MBZUAI's AI Quorum workshop featured Yale biostatistics professor Heping Zhang discussing the challenges of using AI and statistics to analyze noisy biological data for health insights. Zhang highlighted the need to develop methods to extract meaningful stories from noisy data to understand brain function and genetic roles in disease regulation. Harvard's Xihong Lin presented recommendations for building an ecosystem using AI and statistics to improve understanding of the relationship between genome sequences and biological functions. Why it matters: This discussion underscores the importance of AI and statistical methods in addressing the complexities of biological data, particularly in understanding neurological diseases like Alzheimer's, and highlights the need for centralized data infrastructure.
KAUST researchers developed a statistical approach to improve the identification of cancer-related protein mutations by reducing false positives. The method uses Bayesian statistics to analyze protein domain data from tumor samples, accounting for potential errors due to limited data. The team tested their method on prostate cancer data, successfully identifying a known cancer-linked mutation in the DNA binding protein cd00083. Why it matters: This enhances the reliability of cancer research at the molecular level, potentially accelerating the discovery of new therapeutic targets.
A new framework for constructing confidence sets for causal orderings within structural equation models (SEMs) is presented. It leverages a residual bootstrap procedure to test the goodness-of-fit of causal orderings, quantifying uncertainty in causal discovery. The method is computationally efficient and suitable for medium-sized problems while maintaining theoretical guarantees as the number of variables increases. Why it matters: This offers a new dimension of uncertainty quantification that enhances the robustness and reliability of causal inference in complex systems, but there is no indication of connection to the Middle East.
Prof. Marc Genton of KAUST has been appointed Editor-in-Chief of Stat, the ISI online journal for rapid dissemination of statistics research. His term will run from January 1, 2015, to December 31, 2017. Genton aims to maintain the journal's rapid publication speed and improve the quality of accepted papers. Why it matters: This appointment highlights KAUST's growing influence and expertise in statistical research on the international stage.
KAUST Professor Hernando Ombao is leading the Biostatistics Group to develop statistical models for projecting hospitalization surges during the COVID-19 pandemic. The group uses techniques like time series analysis and stationary subspace analysis to understand complex biological processes. The models aim to provide public health officials with accurate hospitalization estimates under varying scenarios. Why it matters: This research contributes to preparedness and resource allocation in healthcare systems during public health crises, with potential applications beyond COVID-19.
KAUST Ph.D. student Zhuo Qu and fellow students from the Statistics Program launched the first American Statistical Association (ASA) student chapter outside of the U.S. in October 2019. The chapter aims to encourage and provide opportunities for KAUST students interested in statistics to connect with statisticians worldwide. In 2020, the chapter plans to organize seminars and connect students interested in statistics and data mining. Why it matters: This initiative highlights KAUST's commitment to fostering a global network of statisticians and promoting data analysis skills among its students, enhancing its role as a hub for international collaboration in STEM fields.
KAUST Professor Marc Genton and his former postdoc Stefano Castruccio jointly won the 2017 Wilcoxon Award for their paper in Technometrics. Their paper, "Compressing an ensemble with statistical models: An algorithm for global 3D spatio-temporal temperature," details a data-compression scheme for climate simulations. The method reduces data-storage requirements and accelerates climate research capacity. Why it matters: This award highlights KAUST's contribution to statistical methods for climate modeling and big data analysis, particularly relevant for studying renewable energy resources in Saudi Arabia.
KAUST Professor Marc Genton has been selected as the 2020 Georges Matheron Lecturer of the International Association for Mathematical Geosciences. Genton will present a lecture at the 36th International Geological Congress in Delhi, India, focusing on geostatistics, climate model outputs, and the ExaGeoStat software developed at KAUST. His lecture will cover Matheron's theory of regionalized variables and showcase ExaGeoStat, a high-performance software for geostatistics with exascale computing capability developed at KAUST. Why it matters: This recognition highlights KAUST's contributions to advanced statistical methods and high-performance computing in geosciences, enhancing its international reputation in these fields.