This study analyzes the evolution of data science vocabulary using 16,018 abstracts containing "data science" over 13 years. It identifies new vocabulary introduction and its integration into scientific literature using techniques like EDA, LSA, LDA, and N-grams. The research compares overall scientific publications with those specific to Saudi Arabia, identifying representative articles based on vocabulary usage. Why it matters: The work provides insights into the development of data science terminology and its specific adoption within the Saudi Arabian research landscape.
KAUST held a research conference on Computational and Statistical Interface to Big Data from March 19-21. The conference covered topics like data representation, visualization, parallel algorithms, and large-scale machine learning. Participants came from institutions including the American University of Sharjah, Aalborg University, and others to exchange ideas. Why it matters: The conference highlights KAUST's focus on promoting big data research and collaboration to address challenges and opportunities in various scientific fields within the Kingdom and globally.
This paper focuses on analyzing surveys of women entrepreneurs in the UAE using machine learning techniques. The goal is to extract relevant insights from the data to understand the current landscape and predict future trends. The study aims to support better business decisions related to women in entrepreneurship.
KAUST hosted a regional Women in Data Science (WiDS) conference, part of a global event held at over 100 regional institutions led by Stanford University. The KAUST event featured exclusively female speakers and aimed to highlight data science research and applications. KAUST is launching a 'Women in Data Sciences and Technology' initiative to support women's education and careers in the field. Why it matters: This initiative can help address the underrepresentation of women in data science in Saudi Arabia and the broader region.
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.
This paper introduces an interpretable pipeline that integrates mobility and social media data to analyze human behavior during crises. The framework was evaluated through two case studies, including a longitudinal analysis of UAE COVID-19 behavior from March 2020 to December 2021. The pipeline aligns heterogeneous daily signals, transforms them into binary behavioral states, applies Formal Concept Analysis (FCA) to extract co-occurrence structures, and mines association rules. Results demonstrate clear cross-domain behavioral structures in crises, yielding both scientifically credible and policy-actionable intelligence. Why it matters: This work provides a novel methodological approach for developing actionable crisis management strategies by fusing multimodal data, directly applicable to public health and emergency response in the UAE and the broader region.