KAUST Professor Marc Genton received the 2024 Don Owen Award from the San Antonio Chapter of the American Statistical Association. The award recognizes Genton's excellence in research, statistical consultation, and service to the statistical community. Genton's research focuses on large-scale spatial and temporal data, with applications to environmental problems, including wind energy potential in Saudi Arabia. Why it matters: This award highlights KAUST's contributions to statistical research and its application to important environmental challenges in the region.
KAUST Professor Marc Genton has received the Royal Statistical Society’s (RSS) 2023 Barnett Award for his contributions to environmental statistics. Genton's work includes the development of ExaGeoStat, a high-performance software for geostatistics, and the use of spectral methods to emulate climate model outputs. His research includes a five-year study on wind energy potential in Saudi Arabia, informing the Kingdom’s national wind energy strategy. Why it matters: This award recognizes impactful environmental statistics research at KAUST with implications for Saudi Arabia's renewable energy sector and beyond.
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.
KAUST Professor Håvard Rue was honored by the Royal Statistical Society (RSS) with the Guy Medal in Silver for his work on efficient computational techniques. The award recognizes Rue's contributions to the theory underpinning the INLA software, particularly through two influential papers on approximate Bayesian inference and Gaussian fields. Rue's research focuses on computational Bayesian statistics and Bayesian methodology, with the R-INLA project being a core part of his work. Why it matters: Recognition of KAUST faculty by international organizations highlights the institution's growing prominence in statistical research and computational modeling.
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 Ph.D. student Gaurav Agarwal won the best student paper award at the International Indian Statistical Association's 2019 Student Paper Competition for his work on the joint distribution of wind speed and direction. Agarwal's research involved developing a visualization tool for bivariate functional data, which can be used in climate and weather prediction models. He also received a scholarship based on his contributions using R. Why it matters: This award recognizes innovative work in environmental statistics at KAUST, highlighting the university's contributions to data science and statistical learning with applications to climate modeling.
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 Professor Marc Genton received the International Statistical Institute's Service Award 2019 for his leadership as editor-in-chief of the journal Stat. His research group at KAUST focuses on developing statistical tools relevant to Saudi Arabia's knowledge economy transition. Genton is also working with the University of Notre Dame on wind energy implementation and infrastructure assessment for NEOM. Why it matters: This award recognizes KAUST's contributions to statistical research and its application to renewable energy and economic development in Saudi Arabia.
KAUST Assistant Professor Raphaël Huser received the American Statistical Association's 2019 Section on Statistics and the Environment Early Investigator Award for his contributions to environmental statistics. Huser's research focuses on developing models for extreme events observed in space and time. He leads the KAUST extSTAT research group, which develops statistical models to understand the stochastic behavior of rare events. Why it matters: Recognition of KAUST faculty highlights the university's growing prominence in statistical research and its application to environmental challenges in the region.
KAUST alumna Yuan Yan received an honorable mention from the American Statistical Association (ASA) for her paper on "Vector Autoregressive Models with Spatially Structured Coefficients for Time Series on a Spatial Grid." Yan, who graduated from KAUST in 2018, was part of Professor Marc Genton's Spatio-Temporal Statistics & Data Science group. She is now a postdoctoral fellow at Dalhousie University, researching fisheries science using spatial statistical models. Why it matters: This recognition highlights the quality of research and education at KAUST, especially in the field of spatio-temporal statistics, and its impact on addressing real-world sustainability challenges.
KAUST Ph.D. student Jian Cao received a best paper award from the American Statistical Association (ASA) for his paper on computing high-dimensional normal and Student-t probabilities. The paper uses Tile-Low-Rank Quasi-Monte Carlo and Block Reordering. Cao, a member of Professor Marc Genton's group, will be recognized at the ASA's Joint Statistical Meetings. Why it matters: This award highlights KAUST's strength in high-performance computing and statistical research, contributing to advancements in handling complex, high-dimensional datasets.
KAUST postdoctoral fellow Carolina Euán received the Sylvia Esterby Presentation Award from the International Environmentrics Society (TIES) for her talk on a spatio-temporal model applied to drought data in Mexico. The research, conducted with KAUST Associate Professor Ying Sun, focuses on modeling dependence between processes observed in two categories, such as dry or rainy days. Euán joined KAUST in 2016 after completing her Ph.D. in statistics from the Research Center in Mathematics (CIMAT), Guanajuato, Mexico. Why it matters: This award recognizes the quality of environmental statistics research being conducted at KAUST and its applicability to understanding complex environmental phenomena in the region and beyond.
KAUST master’s degree student Samuel Horváth won a best poster award at the Data Science Summer School (DS3) in Paris for his poster entitled "Nonconvex Variance Reduced Optimization with Arbitrary Sampling". The poster is based on a paper of the same name currently under review and is joint work between Horváth and his supervisor Professor Peter Richtárik from the KAUST Visual Computing Center. Horváth's research interests are at the interface of statistical learning and big data optimization, with a focus on randomized methods for non-convex problems. Why it matters: This award recognizes the quality of KAUST's research and its students' contributions to the field of data science and optimization.
KAUST Ph.D. student Yuxiao Li received a Student Paper Award from the American Statistical Association (ASA) for his paper on efficient estimation of non-stationary spatial covariance functions. The award-winning paper is Li's first research paper at KAUST, completed as a member of the Environmental Statistics Group led by Professor Ying Sun. His research focuses on short-term space-time precipitation modeling, addressing the challenges of modeling rainfall zeros and amounts along with complex spatio-temporal dependencies. Why it matters: This award recognizes KAUST's contributions to advanced statistical methods for environmental modeling, highlighting the university's strength in addressing complex environmental challenges.
KAUST Ph.D. alumna Sabrina Vettori and Ph.D. student Yuxiao Li received a Distinguished Student Paper Award at the 2018 Eastern North American Region (ENAR) Spring Meeting of the International Biometric Society. Li's paper focused on efficient estimation for non-stationary spatial covariance functions, while Vettori's paper addressed Bayesian hierarchical modelling of air pollution extremes. Both students were recognized for their contributions to statistical environmental studies and air pollution modeling. Why it matters: This award highlights KAUST's commitment to fostering high-quality research and recognizes the achievements of its students in addressing critical environmental challenges.
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 Assistant Professor Ying Sun won the 2017 Section on Statistics and the Environment Early Investigator Award. The award recognizes early-career researchers making significant contributions to environmental statistics. The award was given by the American Statistical Association. Why it matters: This highlights KAUST's strength in interdisciplinary research and its faculty's recognition on the international stage.
KAUST Ph.D. student Sabrina Vettori won the 2017 Student Paper Competition sponsored by the Section on Statistics and the Environment of the American Statistical Association. Her winning paper was titled "Bayesian clustering and dimension reduction in multivariate air pollution extremes", co-authored by Huser and Genton. The competition focused on environmental statistics, with winners presenting at the Joint Statistical Meetings. Why it matters: This award recognizes KAUST's contribution to environmental statistics and highlights the university's ability to attract and nurture talent in this critical area.
KAUST researchers from statistics and earth science collaborated to improve earthquake source modeling. They developed a statistical ranking tool to classify 2D fields, applicable to geoscience models like temperature or precipitation. The tool helps compare different 2D fields describing the earthquake source process and quantify inter-event variability. Why it matters: This cross-disciplinary approach enhances the reliability of earthquake rupture models, contributing to better hazard assessment and risk management in seismically active regions.
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.
MBZUAI researchers developed a new conditional independence test (DCT) that determines the dependence of two variables when both are discrete, continuous, or when one is discrete and the other is continuous. The new test addresses cases where variables are inherently continuous but represented in discretized form due to data collection limits. The findings will be presented at the 13th International Conference on Learning Representations (ICLR) in Singapore. Why it matters: This research addresses a fundamental problem in machine learning and statistics, improving causal relationship discovery in mixed datasets common across finance, public health, and other fields.
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.
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.
MBZUAI is hosting an "AI Quorum on Statistics for the Future of AI" in Abu Dhabi, focusing on the intersection of statistics and AI in healthcare. Organized by Professors Tian Zheng (Columbia University) and Hongtu Zhu (UNC), the event gathers experts from top global universities and organizations like Eli Lilly and MD Anderson Cancer Center. The workshop aims to integrate statistical insights into AI research, fostering innovations in the field. Why it matters: By convening international experts, MBZUAI is positioning itself as a hub for interdisciplinary AI research with a focus on healthcare applications.
KAUST Ph.D. student Ghulam Qadir received a best poster award at the GRASPA 2019 conference in Italy. The winning poster, titled "Estimation of Spatial Deformation for Non-stationary Processes via Variogram Alignment," was based on Qadir's Ph.D. research project. The research focuses on developing covariance models for multivariate nonstationary random fields with applications to environmental data. Why it matters: This award recognizes KAUST's contribution to environmental statistics and highlights the university's commitment to advancing research in this area.
Wanfang Chen and Yuxiao Li, a married couple, came to KAUST in August 2016 to pursue Ph.D. studies in statistics under the supervision of Distinguished Professor Marc Genton and Professor Ying Sun respectively. Prior to KAUST, they obtained degrees from the Beijing Institute of Technology, with Chen also attending Xiamen University and Li attending the University of California, Irvine. Both students have completed their first academic papers and have submitted the papers to journals. Why it matters: This highlights KAUST's ability to attract international talent in STEM fields, contributing to its research output and global reputation.
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 Assistant Professor of Statistics Ying Sun won the 2016 Abdel El-Shaarawi Young Researcher (AEYR) Award in June. The award recognizes young researchers for contributions to statistics and related fields. Why it matters: This highlights KAUST's commitment to attracting and recognizing talented researchers in data science and related fields.
This article discusses a talk by Gábor Lugosi on "network archaeology," specifically the problems of root finding and broadcasting in large networks. The talk addresses discovering the past of dynamically growing networks when only a present-day snapshot is observed. Lugosi's research interests include machine learning theory, nonparametric statistics, and random structures. Why it matters: Understanding the evolution and origins of networks is crucial for various applications, including analyzing social networks, biological systems, and the spread of information.