Topics

<> statistics (33)

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".

1 min read ·
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

1 min read ·
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

1 min read ·
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

1 min read ·
2017 Workshop Modern Statistics for Complex Data Structures

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

1 min read ·
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!

1 min read ·
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)

1 min read ·
CEMSE Alumni: Mohammed Alsobay (Statistics Program)

Marc Genton received an ISI Service Award 2019

1 min read ·
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.

3 Faculty Positions in Statistics 2020 at KAUST

1 min read ·
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).

2019 Statistics and Data Science Workshop

1 min read ·
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

1 min read ·
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

1 min read ·
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

1 min read ·
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!

1 min read ·
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

1 min read ·
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

1 min read ·
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

1 min read ·
The evolving periodicity of the brightness of certain types of stars can now be described mathematically.

Mixing precision for model acceleration

1 min read ·
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

1 min read ·
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

1 min read ·
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

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.

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
<> machine learning (15)

David Gomez-Cabrero

Associate Professor, Bioscience
Professor Cabrero's research focuses on developing statistics and machine learning tools for understanding biological systems that address health-related applications, advance biomedical research and enhance our understanding of the molecular mechanisms of diseases with the potential to uncover novel therapeutic approaches.

Hayssam Dahrouj

Senior Research Scientist at KAUST (King Abdullah University of Science and Technology)

Karen Sanchez

Postdoctoral Research Fellow
Karen Sanchez is a Postdoctoral Researcher in the IVUL lab at KAUST, specializing in deep learning, machine learning, and artificial intelligence (AI) for healthcare applications. Her research focuses on video understanding, domain adaptation, generative AI, and methods for preserving patient privacy.

Hani A. Al Majed

M.S. Student
Electrical and Computer Engineering Master's student at KAUST under the supervision of Professor Bernard Ghanem, with expertise in machine learning workflow automation, physics-informed neural operators, and AI for Science, particularly in chemistry applications. Passionate about leveraging AI to solve complex scientific problems.

Ahmad Sait

M.S. Student, Computer Science
Ahmad Sait is a Master's student in the Computer Science program at KAUST. Before joining KAUST, Ahmad obtained his Bachelor's degree in Computer Science from King Abdulaziz University, Saudi Arabia. Ahmad is also a KAUST Academy graduate from the AI track, having been under the supervision of Prof. Naeemullah Khan. The Academy provided him with seven opportunities to assist in teaching students enrolled in subsequent cohorts.

Saleh AlJazzar

Research Fellow at King Abdullah University of Science and Technology (KAUST) .

Muhammad Esmat

Visiting student at, King Abdullah University of Science and Technology (KAUST).

Yasser Jaber

Visiting Student Researcher ,Machine Learning Engineer at KAUST (King Abdullah University of Science and Technology).
Syed Abidullah Fareed

Syed Abidullah Fareed

Visiting Student (former), Information Science Lab

Ahsan Mehmood

Intern (former), Information Science Lab
Visiting student at King Abdullah University of Science and Technology (KAUST).

Abdulkarem Badiea Alshaibani

Visiting Student at the Information Science Lab at the King Abdullah University of Science and Technology (KAUST).

CEMSE Big Data Open Day shows off fascinating discoveries

1 min read ·
The fifth-anniversary event marked CEMSE's biggest successes and featured 20 KAUST discoveries, with more than 1,000 visitors from the community attending the event."Big data has many applications: to understand medicine better; to manage food supplies, and to connect objects. Data is at the center of everything," said Dean Mootaz Elnozahy of the University's Computer, Electrical, Mathematical Science and Engineering (CEMSE) Division at the CEMSE Big Data Open Day held on December 4, 2016.

Deep learning in the emergency department

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

Souhaib Ben Taieb

Postdoctoral Research Fellow, Statistics
Dr. Souhaib Ben Taieb is an Assistant Professor in Computer Science at University of Mons (UMONS) in Belgium. He is also an Adjunct Senior Lecturer in the Department of Econometrics and Business Statistics at Monash University in Melbourne, Australia. From 2016 to 2018, Souhaib was a Lecturer in the Department of Econometrics and Business Statistics at Monash University in Melbourne, Australia. In 2015, he was a postdoctoral research fellow in the Spatio-Temporal and Data Science Group at KAUST. He was a visiting scholar at several international institutions, including the School of Earth
Sameh Abdulah

Sameh Abdulah

Sameh Abdulah > Appointments, Roles and Affiliations, Hierarchical Computations on Manycore Architectures
Dr. Sameh Abdulah specializes in HPC, scalable algorithms, and climate modeling, with innovations receiving the ACM Gordon Bell Prize for Climate Modelling.
<> spatio-temporal statistics (11)

Functional data depth and its application in the visualization of spatio-temporal covariance structures

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KAUST

Functional data analysis is a very active research area due to the overwhelming existence of functional data. In the first part of this talk, I will introduce how functional data depth is used to carry out exploratory data analysis and explain recently-developed depth techniques. In the second part, I will discuss spatio-temporal statistical modeling. It is challenging to build realistic space-time models and assess the validity of the model, especially when datasets are large. I will present a set of visualization tools we developed using functional data analysis techniques for visualizing covariance structures of univariate and multivariate spatio-temporal processes. I will illustrate the performance of the proposed methods in the exploratory data analysis of spatio-temporal data.

2015 Workshop on Computational Space-Time Statistics

1 min read ·
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

Zipei Geng

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

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

Soumya Das

Ph.D. Student, Statistics
Soumya Das is a Ph.D. candidate in Statistics at King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Marc Genton. Education and Early Career M.sc. (2015-17) from Indian Institute of Technology Kanpur, Uttar Pradesh, India B.Sc. (2012-15) from Ramakrishna Mission Residential College (Autonomous), Narendrapur, Kolkata, West Bengal, India Research Interests Cyclostationary processes, Non-parametric analysis, Non-stationary models, Skewed distributions, Spatio-temporal statistics, Threshold models. Awards and Distinctions The General Proficiency Medal for

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.

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

Amanda Lenzi

Postdoctoral Research Fellow, Statistics
Amanda Lenzi is currently working as a Postdoctoral fellow under Mihai Anitescu, in Argonne National Laboratory, Chicago USA Amanda Lenzi was a Post-Doctoral fellow in Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Marc G. Genton in his research group. Education and Early Carreer Amanda Lenzi completed her Ph.D. at the Technical University of Denmark (DTU) in 2017. She also holds a B.S. and an M.S. degree in Statistics from the University of Campinas (UNICAMP), Brazil. She joined KAUST in September 2017. Research Interest Her

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
<> big data (7)

On The Coupling between HPC and Statistics: Challenges, Opportunities, and Future Trends of Emerging Techniques -- (Virtual SIAM CSE21 Minisymposium)

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KAUST

Abstract Big data modeling/inference and large-scale simulations have followed largely independent paths to the high-performance computing (HPC) frontier, but important opportunities now arise that can be addressed by combining the strengths of each. HPC is becoming increasingly significant in scaling existing statistical methods to larger and more complex applications and developing novel methods that are amenable to scaling within the constraints that exist in modern HPC architectures. The purpose of this minisymposium is to bring together researchers in the area of statistics and HPC to

High Performance Statistical Computing (HPSC): Challenges and Best Practices

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KAUST

************************************************** Organizers: Sameh Abdulah and Marc Genton ************************************************** Abstract Big data in terms of volume, intensity, and complexity is one of the main challenges to statisticians. The availability of different data sources has essential implications in collecting colossal data volumes that require other ways to be managed and analyzed. Existing analytical tools cannot be applied easily to big data volumes due to memory and computation constraints. Previously, statistical applications and traditional high performance

CEMSE Big Data Open Day shows off fascinating discoveries

1 min read ·
The fifth-anniversary event marked CEMSE's biggest successes and featured 20 KAUST discoveries, with more than 1,000 visitors from the community attending the event."Big data has many applications: to understand medicine better; to manage food supplies, and to connect objects. Data is at the center of everything," said Dean Mootaz Elnozahy of the University's Computer, Electrical, Mathematical Science and Engineering (CEMSE) Division at the CEMSE Big Data Open Day held on December 4, 2016.

Star light, star bright…as explained by math

1 min read ·
The evolving periodicity of the brightness of certain types of stars can now be described mathematically.

Mixing precision for model acceleration

1 min read ·
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

1 min read ·
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.

Souhaib Ben Taieb

Postdoctoral Research Fellow, Statistics
Dr. Souhaib Ben Taieb is an Assistant Professor in Computer Science at University of Mons (UMONS) in Belgium. He is also an Adjunct Senior Lecturer in the Department of Econometrics and Business Statistics at Monash University in Melbourne, Australia. From 2016 to 2018, Souhaib was a Lecturer in the Department of Econometrics and Business Statistics at Monash University in Melbourne, Australia. In 2015, he was a postdoctoral research fellow in the Spatio-Temporal and Data Science Group at KAUST. He was a visiting scholar at several international institutions, including the School of Earth
<> Environmental Statistics (7)

Workshop on Computational Space-Time Statistics

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B1 L4 R4102

Workshop on Computational Space-Time Statistics

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

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.

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

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
<> artificial intelligence (6)
Najmeddine Dhieb

Najmeddine Dhieb

Yasmeen Alsaedy

M.S. Student, Computer Science
She is an AI researcher specializing in generative AI and deep learning. With a strong academic foundation and hands-on experience in machine learning, data science and artificial intelligence, she is passionate about advancing AI applications in real-world scenarios. She has contributed to cutting-edge projects, published research, and collaborated on innovative solutions that bridge the gap between AI theory and practice. Her expertise includes designing and implementing advanced data models, AI-driven visual/text computing, and deep learning architectures.

Yasser Jaber

Visiting Student Researcher ,Machine Learning Engineer at KAUST (King Abdullah University of Science and Technology).
Syed Abidullah Fareed

Syed Abidullah Fareed

Visiting Student (former), Information Science Lab

Ahmed Youssef Ragab Radwan

Visiting Student (former), Information Science Lab
Visiting student at the Information Science Lab, in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST)

Deep learning in the emergency department

1 min read ·
Harnessing the power of deep learning leads to better predictions of patient admissions and flow in emergency departments.
<> Computer Vision (6)

Abdelrahman Eldesokey

Postdoctoral Research Fellow
I am a Postdoctoral Fellow at KAUST, Saudi Arabia, specializing in Generative AI and conducting research on advanced topics in Generative and Large Language Models (LLMs). I bring over 11 years of combined academic and industrial experience in Computer Vision and Deep Learning across Sweden, Egypt, and Saudi Arabia.
Najmeddine Dhieb

Najmeddine Dhieb

Karen Sanchez

Postdoctoral Research Fellow
Karen Sanchez is a Postdoctoral Researcher in the IVUL lab at KAUST, specializing in deep learning, machine learning, and artificial intelligence (AI) for healthcare applications. Her research focuses on video understanding, domain adaptation, generative AI, and methods for preserving patient privacy.

Aznaur Aliev

M.S. Student

Hani A. Al Majed

M.S. Student
Electrical and Computer Engineering Master's student at KAUST under the supervision of Professor Bernard Ghanem, with expertise in machine learning workflow automation, physics-informed neural operators, and AI for Science, particularly in chemistry applications. Passionate about leveraging AI to solve complex scientific problems.

Ahmad Sait

M.S. Student, Computer Science
Ahmad Sait is a Master's student in the Computer Science program at KAUST. Before joining KAUST, Ahmad obtained his Bachelor's degree in Computer Science from King Abdulaziz University, Saudi Arabia. Ahmad is also a KAUST Academy graduate from the AI track, having been under the supervision of Prof. Naeemullah Khan. The Academy provided him with seven opportunities to assist in teaching students enrolled in subsequent cohorts.
<> deep learning (6) <> modeling (5) <> Signal processing (4) <> AI (3) <> applied statistics (3)

On The Coupling between HPC and Statistics: Challenges, Opportunities, and Future Trends of Emerging Techniques -- (Virtual SIAM CSE21 Minisymposium)

-

KAUST

Abstract Big data modeling/inference and large-scale simulations have followed largely independent paths to the high-performance computing (HPC) frontier, but important opportunities now arise that can be addressed by combining the strengths of each. HPC is becoming increasingly significant in scaling existing statistical methods to larger and more complex applications and developing novel methods that are amenable to scaling within the constraints that exist in modern HPC architectures. The purpose of this minisymposium is to bring together researchers in the area of statistics and HPC to

High Performance Statistical Computing (HPSC): Challenges and Best Practices

-

KAUST

************************************************** Organizers: Sameh Abdulah and Marc Genton ************************************************** Abstract Big data in terms of volume, intensity, and complexity is one of the main challenges to statisticians. The availability of different data sources has essential implications in collecting colossal data volumes that require other ways to be managed and analyzed. Existing analytical tools cannot be applied easily to big data volumes due to memory and computation constraints. Previously, statistical applications and traditional high performance

Marc G. Genton

Al-Khawarzmi Distinguished Professor, Statistics
Professor Genton’s pioneering research on large-scale spatial and temporal data has profoundly affected the field of environmental statistics. He is an expert in spatial and spatio-temporal statistics with applications to environmental problems.
<> climate science (3) <> computational predictions (3) <> data analysis (3) <> data science (3)

2019 Statistics and Data Science Workshop

1 min read ·
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.

KAUST STAT students come together for second ASA chapter meeting

1 min read ·
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.

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
<> extreme computing (3) <> functional data analysis (3)

Functional data depth and its application in the visualization of spatio-temporal covariance structures

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KAUST

Functional data analysis is a very active research area due to the overwhelming existence of functional data. In the first part of this talk, I will introduce how functional data depth is used to carry out exploratory data analysis and explain recently-developed depth techniques. In the second part, I will discuss spatio-temporal statistical modeling. It is challenging to build realistic space-time models and assess the validity of the model, especially when datasets are large. I will present a set of visualization tools we developed using functional data analysis techniques for visualizing covariance structures of univariate and multivariate spatio-temporal processes. I will illustrate the performance of the proposed methods in the exploratory data analysis of spatio-temporal data.

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.

Joydeep Chowdhury

Postdoctoral Research Fellow, Statistics
Joydeep Chowdhury is a postdoctoral fellow in the Spatio-Temporal Statistics & Data Science research group of Professor Marc G. Genton. His current research activities focus on problems in multivariate statistics, spatial statistics and functional data analysis.
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Bang Huang

Postdoctoral Research Fellow, Electrical and Computer Engineering
Bang Huang works as a postdoctoral fellow at the Computer Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Mohamed-Slim Alouini in the Communication Theory Lab (CTL). Education and Early Career Bang Huang received the M.S. degree (with honors) in electrical engineering and Ph.D degree in information and communication engineering from the School of Information and Communication Engineering (SICE), University of Electronic Science and Technology of China (UESTC), Chengdu, China, in

Xiaowei Pang

Postdoctoral Research Fellow, Electrical and Computer Engineering
Xiaowei Pang is currently a Postdoctoral Research Fellow at the Computer Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), working with Professor Mohamed-Slim Alouini in the Communication Theory Lab (CTL). Education and Early Career Xiaowei Pang received her B.S. degree in Telecommunication Engineering from Northwestern Polytechnical University, Xi'an, China, in 2018, and her Ph.D. degree in Information and Communication Engineering from Dalian University of Technology, Dalian, China, in 2024. From 2021 to 2022
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Hussein Al-Shatri

I chose KAUST because of its reputation for excellence in research and its recent emphasis on applied research. With my extensive background in both academic research and industrial development, I am excited to contribute my experience and ambition. My goal is to foster new ideas and develop innovative prototypes in the field of wireless communications, which will undoubtedly enhance people's lives.
<> healthcare (3) <> KAUST Competition on Spatial Statistics (3)

2021 KAUST Competition on Spatial Statistics for Large Datasets

Introduction With the development of observing techniques and computing devices, it has become easier and more common to obtain large datasets. Statistical inference in spatial statistics becomes computationally challenging. For decades, various approximation methods have been proposed to model and analyze large-scale spatial data when the exact computation is infeasible. However, in the literature, the performance of the statistical inference using those proposed approximation methods was usually assessed with small and medium datasets only, for which the exact solution can be obtained. Then

2023 KAUST Competition on Spatial Statistics for Large Datasets

Introduction The rapid increase in the volume of geospatial data over recent years has added more challenges to processing these data using traditional methods. Thus, geospatial applications have brought High-Performance Computing (HPC) into the mainstream and further increased its use in the spatial statistics field. ExaGeoStat is one example of an HPC software that enables large-scale parallel generation, modeling, and prediction of large geospatial data via covariance matrices. Unlike other existing tools, which typically rely on approximations to deal with the vast data volume on day-use

2022 KAUST Competition on Spatial Statistics for Large Datasets

Introduction Spatial statistics is an example of a scientific field that requires novel methods to model and analysis large-scale spatial data. In the literature, research studies have proposed different approximation methods to handle large data sizes on traditional hardware. However, with the availability of modern High-Performance Computing (HPC) systems, large-scale exact computation becomes possible and allows processing larger data sizes more easily than before. For decades, the lack of large-scale exact computation has led to an inefficient assessment of spatial modeling approximation
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