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.
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.
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.
Laura Koesten, Assistant Professor of Human-Computer Interaction at MBZUAI, studies how people interpret and interact with data, driven by the increasing need to adapt digital environments to people. Her work focuses on making data more accessible and understandable for various audiences, drawing from her Ph.D. research at the University of Southampton and postdoctoral work at King's College London. She emphasizes the importance of data literacy for citizens in understanding how data is used in decision-making systems. Why it matters: This research contributes to bridging the gap between complex AI systems and human understanding, fostering broader societal engagement with data-driven technologies in the UAE and beyond.
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 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 Assistant Professor Paula Moraga has authored a new textbook, "Spatial Statistics for Data Science: Theory and Practice with R," based on her lectures. The book is available for free on her website and in hard copy through the publisher. Dr. Moraga's research focuses on developing statistical methods and computational tools for geospatial data analysis and health surveillance, with applications in reducing disease burden and identifying high-risk populations. Why it matters: The publication strengthens KAUST's research profile in spatial data science and offers valuable open-source resources for addressing critical challenges in public health and resource management within Saudi Arabia and the broader region.