Environmental Statistics

Workshop on Computational Space-Time Statistics

-

B1 L4 R4102

Workshop on Computational Space-Time Statistics

Uncertainty Quantification of Tsunami Models

-

B1

In this talk, we first show various strategies for the efficient emulation of simulators having uncertain inputs, with applications to tsunami wave modelling. A fast surrogate of the simulator's time series of outputs is provided by the outer product emulator.

Workshop on Statistical Process Monitoring and Risk Assessment for Engineering and Spatial Environmental Applications

-

B1 L4 R4102

LIST OF SPEAKERS KAUST Environmental Statistics Group, CEMSE Division - Ying Sun (ying.sun@kaust.edu.sa), PI - Fouzi Harrou (fouzi.harrou@kaust.edu.sa), Postdoc - Huang Huang (huang.huang@kaust.edu.sa), PhD Student - Tianbo Chen (tianbo.chen@kaust.edu.sa), PhD Student - Rui Meng (rui.meng@kaust.edu.sa), Master Student - Sulaiman Binkhamis (sulaiman.binkhamis@kaust.edu.sa), Master Student Hydrology and Land Observation Group, BESE Division - Gaohong Yin (gaohong.yin@kaust.edu.sa), Master Student Collaborators - NorEddine Ghaffour (noreddine.ghaffour@kaust.edu.sa), co-I, WDRC - Matthew McCabe

Computational Methods for Large Spatio-temporal Datasets and Functional Data Ranking

-

B1 L4 R4102

In this thesis defense, I will talk about two topics—computational methods for large spatial datasets and functional data ranking. Both are tackling the challenges of big and high-dimensional data.

A talk with PhD student Sabrina Vettori: Catastrophic events are forecastable, after all!

1 min read ·
Environmental disasters occur daily and hit almost every country around the world, taking a heavy toll on lives and economies. According to the United Nations Office for Disaster Risk Reduction, every year there are around 350 reported disasters causing more than 20,000 deaths and affecting the life of almost 100 million people with an overall economic damage of 66.5 billion US$. But this can change.

Professor Sun receives the Environment Early Investigator Award 2017

1 min read ·
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).

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

1 min read ·
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."

Students shine on the statistics stage

1 min read ·
KAUST Ph.D. alumna Sabrina Vettori and Ph.D. student Yuxiao Li have 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.

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

1 min read ·
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

1 min read ·
Rainfall simulations in statistical models could allow accurate prediction of dangerous landslides.

Taking aim at outliers

1 min read ·
Automatic detection of uncharacteristic data sequences could change the way data is processed and analyzed.

Statistics plot pollution to inform policy

1 min read ·
The spatial variation in different air pollution components helps identify possible targets for pollution control.

Honing in on hotspots in spatial data

1 min read ·
More accurate detection of hotspot clusters provides new insights into the behavior of air pollution.

Expanding the scale of dangerous weather prediction

1 min read ·
A more accurate and efficient method of capturing the local factors that lead to extreme rainfall enables better flood prediction across larger regions.

KAUST Ph.D. student receives environmetrics best poster award

1 min read ·
Ghulam Qadir, a third-year Ph.D. student in KAUST Associate Professor Ying Sun's Environmental Statistics research group, recently received a best poster award from the Italian Environmetrics Society (GRASPA) and the International Environmetrics Society (TIES) at the GRASPA 2019 conference held from July 15 to 16 in Pescara, Italy.

Shaking up the landslide model

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

CEMSE Division student spotlight: Gaurav Agarwal

1 min read ·
Gaurav Agarwal firmly believes in the age-old adage that patience is a virtue. Without patience, life’s challenges cannot be overcome, and a steadfast belief in perseverance has served the statistics Ph.D. student well throughout his academic career.

Meet KAUST prospective student: Cristian Felipe Jiménez Varón

1 min read ·
Cristian Felipe Jiménez Varón is an applied mathematics graduate who will join KAUST from the Universidad Nacional de Colombia Sede Manizales (UNAL), Colombia. He also holds dual bachelor's degrees in industrial engineering and chemical engineering fromUNAL .

Turning your desktop to a Supercomputer with ExaGeoStatR

1 min read ·
Turning your desktop to a Supercomputer with ExaGeoStatR Download at https://github.com/ecrc/exageostatR. Remember the old times where you had to leave your R simulations running for the whole night on your desktop due to a large climate/weather dataset? ExaGeoStatR combines the user productivity of R with high performance computing linear algebra software libraries to deliver supercomputing-style environment right below your desk. ExaGeoStatR enables computing the maximum likelihood using large environmental datasets on R, while extracting performance from the underlying hardware resources

Modeling rainfall drop by drop

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

Beating the curse of dimensionality

1 min read ·
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

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

Getting to the bottom of deadly weather

1 min read ·
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.

Getting the tail straight

1 min read ·
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.

A model for millions of locations

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

Professor Marc Genton receives the prestigious 2024 Don Owen Award

1 min read ·
KAUST Professor Marc Genton, a pioneer in environmental statistics, was awarded the prestigious 2024 Don Owen Award for his outstanding contributions to research, consultation, and service to the statistical community.

Zhuldyzay Baki

M.S. Student, Statistics
Zhuldyzay Baki obtained her MSc. in Statistics at the King Abdullah University of Science and Technology in December 2018. After her time at KAUST, she got a job as a Risk Insurance Associate at PwC, Kazakhstan. Areas of expertise and current scientific interests Zhuldyzay Baki's research interests focus on statistics of extremes with applications to environmental data.

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

Wu Wang

Postdoctoral Research Fellow, Statistics
Wu Wang is a postdoctoral fellow at King Abdullah University of Science and Technology (KAUST) working with Professor Ying Sun in the environmental statistics research group. Education Wu Wang received his Ph.D. degree in June 2017 from Fudan University, China. He joined KAUST in September 2017 Wu Wang received his Bachelor's degree in 2008 from East China Normal University Research Interest Wu Wang' s research interests lie in spatial varying coefficients regression with application in particulate matter modeling. He is also interested in functional data analysis, especially functional linear

Stefano Castruccio

Postdoctoral Research Fellow, Statistics
Stephano Castruccio is currently working as an Assistant Professor at the University of Notre Dame (USA). Stefano worked as a Post Doc in KAUST, under Distinguished Professor Marc Genton . He did his PhD from University of Chicago, USA Research Interests Spatio-temporal modeling, climate model sensitivity, hierarchical modeling, monitoring air pollution

Sabrina Vettori

Ph.D. Student, Statistics
Sabrina Vettori was a Ph.D. student in Statistics at the King Abdullah University of Science and Technology (KAUST), under the joint supervision of Prof. Marc Genton and Prof. Raphaël Huser. She successfully defended her Ph.D. thesis entitled " Models and Inference for Multivariate Spatial Extremes" and graduated in December 2017; see his PhD thesis here. Her PhD committee was composed of Professors Marc Genton (chair), Raphaël Huser (co-chair), Anthony Davison (external examiner from EPFL, Switzerland), Ying Sun and Slim Alouini. After her PhD, Sabrina co-founded the successful KAUST start-up

Raphaël Huser

Associate Professor, Statistics
Professor Huser develops novel statistical methodology and machine learning solutions to model and predict extreme events in various applications ranging from climate and earth sciences to finance and neuroscience.

Luigi Lombardo

Postdoctoral Research Fellow, Statistics
Dr. Luigi Lombardo joined the Extreme Statistics Research Group and worked as Postdoctoral Research Fellow from April, 2016, to December, 2018, under the joined supervision of Prof. Raphaël Huser and Prof. Martin Mai. After his postdoc at KAUST, Luigi moved to ITC, University of Twente, Netherlands, where he embraced an academic career by becoming an Assistant Professor of Soil Science. See his personal website here. Education and early career Luigi Lombardo received his Ph.D. from Universita’ degli Studi di Palermo, Palermo, Italy in 2015. He joined KAUST as a postdoc in April 2016, and moved

Junho Lee

Postdoctoral Research Fellow, Statistics
Short Bio about yourself Junho Lee received both undergraduate and Master degrees in Statistics from Yonsei University, Seoul, Korea. After his Master degree and before beginning his Ph.D. study, Junho Lee had worked at Korea Credit Bureau as a researcher to develop the Credit Scoring system for Bank, Credit Card and Insurance industries in Korea. Before joining KAUST in August 2017, Junho Lee received his Ph.D. in Statistics from University of Wisconsin-Madison, USA. Here, at KAUST, Junho Lee is working at Environmental Statistics Group with Prof. Ying Sun. Education and Early Career Ph.D. in

Jose Hermenegildo Ramirez Gonzalez

Postdoctoral Research Fellow, Statistics

Jordan Richards

Postdoctoral Research Fellow, Statistics
Dr. Jordan Richards was a postdoctoral fellow in the Extreme Statistics Research Group of Prof. Raphaël Huser from October 2021 to January 2024, developing machine learning approaches for spatial extremes with environmental applications. After his postdoc at KAUST, Jordan moved to the University of Edinburgh, UK, where he embraced an academic career by becoming a Lecturer (equivalent to Assistant Professor) in Statistics. See his personal website here. Education and early career Jordan Richards received his Ph.D. from Lancaster University under the supervision of Jonathan A. Tawn, Jennifer L

Huang Huang

Ph.D., Statistics
Career 2022-, Quantitative Researcher & Developer, Abu Dhabi Investment Authority (ADIA), UAE. 2019-2021, Research Scientist, King Abdullah University of Science and Technology (KAUST), Saudi Arabia. Research statistical modeling of multivariate spatio-temporal data with large size Apply machine learning models to environmental data with comparisons to spatio-temporal statistical models Lead reading groups on spatial machine learning Collaborate with computer scientists in developing high-performance packages for large geostatistical inference Mentor students on their internship on research

Ghulam A. Qadir

Ph.D. Student, Statistics
Ghulam Qadir is a 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 KAUST creates a perfect research-friendly environment where students are provided with excellent resources which allow them to turn their research creativity into valuable discoveries as well as make them ready for their future endeavors. Education and Early Career M.Sc. Statistics, Hindu College, University of Delhi, 2016 B.Sc. (Hons.) Statistics, Hindu College, University of Delhi, 2014 Research

Gaurav Agarwal

Ph.D., Statistics
Gaurav Agarwal 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. His research focused on developing statistical models for dealing with the issues concerning the environment. Bringing data to life, with statistics and programming excites him. Besides research, he likes to dance and work out. Research Interest Gaurav's research interests included Multivariate quantiles, Non-parametric statistics, Functional data analysis, and Spatial Statistics. Awards and

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

Daniela Castro-Camilo

Postdoctoral Research Fellow, Statistics
Dr. Daniela Castro-Camilo was a postdoctoral fellow in the Extreme Statistics Research Group of Prof. Raphaël Huser from November, 2015, to May, 2019, doing research on the theory and applications of multivariate and spatial extremes. After her postdoc at KAUST, Daniela moved to the University of Glasgow, United Kingdom, where she embraced an academic career by becoming a Lecturer (equivalent to Assistant Professor in the UK) of Statistics. See her personal webpage. Education and early career Daniela Castro-Camilo received her Ph.D. from Pontificia Universidad Católica de Chile in August 2015

Cristian Felipe Jiménez Varón

Ph.D. Student, Statistics
Cristian Felipe Jiménez Varón is a Ph.D. candidate in the KAUST Environmental Statistics research group under the supervision of Professor Ying Sun. Before joining KAUST, Cristian obtained a master's degree in applied mathematics from the Universidad Nacional de Colombia Sede Manizales (UNAL), Colombia. He also holds dual bachelor's degrees in industrial engineering and chemical engineering from UNAL. Research Interests Cristian's research interests include the analysis of large volumes of data, statistical modeling and environmental statistics.

Carolina D. Euan Campos

Postdoctoral Research Fellow, Statistics
Dr. Carolina Euan is a postdoctoral fellow at the Environmental Statistics Research Group at King Abdullah University of Science and Technology (KAUST). Education and Early Career Carolina Euan received her Ph.D. degree in Probability and Statistics from the Center for Research in Mathematics (CIMAT), Mexico, in 2016. She joined KAUST as a Postdoctoral Research Fellow in October 2016. Research Interest High dimensional Time Series, Spatio-Temporal Models, Non-Stationary Processes, Complex Data Visualization, Computational Statistics. Awards and Distinctions Sylvia Esterby Presentation Award

Arnab Hazra

Postdoctoral Research Fellow, Statistics
Dr. Arnab Hazra was a postdoctoral fellow in the Extreme Statistics Research Group of Prof. Raphaël Huser from February 2019 until December 2021, doing research on spatial extremes with environmental applications and bayesian statistics. After his postdoc at KAUST, Arnab moved to the Indian Institute of Technology (IIT), Kanpur, India, where he embraced an academic career by becoming an Assistant Professor of Statistics. See his personal website here. Education and early career Arnab Hazra received his Ph.D. in Statistics from North Carolina State University, United States. His Ph.D. advisors