KAUST researchers developed a machine learning algorithm to control a deformable mirror within the Subaru Telescope's exoplanet imaging camera, compensating for atmospheric turbulence. The algorithm, which computes a partial singular value decomposition (SVD), outperforms a standard SVD by a factor of four. The KAUST team received a best paper award at the PASC Conference for this work, which has already been deployed at the Subaru Telescope. Why it matters: This advancement enables sharper images of exoplanets, facilitating their identification and study, and showcases the impact of optimizing core linear algebra algorithms.
MBZUAI researchers presented a new machine learning method at ICLR for uncovering hidden variables from observed data. The method, called "complementary gains," combines two weak assumptions to provide identifiability guarantees. This approach aims to recover true latent variables reflecting real-world processes, while solving problems efficiently. Why it matters: The research advances disentangled representation learning by finding minimal assumptions necessary for identifiability, improving the applicability of AI models to real-world data.
MBZUAI Professor Kun Zhang is developing machine learning techniques to identify hidden causal variables, which are underlying concepts driving cause-and-effect relationships. Zhang and colleagues from Carnegie Mellon University are presenting a new approach for this at ICML 2024. Their method, causal representation learning, assumes that measured variables are generated by unobserved latent variables. Why it matters: Uncovering hidden causal relationships can significantly advance understanding in various fields by revealing the underlying mechanisms driving observed phenomena.
A new paper from MBZUAI researchers explores using ChatGPT to combat the spread of fake news. The researchers, including Preslav Nakov and Liangming Pan, demonstrate that ChatGPT can be used to fact-check published information. Their paper, "Fact-Checking Complex Claims with Program-Guided Reasoning," was accepted at ACL 2023. Why it matters: This research highlights the potential of large language models to address the growing challenge of misinformation, with implications for maintaining information integrity in the digital age.
The paper proposes a method for causal inference using satellite image time series to determine the impact of interventions on climate change, focusing on quantifying deforestation due to human causes. The method uses computer vision and deep learning to detect forest tree coverage levels over time and Bayesian structural causal models to estimate counterfactuals. The framework is applied to analyze deforestation levels before and after the hyperinflation event in Brazil in the Amazon rainforest region.
The KAUST Amateur Astronomy Association (AAA), led by Ph.D. student Daniel Corzo, uses telescopes to observe the night sky. The group organizes events to view celestial objects like Saturn and the Milky Way from locations with low light pollution. Corzo's interest in astronomy was sparked by visits to NASA's Johnson Space Center and science fiction literature. Why it matters: Such initiatives promote scientific curiosity and engagement within the KAUST community, potentially inspiring further interest in STEM fields in Saudi Arabia.
KAUST collaborated with NASA's Langley Research Center to launch six weather balloons from KAUST's Coastal & Marine Laboratory, reaching an altitude of 35 kilometers. The balloons were equipped with instruments to measure meteorological properties and characterize the optical properties of aerosols, including a Compact Optical Backscatter Aerosol Detector (COBALD). The research focuses on understanding the impact of dust aerosols on the Arabian Peninsula, including their effects on climate, air quality, and solar energy. Why it matters: This collaboration advances understanding of atmospheric aerosols in the region, with implications for climate modeling, solar energy efficiency, and Red Sea ecosystems.
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.