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.
KAUST researchers collaborated with the Paris Observatory and the National Astronomical Observatory of Japan (NAOJ) to develop advanced Extreme-AO algorithms for habitable exoplanet imaging. The new algorithms, powered by KAUST's linear algebra code running on NVIDIA GPUs, optimize and anticipate atmospheric disturbances. The implemented Singular Value Decomposition (SVD) algorithm won an award at the PASC Conference 2018 and is used at the Subaru Telescope in Hawaii. Why it matters: This advancement enhances the ability to image exoplanets, potentially leading to breakthroughs in the search for habitable planets using ground-based telescopes.
KAUST researchers developed a new algorithm for detecting cause and effect in large datasets. The algorithm aims to find underlying models that generate data, helping uncover cause-and-effect dynamics. It could aid researchers across fields like cell biology and genetics by answering questions that typical machine learning cannot. Why it matters: This advancement could equip current machine learning methods with abilities to better deal with abstraction, inference, and concepts such as cause and effect.
KAUST Ph.D. student Jinhui Xiong won the best paper award at the 24th International Symposium on Vision, Modeling, and Visualization in Germany for his paper "Stochastic Convolutional Sparse Coding". The paper, co-authored with KAUST Professors Peter Richtárik and Wolfgang Heidrich, introduces a novel stochastic spatial-domain solver for Convolutional Sparse Coding (CSC). The proposed algorithm outperforms state-of-the-art solutions in terms of execution time and offers an improved representation for learning dictionaries from sample images. Why it matters: This award recognizes significant research in efficient image representation and dictionary learning, contributing to advancements in visual computing and AI at KAUST.
KAUST Ph.D. student Lukas Larisch won the Parameterized Algorithms and Computational Experiments (PACE) 2017 Challenge in the Optimal Tree Decomposition Challenge, solving more instances than competitors. He received the award at the International Symposium on Parameterized and Exact Computation (IPEC 2017) in Vienna, Austria. Larisch is pursuing his Ph.D. at KAUST and working in the University's Extreme Computing Research Center, focusing on acoustics and graph structure theory. Why it matters: This recognition highlights KAUST's contribution to advanced computer science research and its ability to attract and foster talented researchers in niche areas like parameterized complexity.