statistics

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.

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".

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

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

2017 Workshop Modern Statistics for Complex Data Structures

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2017 Workshop Modern Statistics for Complex Data Structures

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

CEMSE Alumni: Mohammed Alsobay (Statistics Program)

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CEMSE Alumni: Mohammed Alsobay (Statistics Program)

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.

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.

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.

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!

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.

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.

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.

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.

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.

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

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

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.

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.

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

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.

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