Natasa Przulj at the Barcelona Supercomputing Center is developing an AI framework that fuses multi-omic data to improve precision medicine. The framework uses graph-regularized non-negative matrix tri-factorization (NMTF) and network science algorithms for patient stratification, biomarker prediction, and drug repurposing. It is applied to diseases like cancer, Covid-19, and Parkinson's. Why it matters: This research can enable more personalized and effective treatments by leveraging complex biological data to understand disease mechanisms and tailor therapies.
Dezhen Song from Texas A&M University presented a talk on Co-Modality Active sensing and Perception (C-MAP) for robotics, covering sensor fusion for autonomous vehicles, augmented reality, and remote environmental monitoring. The talk highlighted lessons learned in sensor fusion using autonomous motorcycles and NASA Robonaut as examples. Recent works in robotic remote environment monitoring, especially focused on subsurface surface void and pipeline mapping were discussed. Why it matters: This research explores sensor fusion techniques to enhance robot perception, which could improve the robustness and capabilities of autonomous systems developed and deployed in the Middle East, particularly in challenging environments.
This paper introduces an interpretable pipeline that integrates mobility and social media data to analyze human behavior during crises. The framework was evaluated through two case studies, including a longitudinal analysis of UAE COVID-19 behavior from March 2020 to December 2021. The pipeline aligns heterogeneous daily signals, transforms them into binary behavioral states, applies Formal Concept Analysis (FCA) to extract co-occurrence structures, and mines association rules. Results demonstrate clear cross-domain behavioral structures in crises, yielding both scientifically credible and policy-actionable intelligence. Why it matters: This work provides a novel methodological approach for developing actionable crisis management strategies by fusing multimodal data, directly applicable to public health and emergency response in the UAE and the broader region.
Researchers at MBZUAI have introduced a novel approach to enhance Large Multimodal Models (LMMs) for autonomous driving by integrating 3D tracking information. This method uses a track encoder to embed spatial and temporal data, enriching visual queries and improving the LMM's understanding of driving scenarios. Experiments on DriveLM-nuScenes and DriveLM-CARLA benchmarks demonstrate significant improvements in perception, planning, and prediction tasks compared to baseline models.