KAUST researchers collaborated on a study published in Nature analyzing microbiomes in 170 glacier-fed streams worldwide. The study, led by EPFL, identified a unique microbiome distinct from other cryospheric systems, with almost half the bacteria endemic to specific mountain ranges. KAUST's sequencing efforts helped create a global atlas of these threatened microbiomes. Why it matters: Understanding these microbiomes is crucial for monitoring the impact of climate change on vital freshwater sources originating from glaciers.
KAUST's Winter Enrichment Program (WEP) 2016 featured a poster competition highlighting research by graduate students, postdocs, and international undergraduates. A science fair included shows by science podcast host Dr. Chris Smith, art exhibits, and a visualization lab. Exhibits included "On the Trail of the Glaciers: An Interactive Experience" and short films produced by KAUST students. Why it matters: Such programs foster scientific engagement and communication within the KAUST community and beyond.
KAUST researchers Carlos Preckler and Diego Rivera participated in Saudi Arabia's first scientific mission to Antarctica from January 11 to February 27. They collected ocean samples to study how whale populations mitigate climate change through carbon sequestration. The team aims to quantify the impact of whales on carbon capture and correlate whale population dynamics with carbon sequestration over the past 400 years. Why it matters: This research provides valuable insights into the economic benefits of whale conservation and contributes to global efforts in understanding the role of marine ecosystems in climate change mitigation.
Qirong Ho, co-founder and CTO of Petuum Inc., will be contributing to the "ML Systems for Many" initiative. Petuum is recognized for creating standardized building blocks for AI assembly. Ho also holds a Ph.D. from Carnegie Mellon University and is part of the CASL open-source consortium. Why it matters: Showcases the ongoing efforts to democratize AI development and deployment, making it more accessible and sustainable, although the specific initiative is not further detailed.
Professor Arnab Pain's group at KAUST discovered new insights on how a malaria protein enables parasites to spread malaria in human cells. Professor Haavard Rue's group upgraded the Integrated and Nested Laplace Approximation (INLA) for faster real-time modeling of large datasets. A KAUST-led study examined the stability of Y-series nonfullerene acceptors for organic solar cells. Why it matters: KAUST continues producing impactful research across diverse fields from medicine to climate change, advancing scientific knowledge and potential applications.
MBZUAI Executive Program participants gathered for community-building activities on Jubail Island, including a mangrove walk and dinner. MBZUAI President Eric Xing emphasized the opportunity to build partnerships and an AI community. The event aimed to foster collaboration and understanding among participants to drive positive AI progress. Why it matters: Such initiatives can help bridge divides between organizations and facilitate the responsible development of AI in the UAE.
A team from KAUST's Earth Science and Engineering program visited the site of the ongoing volcanic eruption in Iceland, which began in August 2014. Researchers monitored ground movements related to a collapsing structure near the eruption site using GPS instruments to measure vertical ground displacements. They aim to compare these measurements with satellite radar data to quantify volume changes before, during, and after the eruption. Why it matters: This study exemplifies the application of KAUST's earth science expertise to understanding and monitoring significant geological events, contributing to hazard assessment and risk management in volcanically active regions.
This article discusses approximating a high-dimensional distribution using Gaussian variational inference by minimizing Kullback-Leibler divergence. It builds upon previous research and approximates the minimizer using a Gaussian distribution with specific mean and variance. The study details approximation accuracy and applicability using efficient dimension, relevant for analyzing sampling schemes in optimization. Why it matters: This theoretical research can inform the development of more efficient and accurate AI algorithms, particularly in areas dealing with high-dimensional data such as machine learning and data analysis.