big data

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

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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

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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.

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