The Saudi Data and AI Authority (SDAIA) has launched a national AI and data curriculum to be implemented across all universities in Saudi Arabia. The curriculum aims to equip students with foundational knowledge in AI and data science, regardless of their field of study. The initiative is part of Saudi Arabia's broader efforts to develop a skilled workforce in line with Vision 2030. Why it matters: This nationwide curriculum signals a commitment to building a future-ready workforce capable of leveraging AI across diverse sectors in the Kingdom.
The Saudi Data and AI Authority (SDAIA) has launched a national AI and data curriculum to be implemented across all universities in Saudi Arabia. The curriculum aims to equip students with essential skills in AI and data science, regardless of their field of study. The initiative is part of Saudi Arabia's broader efforts to build a skilled workforce and advance its digital transformation agenda. Why it matters: This nationwide curriculum signals a major push to develop a homegrown AI talent pool and accelerate AI adoption across various sectors in Saudi Arabia.
The Korea Institute of Science and Technology Information (KISTI) has signed MOUs with the Kuwait Institute for Scientific Research (KISR) and Kuwait University (KU) to cooperate in AI and data science. The partnerships aim to enhance research capabilities and promote technological innovation in both countries through joint projects and knowledge sharing. KISTI will collaborate with KISR on AI-based smart environmental monitoring and with KU on developing data analysis and AI technologies. Why it matters: These partnerships can accelerate the development and adoption of AI technologies in Kuwait and foster collaboration between Korean and Kuwaiti researchers.
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.
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.
Dr. Paula Moraga, an Assistant Professor at KAUST, has been awarded the 2023 Letten Prize for her work on disease surveillance systems. The prize recognizes researchers under 45 for contributions to health, development, environment, and equality. Moraga's research enables early epidemic detection, and she was selected from 164 applicants. Why it matters: This award highlights KAUST's contributions to public health research and underscores the importance of AI and data science in addressing global health challenges.
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.
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 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.
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.
MBZUAI's Computational Biology department hosted the first Data Carpentry workshop in the UAE, a global initiative focused on foundational data science skills for researchers. The workshop included 35 participants from academia and industry, covering topics like data tidiness, cloud computing, command lines, and data wrangling. 70% of the participants were female, and several were Emirati nationals. Why it matters: This initiative addresses the growing need for computational skills in genomics research, bridging the gap between wet lab experiments and data analysis for scientists in the UAE.
Faris Almalik, an MBZUAI alumnus, discusses his journey from mechanical engineering to becoming a senior data scientist at the Department of Government Enablement – TAMM in Abu Dhabi. Almalik emphasizes his passion for continuous learning and knowledge sharing, which has driven his career across defense, education, finance, and government sectors. He published his first paper on adversarial attacks on medical imaging at MICCAI during his time at MBZUAI. Why it matters: This highlights the success of MBZUAI in producing impactful AI professionals who contribute to both research and practical applications in key sectors within the UAE.
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.
Emilio Porcu from Khalifa University presented on temporally evolving generalized networks, where graphs evolve over time with changing topologies. The presentation addressed challenges in building semi-metrics and isometric embeddings for these networks. The research uses kernel specification and network-based metrics and is illustrated using a traffic accident dataset. Why it matters: This work advances the application of kernel methods to dynamic graph structures, relevant for modeling evolving relationships in various domains.