Researchers propose a spatio-temporal model for high-resolution wind forecasting in Saudi Arabia using Echo State Networks and stochastic partial differential equations. The model reduces spatial information via energy distance, captures dynamics with a sparse recurrent neural network, and reconstructs data using a non-stationary stochastic partial differential equation approach. The model achieves more accurate forecasts of wind speed and energy, potentially saving up to one million dollars annually compared to existing models.
A research paper by Dr. Reda Alami of the AI and Digital Science Research Center (AIDRC) at TII has been accepted for publication at the 14th Asian Conference on Machine Learning (ACML 2022). The paper addresses sequential decision-making under uncertainty in non-stationary environments, proposing a Bayesian Change-Point Detection with Thompson Sampling (Bayesian-CPD-TS) algorithm. The algorithm combines decision-making under uncertainty and sequential detection of abrupt changes. Why it matters: This recognition highlights the growing AI research capabilities within the UAE and its contribution to the global machine learning community.
KAUST Ph.D. student Yuxiao Li received a Student Paper Award from the American Statistical Association (ASA) for his paper on efficient estimation of non-stationary spatial covariance functions. The award-winning paper is Li's first research paper at KAUST, completed as a member of the Environmental Statistics Group led by Professor Ying Sun. His research focuses on short-term space-time precipitation modeling, addressing the challenges of modeling rainfall zeros and amounts along with complex spatio-temporal dependencies. Why it matters: This award recognizes KAUST's contributions to advanced statistical methods for environmental modeling, highlighting the university's strength in addressing complex environmental challenges.
KAUST Ph.D. alumna Sabrina Vettori and Ph.D. student Yuxiao Li received a Distinguished Student Paper Award at the 2018 Eastern North American Region (ENAR) Spring Meeting of the International Biometric Society. Li's paper focused on efficient estimation for non-stationary spatial covariance functions, while Vettori's paper addressed Bayesian hierarchical modelling of air pollution extremes. Both students were recognized for their contributions to statistical environmental studies and air pollution modeling. Why it matters: This award highlights KAUST's commitment to fostering high-quality research and recognizes the achievements of its students in addressing critical environmental challenges.
This article discusses a talk by Mingming Gong from the University of Melbourne at MBZUAI on bridging causality and machine learning. The talk focuses on using machine learning to discover causal structures from observational data, and leveraging causal structures to improve machine learning generalization and prediction in non-stationary environments. Gong's research explores theoretical foundations and computational innovations in causal structure learning from real-world data. Why it matters: This research direction is crucial for advancing AI systems that can reason about cause and effect, leading to more robust and reliable decision-making in complex environments.
KAUST Ph.D. student Ghulam Qadir received a best poster award at the GRASPA 2019 conference in Italy. The winning poster, titled "Estimation of Spatial Deformation for Non-stationary Processes via Variogram Alignment," was based on Qadir's Ph.D. research project. The research focuses on developing covariance models for multivariate nonstationary random fields with applications to environmental data. Why it matters: This award recognizes KAUST's contribution to environmental statistics and highlights the university's commitment to advancing research in this area.