and Recreating Diffeomorphisms for Cell Annotation in Dense Microscopy Images
Chen Liu
Danqi Liao
Alejandro Parada-Mayorga
Alejandro Ribeiro
Marcello DiStasio
Smita Krishnaswamy
The proliferation of digital microscopy images, driven by advances in automated whole slide scanning, presents significant opportunities for… (see more) 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) (published)
doi.org
arxiv.org
Graph Neural Networks Meet Probabilistic Graphical Models: A Survey
Chenqing Hua
Sitao Luan
Qian Zhang
Jie Fu
Guy Wolf
2025-04-05
IEEE Conference on Acoustics, Speech, and Signal Processing (published)
doi.org