statistics

Welcome at Kaust UQ School On Numerical Methods For Direct And Inverse Problems 2016 22-28 May

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B9 H1 R2322

KAUST UQ SCHOOL 2016 is an annual base thematic conference at King Abdullah University of Science and Technology held by Raul Tempone, Professor of Applied Mathematics and Computational Sciences at CEMSE (Computer, Electrical and Mathematical Sciences & Engineering Division). Tempone’s interests in the mathematical foundation of computational science and engineering are reflected in this summer school. The school’s goal is to provide participants with an overview on the most recent research progress in the field of uncertainty quantification, with emphasis to • Multi-Level and Multi-Index

Spatio-Temporal Data Analysis by Transformed Gaussian Processes

Yuan Yan, PhD Student of Prof. Marc Genton, KAUST

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B2 L5 R5220

In the analysis of spatio-temporal data, statistical inference based on the assumption that the data follow a Gaussian distribution is ubiquitous due to its many attractive properties. However, data collected from different fields of science, such as meteorology, astronomy, biology and medicine, rarely meet the assumption of Gaussianity. One option is to apply a monotonic transformation to the data such that the transformed data have a distribution that is close to Gaussian. In this thesis, we focus on a flexible two-parameter family of transformations, the Tukey g-and-h (TGH) transformation

Statistical models based on stochastic partial differential equations

Prof. David Bolin, Statistics, KAUST

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B9 L2 H1 R2322

The talk will give an overview of some recent developments of statistical models based on stochastic partial differential equations. We will in particular focus on equations with non-local differential operators or non-Gaussian driving noise, and explain when any why such models are useful. As motivating applications, analysis of longitudinal medical data and ocean waves will be considered.

L'oreal-UNESCO UK & Ireland For Women In Science Fellowships (FWIS) for the article "Modelling particulate matter pollution from vegetation fires in South-East Asia".

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Paola Crippa (presently at University of Newcastle, UK) is one of the winners of the highly contested 2015 L'oreal-UNESCO UK & Ireland For Women In Science Fellowships (FWIS) for the article "Modelling particulate matter pollution from vegetation fires in South-East Asia".

Brain waves in boxes

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A statistical method helps to identify abnormal signals in electroencephalograms and locate their source in the brain.

Conference on Advances in Uncertainty Quantification Methods, Algorithm and Applications (#UQAW2016)

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The 4th consecutive year that KAUST is holding the UQAW annual event dedicated to reporting on the state-of-the-art and latest advances in the methods used to quantify the calculation uncertainties. The aim of the discussion forum in exchanging information on innovative algorithm design as well as their practical applications and performances in experimental settings.

Feeding in the moonlight

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Consistent around the world's ocean, the phases of the moon affect the upward migration of the world’s most abundant type of fish for feeding.

A clearer view of rainfall patterns

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Improved random modeling allows scientists to generate realistic patterns of high-frequency rainfall.

Getting a handle on extremes

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By tapping into the power of extreme value theory, an international team of researchers including Raphaël Huser from the University's Computer, Electrical and Mathematical Science and Engineering Division has developed a statistical model that overcomes the shortcomings of previous schemes to provide a reliable basis for climate research and the prediction of drought and flood. The model can accurately describe observed rainfall data and reliably predict the likelihood of future extreme events. The analysis of hourly or daily rainfall data presents many challenges for researchers and

Making space for climate simulations

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A statistics-based data compression scheme cuts data storage requirements for large-scale climate simulations by as much as 98 percent.

Getting the jump on industrial failures

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A statistics-driven method for the early detection of emerging problems in industrial processes could improve industrial safety, reliability and productivity.

Keeping antennas at peak performance

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Sensitive detection of partial faults in antenna systems could prevent performance degradation in wireless networks.

A safe sweep of weather data

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A statistical technique for automatically cleaning erroneous data from weather-balloon observations will improve the accuracy of weather forecasting.

Improving connections for spatial analysis

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A statistical model that accounts for common dependencies in spatial data yields more realistic results for studies of temperature, wind and pollution levels.

Smart healthcare uses stats to spot a stumble

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Statistical monitoring technology can detect serious falls and immediately warn healthcare providers.

Going to extremes to predict natural disasters

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A systematic approach to selecting and configuring statistical models improves predictions of extreme events.

Letting the data speak for itself

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A new statistical approach for environmental measurements lets the data determine how to model extreme events.

Dataset size counts for better predictions

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A statistical technique for modeling large datasets improves interpretation of climate and environmental data.

Diagnosing the impacts of health policy

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Study design allows more rigorous evaluation of complex healthcare interventions.

One bad apple can spoil a robot swarm

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Coordinated robot swarms can achieve amazing feats, but even a single faulty unit can have serious consequences.

Modeling where the wind blows

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A new statistical technique could be used to more accurately locate global wind resources.

Revealing hidden relationships in data

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A method to visualize hidden statistical structure helps make sense of environmental data.

Making data more accessible

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Ying Sun is a multi-award-winning statistician who is inspired by the value of statistics in solving real-world problems.

Pulling rank on spatial statistics

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A technique that uses the power of computing could solve statistical problems cheaper and faster than current methods.

Challenging statistics of weather extremes

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More accurate statistical modeling of extreme weather will improve forecasting and disaster mitigation.

Zooming in on brain function

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Faster computations will allow researchers to see the finer details of brain activity in functional brain imaging.

Second year Ph.D. student to receive top statistics award

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KAUST second year Ph.D. student Yuxiao Li has been selected to receive a Student Paper Award from the American Statistical Association (ASA) for his paper entitled "Efficient Estimation of Non-stationary Spatial Covariance Functions with Application to High-resolution Climate Model Emulation."

Point patterns help to predict landslides

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Rainfall simulations in statistical models could allow accurate prediction of dangerous landslides.

Connecting the dots to unlock brain structure

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Mapping genetic influences on connections between neural networks could lead to a better understanding of brain organization and behavior.

Making loudspeakers resonate like atoms

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Tiny loudspeakers can efficiently produce low-frequency sounds due to effects similar to the quantum physics governing atoms.

Searching for wind for the future

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The first quantification of wind energy in Saudi Arabia points to high wind power potential for many decades to come.

Reining in computational complexity

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A more efficient approach to modeling spatial data involving thousands of variables keeps computation time in check.

Solving real-world problems

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A tool developed by Håvard Rue has transformed data analysis, interpretation and communication. It has been applied broadly: from modeling the spread of infectious diseases to mapping fish stocks.

Stimulating deeper insights into brain function

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Modeling changes in brain activity over time provides deeper insights into learning and behavioral responses.

New stats apps show a virtual reality

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Harnessing the power of virtual reality will help to visualize data and improve statistical models.

Clustering helps unlock secrets of the human brain

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The latest statistical methods from research on complex high-dimensional environmental data also yield powerful tools for interpreting brain activity.

Simple statistics can be good enough

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Study of the mismatch between spatial environmental data and a commonly used statistical analysis suggests simpler statistics are sufficient in many cases.

Taking aim at outliers

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Automatic detection of uncharacteristic data sequences could change the way data is processed and analyzed.

Cutting datasets down to size

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A powerful statistical tool could significantly reduce the burden of analyzing very large datasets.

Epilepsy study shows the shape of things to come

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Pyramidal graphs resulting from statistical analyses of EEG recordings can improve our understanding of epileptic seizures.

Honing in on hotspots in spatial data

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More accurate detection of hotspot clusters provides new insights into the behavior of air pollution.

Marc Genton received an ISI Service Award 2019

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Marc Genton, Chair and Distinguished Professor of the Statistics Program of KAUST and Head of the Spatio-Temporal Statistics and Data Science Research Group, received an International Statistical Institute (ISI) Service Award 2019 for his efforts and leadership as Editor-in-Chief of the journal Stat. Professor Genton received his award at the Awards Ceremony during the World Statistical Congress (WSC) 2019 held in Kuala Lumpur, Malaysia, 18-23 August 2019.

Shaking up the landslide model

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Areas without earthquake monitoring systems could model the tremors by using data from local landslides.

Testing barley's salt tolerance is a numbers game

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Factors influencing the tolerance of barley to saline soils have been uncovered using an advanced robust statistical technique.

A sharper view of flood risk

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Extreme weather patterns and regions at risk of flooding could be easier to spot using a new statistical model for large spatial datasets.

Blueprint may power up KSA's wind energy future

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High-resolution analysis of wind speed across Saudi Arabia can help fast track the expansion of the Kingdom’s emerging world-class wind energy industry.

Wind forecasts power up for reliable energy production

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Prediction of wind speed and direction for up to several hours in advance improves Saudi Arabia’s wind energy potential.

Deep learning in the emergency department

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Harnessing the power of deep learning leads to better predictions of patient admissions and flow in emergency departments.

Modeling the neighborhood boosts landslide prediction

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A prediction model that considers multiple landslides over time in a given region may improve the accuracy of early warning systems.

KAUST STAT students come together for second ASA chapter meeting

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Members of the KAUST American Statistical Association (ASA) student chapter recently came together for the group’s second online meeting held on Tuesday, November 10, 2020. The meeting served as an orientation exercise for new KAUST Statistics (STAT) Program students while also highlighting the shared experience of STAT Ph.D. candidates: Jian Cao, Wanfang Chen, Yuxiao Li, and Gaurav Agarwal.

Modeling rainfall drop by drop

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A high-frequency model developed using data from new high-precision rain gauges gives fresh insight into the dynamics of rain and runoff events.

Getting the inside track on street design

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Large-scale analysis of pedestrian data from three European cities examines how building density and street design influence pedestrian behavior.

A brainwave from social networks

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The statistics used to understand social networks reveal the diversity of functional connections in the brain.

Predicting future hotspots in the Red Sea

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Predicting areas of the Red Sea most susceptible to overheating by the end of the century could help conservation efforts to protect its precious coral reefs.

Star light, star bright…as explained by math

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The evolving periodicity of the brightness of certain types of stars can now be described mathematically.

Heatwave model shows it is hotter in more places, more often

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New modeling shows that heatwaves across Europe have increased in both frequency and spatial extent over the past century.

Deeper defense against cyber attacks

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To address the growing threat of cyberattacks on industrial control systems, a KAUST team including Fouzi Harrou, Wu Wang and led by Ying Sun has developed an improved method for detecting malicious intrusions.

Finding true protein hotspots in cancer research

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Novel statistical methods help tell the difference between "false positives" and true detections of protein domain hotspots that could be linked to cancer.

Competition sheds light on approximation methods for large spatial datasets

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Marc Genton, Huang Huang and colleagues from KAUST organized a global competition with 21 competing teams to compare different approximation methods for analyzing large spatial datasets.

Decrypting the interdependence of cryptocurrencies

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A team of KAUST statisticians has analyzed the price movements of the top five cryptocurrencies to further understand their market behavior. Their model will help investors assess the risks of investing in cryptocurrencies.

Getting the tail straight

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KAUST’s Extreme Statistics Group has developed an improved statistical model for analyzing environmental data of extreme events, such as heavy rainfall or strong wind data.

Finding Terra Incognita

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A new study addresses the difficulty in modeling atmospheric turbulence at sub-kilometer resolution, which is challenging due to atmospheric variability, meteorology and changeable terrain such as mountains and cities.

Can machine learning help predict disease spread?

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Machine learning techniques can provide accurate forecasting of the spread of viruses during pandemics. Under the supervision of Ying Sun and Fouzi Harrou, Yasminah Alali developed an approach that removes human bias and assumptions, predicting pandemic evolution more accurately.

Extreme analysis of seizures

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CEMSE researchers Matheus Guerrero, Raphaël Huser and Hernando Ombao developed a new approach for analyzing seizures in epilepsy patients by applying extreme value theory to real EEG seizure data.

Professor Bolin earns American Statistical Association Section on Statistics and the Environment Early Investigator Award

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Prof. David Bolin has been selected for the American Statistical Association Section on Statistics and the Environment Early Investigator (ENVR) Award for his outstanding contributions to environmental statistics.

A model for millions of locations

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CEMSE statisticians developed a framework which enables modeling of a range of meteorological and environmental datasets from up to 2 million locations globally.