Gaurav Agarwal, a statistics Ph.D. student in the Environmental Statistics Group at KAUST, is researching statistical methods with environmental applications, such as understanding salt tolerance in plants. He is developing a user-friendly web application to make these methods accessible to those with limited statistical backgrounds. Agarwal also focuses on data visualization and outlier detection techniques for quality control of radiosonde wind data. Why it matters: This research contributes to environmental science by providing accessible statistical tools and methods for analyzing complex environmental data, potentially aiding in addressing challenges like plant resilience and climate monitoring.
KAUST Professor Raul Tempone, an expert in Uncertainty Quantification (UQ), has been appointed as an Alexander von Humboldt Professor at RWTH Aachen University in Germany. This professorship will enable him to further his research on mathematics for uncertainty quantification with new collaborators. Tempone believes the KAUST Strategic Initiative for Uncertainty Quantification (SRI-UQ) contributed to this award. Why it matters: This appointment enhances KAUST's visibility and facilitates cross-fertilization between European and KAUST research groups, benefiting both institutions and attracting talent.
KAUST's Computational Bioscience Research Center (CBRC) held a Research Conference on Big Data Analyses in Evolutionary Biology. The conference focused on the impact of large "omics" datasets on evolutionary biology, requiring big data approaches for analysis. Researchers discussed how computer science can contribute to biology and vice versa. Why it matters: Such interdisciplinary events at KAUST can foster innovation at the intersection of computational science and biology, advancing research in both fields.
KAUST professor David Ketcheson uses mathematical modeling to understand COVID-19 transmission. He applies differential equations to explain the progression of SARS-CoV-2, utilizing the SIR model to predict the spread. Ketcheson's analysis suggests that the reproduction number for COVID-19 could be as high as 5, emphasizing the need for social distancing. Why it matters: This highlights the role of mathematical modeling and data analysis in understanding and predicting the spread of infectious diseases, particularly in the context of pandemic response.
Michael Holland from NYU's Center for Urban Science & Progress (CUSP) presented a keynote lecture at KAUST's Winter Enrichment Program (WEP) 2015 on the importance of urban science. CUSP, launched in 2012, aims to make New York City a world capital of science and technology through multi-sector research and education. Holland emphasized how analyzing urban data can improve city government, planning, policy, and citizen engagement. Why it matters: As urbanization increases, the development of urban science and the effective use of urban data become crucial for sustainable and efficient city management in the GCC region and globally.
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 researchers organized a week-long workshop on bioinformatics, covering genomics and transcriptomics data analysis. The workshop targeted students, postdocs, and senior researchers, providing hands-on training in coding and analysis using tools like R, Python, and shell scripts. Attendees with little prior computational biology experience were introduced to fundamental concepts and tools for handling large sequencing datasets. Why it matters: The workshop addresses the increasing need for bioinformatics expertise at KAUST and in the region, crucial for advancing research in fields like evolution and complex diseases.
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.