statistics

Collective Estimation of Multiple Bivariate Density Functions with Application to Angular-sampling-based Protein Structure Prediction

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B1

In this talk we develop a method for simultaneous estimation of density functions for a collection of populations of protein backbone angle pairs. Each log density function in the collection is modeled as a linear combination of a common set of basis functions. The shared basis functions are modeled as bivariate splines on triangulations and are estimated using data. The circular nature of angular data is taken into account by imposing appropriate smoothness constraints across boundaries of the triangles.

Disease Risk Estimation by Combining Case-Control Data with Aggregated Information on the Population at Risk

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B1 L4 R4102

We propose a novel statistical framework by supplementing case–control data with summary statistics on the population at risk for a subset of risk factors. Our approach is to first form two unbiased estimating equations, one based on the case–control data and the other on both the case data and the summary statistics, and then optimally combine them to derive another estimating equation to be used for the estimation.

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

Truth, Knowledge, P-Values, Bayes, & Inductive Inference

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B1 L4 R4102

The talk will concern the role of statistical thinking in the search for Truth. This will bring us to a discussion of P-values, which has been, and still is, a central concept in Statistics and is a critical and highly controversial tool of scientific research. Recently it has elicited much, sometimes heated, debates and discussions. The American Statistical Association (ASA) was even compelled to release an official statement in early March 2016 regarding this issue, and a psychology journal gone to the extreme of banning the use of P-values in articles appearing in its journal!

Modern Techniques of Statistical Optimization for Machine Learning

Tong Zhang, Professor of Computer Science and Mathematics, HKUST

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B9 H1

Many problems in machine learning rely on statistics and optimization. To solve these problems, new techniques are needed. I will show some of these new techniques through selected machine learning problems I have recently worked on, such as nonconvex stochastic optimization, distributed training, adversarial attack, and generative models.

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.

Multivariate Functional Data Analysis and Visualization

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B3 L5 R5220

As a branch of statistics, functional data analysis studies observations regarded as curves, surfaces, or other objects evolving over a continuum. Current methods in functional data analysis usually require data to be smoothed and analyzed marginally, which may hide some outlier information or take extra time on pretreating the data. After exploring model-based fitting for regularly observed multivariate functional data, we explore new visualization tools, clustering, and multivariate functional depths for irregularly observed (sparse) multivariate functional data.

The KAUST 2022 Workshop on Statistics

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B2 B3 A0215

The workshop will cover current trends in statistics and the statistical modeling of big and complex data. Over four days, it will have talks by leading experts as well as two poster sessions where students and postdocs will present their work. All talks will be given Auditorium 0215, between Buildings 2 and 3 at KAUST. Speakers Aritz Adin (Public University of Navarre) Ahmadou Alioum (Bordeaux School of Public Health) Denis Allard (INRAE) Vera Baladandayupathani (University of Michigan) Emily Hector (North Carolina State University) Eduardo Garcia Portugues (Carlos III University of Madrid)

The KAUST 2023 Workshop on Statistics

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B18 R4203

The workshop will feature the latest research on statistical methods and modeling to address real-world challenges in health, environment, and sustainability.

Finding the Face in the Crowd

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An algorithm that maximizes the difference between data categories achieves high accuracy in classifying faces.

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.

2015 Workshop on Computational Space-Time Statistics

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Organizers : Prof. Marc G. Genton , Chair of Spatio-Temporal Statistics and Data Science Group Prof. Raphael Huser, Chair of Extreme Statistics Group​ Prof. Ying Sun​, Chair of Environmental Statistics Group Confirmed speakers: Prof. Sudipto Banerjee, University of California Los Angeles, USA Prof. Souhaib Ben Taieb, Monash University, Australia Prof. Stefano Castruccio, Newcastle University, UK Prof. Miguel de Carvalho, Pontificia Universidad Catolica de Chile,​ Chile Prof. Clément Dombry, University of Franche-Comté , France Prof. Xuming He, University of Michigan, USA Prof. Hsin-Cheng Huang

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.

Postdoc Luigi Lombardo gets an Outstanding Student Poster and PICO EGU Award. Congratulations Luigi!

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The 2016 Outstanding Student Poster and PICO (OSPP) Award is awarded to Luigi Lombardo for the poster/PICO entitled Landslide triggering-thickness susceptibility, a simple proxy for landslide hazard? A test in the Mili catchment (North-Eastern Sicily, Italy). Regarding his work, Luigi said: " In the PICO presentation project we modeled the thickness at the crown of each landslide as the dependent variable. As a result, the predictive maps carried more information than classic susceptibility ones, enabling a better proxy to hazard prediction." You can find Luigi's poster here.

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.

2016 Workshop Statistics for High-Dimensional & Complex Data

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Workshop on Statistics for High-Dimensional and Complex Data, Nov. 6-9, 2016​ Organizers : Prof. Marc G. Genton, Chair of Spatio-Temporal Statistics and Data Science Group Prof. Raphael Huser, Chair of Extreme Statistics Group​ Prof. Ying Sun​, Chair of Environmental Statistics Group Confirmed speakers: Prof. David Bolin, Chalmers University of Technology, Sweden Prof. Probal Chaudhuri, Indian Statistical Institute, India Prof. Dan Cooley, Colorado State University, USA​ Prof. Subhajit Dutta, IIT Kanpur, India Prof. Amanda Hering, Baylor University, USA Dr. Anna Kiriliouk, Université

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.

Assistant Professor Ying Sun wins American Statistical Association award

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​​​​Ying Sun, KAUST assistant professor of applied ​mathematics and computational science​, won the 2017 Section on Statistics and the Environment (ENVR) Early Investigator Award from the American Statistical Association (ASA). Sun received her award at the 2017 Joint Statistical Meetings (JSM), held from July 29 to August 3 in Baltimore, Maryland, U.S. Sun's award recognizes her important contributions to the field of environmental statistics that help to address major problems like climate change and environmental pollution. At KAUST, Sun leads the Environmental Statistics Group in the

Professor Sun receives the Environment Early Investigator Award 2017

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Ying Sun, Professor of Statistics in the Computer, Electrical and Mathematical Sciences & Engineering Division (CEMSE) of the King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, has been awarded the 2017 Early Investigator Award by the American Statistical Association’s Section on Statistics and the Environment (ENVR).

Dataset size counts for better predictions

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

2017 Workshop Modern Statistics for Complex Data Structures

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2017 Workshop Modern Statistics for Complex Data Structures

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.

Ph.D. Student Gaurav Agarwal received an Honorable Mention from ENVR Student Paper Competition

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Ph.D. student Gaurav Agarwal received an Honorable Mention from ENVR student paper competition for his paper entitled "Quantile function modeling with application to salinity tolerance analysis in plant data."

Best 2016 Paper in the Journal of Agricultural, Biological and Environmental Statistics

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Professor Huser will present the work in a special invited session (JABES showcase) at the International Biometric Conference taking place in Barcelona, Spain, in July 2018.

PhD student Sabrina Vettori selected for an ENAR Distinguished Student Paper Award. Congrats!

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PhD student Sabrina Vettori has been selected by the International Biometric Society Eastern North American Region (ENAR) to receive a Distinguished Student Paper Award at the 2018 ENAR Spring Meeting in Atlanta ( https://www.enar.org/meetings/spring2018/index.cfm ) for her manuscript entitled " Bayesian Hierarchical Modelling of Air Pollution Extremes Using Multivariate Max-Stable Processes ". She will present her work at an oral contributed paper session, be invited to attend the ENAR President’s reception and publicly receive their award at the beginning of the ENAR President’s Invited

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.

Ph.D. Student Yuxiao Li won the ENAR Distinguished Student Paper Award

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KAUST Ph.D. student Yuxiao Li has been selected by the Eastern North American Region (ENAR) of the International Biometric Society to receive a Distinguished Student Paper Award at the 2018 ENAR Spring Meeting.

Ph.D. Student Yuxiao Li won the ENVR student paper 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."

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.

How extreme weather exacerbates air pollution

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Simultaneously modeling of air pollutants and weather under extreme conditions highlights the potential for serious health risks.

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.

2018 Workshop on Statistics and Data Science

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The Statistics Program at KAUST is proud to host the 2018 Workshop on Statistics and Data Science. This workshop gathers the leading experts on statistical data science to discuss the current needs, challenges and opportunities of modeling massive and high dimensional data, predicting complex biological and physical processes. The workshop will run from November 12-14. Talks and posters presentations will take place in Auditorium 0215 (between Buildings 4 & 5). Details on the workshop (speakers, abstracts and schedule) can be found here.

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.

Raphaël Huser’s article among the top downloaded articles in Springer’s Environmental Sciences Journals

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Luigi Lombardo, Thomas Opitz, and Raphaël Huser’s article “Point process-based modeling of multiple debris flow landslides using INLA: an application to the 2009 Messina disaster”, published in the scientific journal Stochastic Environmental Research and Risk Assessment (SERRA), in July 2018, is among the top downloaded articles in Springer’s Environmental Sciences Journals for the year 2018. Huser is an Assistant Professor of statistics in the CEMSE division and principal investigator of the Extreme Statistics (extSTAT) Research Group at KAUST.

Students Peng Zhong and Rustam Bekishev get their MS degree

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On December 14, 2018, KAUST welcomed 243 graduates at the University’s ninth Commencement ceremony. Among them, students Rustam Bekishev and Peng Zhong, who is now a Ph.D. student In Statistics in our group, got their MS degree. Congratulations!

Postdoc Carolina Euan received Sylvia Esterby Presentation Award from TIES

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Carolina Euán, a postdoctoral fellow in KAUST Associate Professor Ying Sun's Environmental Statistics research group, recently received the Sylvia Esterby Presentation Award from the International Environmentrics Society (TIES) at the 28th Annual TIES Conference 2018 held from July 16 to 21, 2018, in Guanajuato, Mexico. Euán won the award for her talk entitled "Bernoulli Vector Autoregressive Model with Applications to Spatio-temporal Drought Events in Mexico."

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.

3 Faculty Positions in Statistics 2020 at KAUST

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The 3 faculty positions are in the Statistics Program (http://stat.kaust.edu.sa) within the Computer, Electrical, and Mathematical Sciences and Engineering Division. Currently, the Statistics Program has 7 core faculty and 10 affiliated faculty. We are primarily interested in applicants with strong background in one of the following areas: (1) Statistical Data Science and AI, including network data analysis and high-dimensional statistics (https://apply.interfolio.com/69165); (2) Statistical Climatology, with expertise in statistical analysis of climate model output data, in particular regional climate models, and in physical systems (https://apply.interfolio.com/69167); (3) Statistics for Public Health, including smart health data analysis, personalized medicine, and disease mapping (https://apply.interfolio.com/69168).

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.

2019 Statistics and Data Science Workshop

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From November 18 to 20 the KAUST CEMSE Division has hosted the 2019 Statistics and Data Science Workshop. The workshop, organized by Workshop Chair Professor Ying Sun from the KAUST Environmental Statistics Group, featured 17 keynote talks from eminent data science researchers from Asia, Europe, North America and on-campus. Talks included: personalized recommendation by learning from heterogeneous graph data; dynamic spatial panel models; networks, common shocks, and sequential exogeneity; unbiased Markov chain Monte Carlo, plus many more.

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 second wind for turbine design

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Better prediction of extreme winds at sparsely observed locations could help optimize the design of wind farms.

Pop stats for big geodata

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A universal high-performance computing interface allows popular statistical tools to run efficiently on large geospatial datasets.

Training agents to walk with purpose

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By training a search agent to make smarter exploratory decisions, relational data can be classified more accurately and efficiently.

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.

Today we celebrate World Statistics Day!

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Let’s discover the world of the statisticians at KAUST and be ready to be amazed by their cutting-edge research!

KAUST Environmental Statistics group publishes book exploring multivariate statistical analysis

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The KAUST Environmental Statistic (ES) research group has recently published a book titled "Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches." The book is the outcome of the Core Research Grant (GRG) project led by KAUST Professor Ying Sun, Associate Professor of Statistics.

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.

Mixing precision for model acceleration

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A mixed-precision approach for modeling large geospatial datasets can achieve benchmark accuracy with a fraction of the computational run time.

Beating the curse of dimensionality

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By scanning past data for both partial and complete matches to current observations, a KAUST-led research team has developed a prediction scheme that can more reliably forecast the future trajectory of environmental parameters. The collection of data at regular intervals over time is common in many fields but particularly so in environmental, transportation and biological research. Such data are used to monitor and record the current state and also to help predict what might come in the future. A typical approach is to look for previous patterns or trajectories in the data that match the

Trio of tuning tools for modeling large spatial datasets

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Statistical tools help fine-tune the parameters and approximations of models used to make sense of large spatial datasets.

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.

Getting to the bottom of deadly weather

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Cold still causes far more deaths than heat in India. A study of the correlation between temperature and mortality in the Indian city of Pune has found that cold, rather than heat, is by far the bigger killer.

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.

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.

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.

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.

Better health from better data

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The fast-developing science of geospatial statistics offers new insights into diverse health challenges, from cancer hotspots to problems of air pollution and the spread of COVID-19. Three new projects should yield a better understanding of what causes disease in Saudi Arabia and how to fight it. KAUST also stands to make a real contribution to the region by improving health in the Middle East and North Africa (MENA). The field of geospatial statistics and health surveillance, known as GeoHealth, uses data from multiple sources to create disease maps of geographical areas. When information is

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.

Zuzheng Wang

Ph.D. Student, Statistics

Zhuo Qu

Ph.D. Student, Statistics
Zhuo Qu is a PhD candidate in statistics, under Prof. Marc Genton. Zhuo likes to use statistics methods to interpret the data in the applications and find the new possibility of exploring the new statistics model. Being eager to learn and experience the uncertainty in the research and life, she also like chatting, swimming and piano. Her goal is to be a spare-time pianist among professional statisticians. Education and Early Career King Abdullah University of Science and Technology, Thuwal, January 2019 - December 2022, PhD in Statistics King Abdullah University of Science and Technology

Yuxiao Li

Ph.D., Statistics
Yuxiao Li obtained his Ph.D. degree in Statistics from King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Ying Sun in the Environmental Statistics group. Education and Early Career: Yuxiao obtained his Bachelor of Science degree in Mathematics in 2013 from Beijing Institute of Technology, Beijing, China. In 2015, he graduated with his Master of Science degree in Statistics from the University of California at Irvine, USA. Then, he joined the Ph.D. program in Statistics at KAUST. Research Interest: His research interests lie in the fields such as

Xiran Zhang

Ph.D. Student, Statistics

Wanfang Chen

Ph.D., Statistics
Wanfang Chen obtained a Ph.D. degree in Statistics from King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Marc Genton at the "spatio-temporal statistics and data science" research group. Education and Early Career Wanfang Chen obtained her Bachelor Degree of Science in Mathematics at Beijing Institute of Technology (BIT) in China in 2013. She obtained her Master Degree of Science in Econometrics at Xiamen University in China in 2016. She joined the Ph.D. program in Statistics at KAUST in 2016. Research Interest Spatio-temporal statistics, wind

Sihan Chen

Ph.D. Student, Statistics

Samuel Horváth

Ph.D. Student, Statistics
Samuel Horváth is a Ph.D. candidate at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Peter Richtarik in his research group. Education and Early Career Samuel studied Financial Mathematics at Comenius University in Bratislava, Slovakia and obtained his bachelor in 2017. Then, he joined the MS/PhD program in Statistics and Optimization at KAUST in 2017. In 2018, he had an internship (Machine Learning Intern) at Exponea for 3 months. Research Interest His research interest include Optimization for Big Data

Sagnik Mondal

Ph.D. Student, Statistics
Sagnik Mondal is a PhD student in the Statistics program at KAUST. Prior to joining KAUST, Sagnik completed a Master of Science degree from the Indian Institute of Technology (IIT), Kanpur, India.

Qilong Pan

Ph.D. Student, Statistics
Qilong Pan is a MS/Ph.D. student in the Environmental Statistics group under the supervision of Prof. Ying Sun at the King Abdullah University of Science and Technology (KAUST). He believes that life and study at KAUST would be an unforgettable experience for his whole life. Education and early career B.S. in Statistics in 2021, Wuhan University of Technolgy B.A. in English in 2021, Huazhong University of Science and Technology Research Interests Environmental statistics, Spatio-temporal statistics, Deep Learning

Mary Lai O. Salvana

Ph.D. Student, Statistics
Mary Salvaña is a PhD student in the Spatio-Temporal Statistics and Data Science group under the supervision of Prof. Marc Genton at the King Abdullah University of Science and Technology (KAUST). Education and Early Career MS Applied Mathematics graduate from the Ateneo de Manila University (Philippines) Research Interest Spatio-Temporal Statistics, Wind Energy Modelling, Renewable Energy Modelling.

Kesen Wang

Ph.D. Student, Statistics
Kesen Want is a MS student in the Statistics program at KAUST. Prior to joining KAUST, Kesen completed a Bachelor of Science degree from the University of Maryland College Park in the USA.

Jose Hermenegildo Ramirez Gonzalez

Postdoctoral Research Fellow, Statistics

Jian Cao

Ph.D., Statistics
Jian Cao obtained his Ph.D. degree in Statistics from King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Marc Genton at the "spatio-temporal statistics and data science" research group. His research focused on Monte Carlo simulation, hierarchical matrices, tile-low-rank matrices, and solving high dimensional multivariate normal or Student-t probabilities. Education and Early Career Ph.D. degree from King Abdullah University of Science and Technology Master from Shanghai Jiaotong University Bachelor from University of Science and Technology of China

Israel Martínez-Hernández

Postdoctoral Research Fellow, Statistics
Israel Martínez-Hernández is a postdoctoral fellow at the Spatio-Temporal Statistics & Data Science Group at King Abdullah University of Science and Technology (KAUST). Education and Early Career Israel Martínez-Hernández received his Ph.D. degree in Probability and Statistics from the Center for Research in Mathematics (CIMAT), Mexico, in December 2017. He joined KAUST as a Postdoctoral Research Fellow in February 2018. Research Interest Functional Data Analysis, Functional Time Series, Long Memory Process, NonLinear Cointegration, Spatial Functional Data Analysis, Time Series Analysis

Hanan Alahmadi

Ph.D. Student, Statistics
Hanan Alahmadi is a PhD candidate in Statistics at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Paula Moraga in her Geospatial Statistics and Health Surveillance Research Group. Education Profile M.Sc. Statistics, KAUST, 2021 B.Sc. Mathematics, Taibah University , 2017 Research Interest Geospatial data analysis and health surveillance Spatio-temporal disease data Statistics and spatial epidemiology Geospatial modeling Disease mapping Location Building 1, Al-Khawarizmi, 4th floor, 4212-WS12 Publication Joint Quantile Disease Mapping

Haakon Bakka

Postdoctoral Research Fellow, Statistics
Haakon Bakka is a Post-Doctoral fellow In Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Håvard Rue in his research group. Education and Early Career Haakon received his PhD degree from the Norwegian University of Science and Technology in Trondheim, Norway. Research Interest My focus is on applied Bayesian statistics, often with spatial or space-time models, including priors, constructing random effects, fast inference, software development and data analysis.

Gulzhan Tumenbayeva

Ph.D. Student, Statistics
Gulzhan Tumenbayeva is a Ph.D. candidate In Statistics at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Håvard Rue in his research group. Education and Early Career Gulzhan obtained her Bachelor of Science in Mathematics, Diploma with Honors in 2017 from Nazarbayev University, Astana, Kazakhstan. Then, she joined the MS/PhD program in Statistics at KAUST in 2017. In 2018, she graduated her Master of Science in Statistics at KAUST. Research Interest Her research interest is mainly in Bayesian and computational Statistics as well as

Fouzi Harrou

Research Scientist, Statistics
Dr. Harrou received an M.Sc. degree in telecommunications and networking from the University of Paris VI and the Ph.D. degree in systems optimization and security from the University Technology of Troyes (UTT), France. He was an Assistant Professor at UTT for one year and was an Assistant Professor at the Institute of Automotive and Transport Engineering, Nevers, France, for one year. He was also a Post-Doctoral Research Associate at the Systems Modelling and Dependability Laboratory, UTT, for one year. He is currently a Research Scientist with the Division of Computer, Electrical, and

Ercong Zhang

M.S. Student, Statistics
Ercong Zhang is a MS/Ph.D. student in the Statistics program at KAUST. Prior to joining KAUST, Ercong obtained his Bachelor of Science degree in Statistics from Southern University of Science and Technology (SUSTech), Shenzhen, China.

Cristian Chiuchiolo

Cristian Chiuchiolo is a Ph.D. candidate In Statistics at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Håvard Rue in his research group. Education and Early Career Christian obtained his Bachelor Degree in Statistics in 2015 at Florence, Italy. H e graduated with a Master of Science in Statistics at Bologna, Italy in 2017. Research Interest His research interest is mainly in Bayesian and computational Statistics as well as Probability and applications in R-INLA. Honors and Awards Cristian was announced a SAS Certified Base

Avetik Karagulyan

Postdoctoral Research Fellow, Optimization and Machine Learning
Avetik Karagulyan have joined the team of Peter Richtarik at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST) as a Postdoctoral fellow in November 2021. Education and Early Career Avetik Karagulyan has defended his thesis at Center of Research in Economics and Statistics (CREST), Paris in June 2021 entitled as "Sampling with the Langevin Monte-Carlo" under the supervision of professor Arnak Dalalyan. In 2018, he received MSc Mathematics, Vision, Learning (MVA) diploma at ENS Paris-Saclay with highest honors. Avetik Karagulyan graduated from

Alibi Jangeldin

M.S. Student, Statistics
Alibi Jangeldin was an MSc in Statistical Data Science candidate in Spatio-Temporal Statistics Group in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Marc Genton. Education and Early Career Alibi obtained his bachelor degree in Applied Mathematics from Nazarbayev University in Kazakhstan in 2018. He also worked as a data scientist at Ernst & Young and General Prosecutor's Office of Kazakhstan. M.S. In Statistics from KAUST, Dec. 2019. Research Interest His research interest include data science, statistics and machine learning. Awards

Abdulhakim Qahtan

Abdulhakim Qahtan obtained his Ph.D. degree in Computer Science under the supervision of Professor Xiangliang Zhang at Machine Intelligence and kNowledge Engineering (MINE) Laboratory at King Abdullah University of Science and Technology (KAUST). Dissertation Title: Efficient Estimation of Dynamic Density Function with Applications in Streaming Data. First employment: Postdoctoral researcher at QCRI in Qatar. Research Interests Abdulhakim's research interests included Data mining, Statistics​​​ and Applying statistical models in machine learning. Selected Publications Basma Alharbi, Abdulhakim