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.
KAUST and LONGi, a solar energy company, have signed an MoU to collaborate on solar energy innovation. The partnership aims to develop and deploy solar technologies, focusing on improving the efficiency of photovoltaic technologies. Joint projects will include testing PV module concepts, developing new materials, and optimizing performance, alongside talent development programs. Why it matters: This collaboration aligns with Saudi Vision 2030 and the Saudi Green Initiative, fostering sustainable energy solutions and local expertise in the region.
The Human Phenotype Project (HPP), led by researchers from Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), published findings in Nature Medicine detailing an understanding of the health-disease continuum. The HPP involves deep and longitudinal profiling of approximately 28,000 participants, collecting diverse data including medical history, lifestyle, blood tests, and molecular profiling. The project aims to create AI-based predictive models for disease onset and progression, and digital twins to simulate interventions. Why it matters: This research can transform precision medicine and preventative care in the UAE by creating personalized digital twins that can simulate interventions and predict health trajectories.
This paper introduces a longitudinal control system for autonomous racing vehicles with combustion engines, translating trajectory-tracking commands into low-level vehicle controls like throttle, brake pressure, and gear selection. The modular design facilitates integration with various trajectory-tracking algorithms and vehicles. Experimental validation on the EAV24 racecar during the Abu Dhabi Autonomous Racing League at Yas Marina Circuit demonstrated the system's effectiveness, achieving longitudinal accelerations up to 25 m/s². Why it matters: This research contributes to the advancement of autonomous racing technology in the region, showcasing practical applications in high-performance scenarios and fostering innovation in vehicle control systems.
Søren Brunak presented deep learning approaches for analyzing disease trajectories using data from 7-10 million patients in Denmark and the USA. The models predict future outcomes like mortality and specific diagnoses, such as pancreatic cancer, using 15-40 years of patient data. Disease trajectories and explainable AI can generate hypotheses for molecular-level investigations into causal aspects of disease progression. Why it matters: This research demonstrates the potential of large-scale patient data and AI to improve disease prediction and generate hypotheses for further investigation into disease mechanisms relevant to regional healthcare systems.
Eduardo da Veiga Beltrame, bioinformatics lead at ImYoo (a Caltech spinout), presented on scalable methods for single-cell omics data analysis, including kallisto|bustools and scvi-tools. He highlighted their use in ImYoo's decentralized longitudinal study on Inflammatory Bowel Disease (IBD), where patients self-collect capillary blood samples. Beltrame also discussed his research on STEM education programs in Brazil as a visiting scholar at UC Berkeley. Why it matters: This highlights the growing trend of decentralized clinical studies leveraging advanced single-cell technologies for precision medicine, showcasing the potential of remote data collection and analysis in understanding complex diseases.
Science writer Dava Sobel spoke at KAUST in 2019 about the importance of longitude and precision timekeeping for navigation. She discussed the historical difficulties in determining longitude, contrasting it with the ease of finding latitude. Sobel highlighted the Longitude Act of 1714 and figures like John Harrison who addressed these challenges. Why it matters: This lecture exposed the KAUST community to the historical context of navigation and the crucial role of timekeeping, relevant to contemporary technologies like GPS.
Professor Eran Segal presented The Human Phenotype Project, a longitudinal cohort study with over 10,000 participants. The project aims to identify molecular markers and develop prediction models for disease using deep profiling techniques including medical history, lifestyle, blood tests, and microbiome analysis. The study provides insights into drivers of obesity, diabetes, and heart disease, identifying novel markers at the microbiome, metabolite, and immune system level. Why it matters: Such large-scale phenotyping initiatives could inform personalized medicine approaches relevant to the Middle East's specific health challenges.