big data

Bio2Vec Kickoff Meeting

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Paris France

​​Research Scientist Senay Kafkas will be attending the Bio2Vec kickoff meeting in Paris, France.

Big Data Analyses in Evolutionary Biology

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B9 H2

This event is organized by CBRC with financial support from the KAUST Office of Sponsored Research

Paving the Way for Next Generation Wireless Data Center Networks

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B1 L4 R4214

As big data, articial intelligence, cloud services, cellular infrastructure, content delivery; all of which entail interconnected and sophisticated computing and storage resources. Recent studies on traditional data center networks (DCNs) revealed two key challenges: a biased distribution of inter-rack trac, and unidentied ow multi-classes best known as delay sensitive mice ow (MF) and throughput-hungry elephant ow (EF).

Forecasting Multiple Time Series with One-Sided Dynamic Principal Components

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B1 L4 R4102

Generalized Dynamic principal components are presented and it is shown how to define one side inear combinations of the present and past values of the series that minimize the reconstruction mean squared error (ODPC). It is shown that the ODPC introduced in this paper can be successfully used for forecasting high-dimensional multiple time series.

Exploring FPGAs for Virtualized In-Network Acceleration

Dr. Suhaib Fahmy, Associate Professor, Computer Engineering, University of Warwick, UK

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B2 B3 A0215

Abstract With increasing connectivity and reliance on machine intelligence to process ever-growing amounts of data, the question of how to arrange the required connectivity and computation arises. Traditional cloud computing approaches that centralize compute capability in a data center do not scale well to large scale distributed data sources that must then transmit data over constrained networks. Similarly, computing at the very edge of the network is often constrained by limited computational capacity and a lack of access to shared data. In-network computing has been proposed as a way of

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

EF-BV: distributed optimization with compressed communication

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B9 L2 R2322 H1

In the big data era, it is necessary to rely on distributed computing. For distributed optimization and learning tasks, in particular in the modern paradigm of federated learning, specific challenges arise, such as decentralized data storage. Communication between the parallel machines and the orchestrating distant server is necessary but slow. To address this main bottleneck, a natural strategy is to compress the communicated vectors. I will present EF-BV, a new algorithm which converges linearly to an exact solution, with a large class of deterministic or random, biased or unbiased compressors.

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.

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.

Professors discuss big data and human health in latest Sci-Café

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​Professors Takashi Gojobori, Xin Gao and Robert Hoehndorf spoke about big data and human health at the Sci-Café event held on February 27. Their talk, which was moderated by James Calvin, Vice President for Academic Affairs, aimed to answer the question, "Can Big Data Solve My Health Problems?" This event was open to all members of the KAUST community.

The awesome scope of big data

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Sifting through huge amounts of data may bring a better understanding of whale shark social structures, protein targets for drug therapies and disease-causing genes.

Facebook AI new language explained by KAUST Professor Xiangliang Zhang

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That is not a Messenger troll, but the actual conversation between two chatbots by the Facebook AI Research (FAIR). The two robots, Bob and Alice, were thought the art of negotiation apples and books. They had been instructed to work out how to negotiate between themselves and improve their bartering as they went along. But, after leaving the pair alone, they start talking in this uncompressible but yet effective vocabulary.

Exploring science's fourth paradigm

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The recent KAUST Research Conference: Computational and Statistical Interface to Big Data brought together leading computer science researchers and statistical experts to discuss the current state and future of data science. Held on campus from March 19 to 21, the conference covered such data science topics as succinct data representation and storage; big data visualization; parallel and distributed algorithms for inference and optimization; and analysis of large graphs and networks.

Alumni Focus: Ahmed Abdulmajeed Alabdulkarim, M.S. '11

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Ahmed Abdulmajeed Alabdulkarim earned a master's degree from KAUST in 2011. His research interests are in the field of big data, artificial intelligence and machine learning, and he is now pursuing his Ph.D. at the Massachusetts Institute of Technology (MIT) in the U.S. He received his bachelor's degree with honors in computer science from King Saud University, where he graduated at the top of his class. From a young age, Alabdulkarim was passionate about computers. Studying computer science was his drive and motivation to continue his university studies. He explained that he chose KAUST for

Querying big data just got universal

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A universal query engine for big data that works across computing platforms could accelerate analytics research.

Accelerating the grapevine effect

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By looking at classical gossip algorithms from a novel perspective, KAUST Professor Peter Richtarik has found a way to significantly speed up gossip-based information sharing, and in the process, he discovered new applications for this efficient mathematical approach. Gossip involves the sharing of information between individuals in a network and can be applied mathematically in both human social networks and data networks, such as distributed sensors. “A network is a collection of nodes, each connected to other nodes via links,” explains Richtarik. “In social networks, for instance

AI learns complex gene-disease patterns

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A deep learning model improves the ability to identify genes potentially involved in disease.

KAUST Professor Peter Richtárik wins Distinguished Speaker Award

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Peter Richtárik, KAUST professor of computer science, recently received a Distinguished Speaker Award at the Sixth International Conference on Continuous Optimization (ICCOPT 2019) held in Berlin from August 3 to 8. ICCOPT 2019 was organized by the Mathematical Optimization Society and was hosted this year by the Weierstrass Institute for Applied Analysis and Stochastics.

CEMSE Division startups in focus: Peregrine Genomics

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The future has already arrived when it comes to the most exciting and promising field of modern medicine—precision medicine.

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.

Keep the data coming

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A continuous data supply ensures data-intensive simulations can run at maximum speed.

Professor Ghanem wins 2020 Abdul Hameed Shoman Award for Arab Researchers

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Bernard Ghanem, KAUST associate professor of electrical and computer engineering and computer science, recently received the 2020 Abdul Hameed Shoman Award for Arab Researchers for ''Machine Learning and Big Data.''

Cloud simulations get a dose of realism

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A focus on the fundamental physics of cloud formation leads to highly realistic simulations of different types of clouds.

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.

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.

Machine learning at speed

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Optimizing network communication accelerates training in large-scale machine-learning models.

The IEEE CS Global Student Challenge seeks top KAUST CS talent

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The annual IEEE Computer Society Global Student Challenge provides an avenue for students worldwide to create innovative solutions to big data problems. This year’s competition, IEEE CS GSC 2021, encourages each individual or team (max three students) to present solutions to pre-determined problem/issue statement of relevance in CS today.

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.

Drop the zeros for a performance boost

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An eight-fold speed up of deep machine learning can be achieved by skipping the transmission of zero values.

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.

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.

Code of collaboration

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David Keyes's and Matteo Ravasi's cross-disciplinary project results in improved efficiency for seismic processing, with promising applications for the energy industry.

Zuhair Yarub Khayyat

Zuhair Yarub Khayyat obtained his Ph.D. degree in Computer Science under the supervision of Professor Panagiotis Kalnis at InfoCloud Research Group (CLOUD) in King Abdullah University of Science and Technology (KAUST) in 2016. Research Interest Zuhair's research interests included Big Data Management, Parallel and distributed data processing, Cloud Computing and Graph Analytics. His main experience was focused on problem-solving and algorithm development for processing large data in distributed environments. He is a reliable team player with a methodological mindset with the ability to utilize

Xin Gao

Program Chair, Computer Science
Professor Gao is a world-leading expert in developing novel AI solutions for challenges in biology, biomedicine, and health and wellness, with a focus on AI-based drug development, large language models in biomedicine, biomedical imaging analysis, and omics-based disease detection and diagnostics.

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

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

Ruogu Ding

Ruogu Ding obtained his Master's Degree in Computer Science under the supervision of Professor Panagiotis Kalnis at InfoCloud Research Group (CLOUD) in King Abdullah University of Science and Technology (KAUST). Prior to that, Ruogu received his Bachelor's degree in Computer Science from Peking University in July 2009. He worked as a research assistant on large-scale sequence assembly algorithms 2009-2010 at Peking University. Ruogu is currently serving as a Technology Partner at MiningLamp, a fast-growing startup aiming to provide enterprises with big data solutions. He worked on Algorithm

Peter Richtarik

Professor, Computer Science
Professor Richtárik specializes in big data optimization and machine learning. He is known for his work on randomized coordinate descent algorithms, stochastic gradient descent and federated learning.

Kadir Akbudak

Dr. Kadir Akbudak is a Consultant at Extreme Computing Research Center at KAUST, working with Professor David E. Keyes and Dr. Hatem Ltaief. Research Interests Kadir Akbudak's research interests include Combinatorial scientific computing, Graph and hypergraph partitioning, Locality-aware partitioning and scheduling, High performance kernels for sparse computations, Vectorization for irregular applications, Iterative numerical methods and Parallel numerical algorithms. Education Profile Ph.D., Computer Engineering, Bilkent University, 2015. M.S., Computer Engineering, Bilkent University, 2009

Ashraf M. Kibriya

Research Interests ​My research interests include the application of Big Data and Machine Learning techniques to real-world problems in various domains, including biology and biomedicine. In particular, I'm interested in Deep Learning, Text Mining, Network Analysis and Graph Mining. Professional Profile ​2018-Present: Postdoctoral Fellow, KAUST, Thuwal, Saudi Arabia 2011-2018: Doctoral Fellow, KU Leuven, Belgium 2010-2011: Pre-doctoral fellow, KU Leuven, Belgium 2007-2009: Database developer / Datamining specialist, Datamine Ltd., New Zealand 2002-2005: Research Programmer, Machine Learning

Amer AlGhadhban

Amer AlGhadhban is a Ph.D. student under the supervision of Professor Basem Shihada at Networking Research Lab in King Abdullah University of Science and Technology (KAUST). Amer obtained both of his degrees B.S. and M.Sc. in Computer Engineering from King Fahd University of Petroleum and Minerals (KFUPM). He was a Cisco Academy instructor and during that he earned multiple professional certificates: Cisco Certified Network Associate (CCNA), Cisco wireless, SANS-GIAC Certified Firewall Analyst (GCFW-Gold), Certified Ethical Hacker (CEH), Security Certified Network Engineer (SCNP), and SANS

Ahmed Abdulmajeed Alabdulkarim

Ahmed Abdulmajeed Alabdulkarim earned a master's degree from KAUST in 2011. His research interests are in the field of big data, artificial intelligence and machine learning, and he is now pursuing his Ph.D. at the Massachusetts Institute of Technology (MIT) in the U.S. He received his bachelor's degree with honors in computer science from King Saud University, where he graduated at the top of his class. From a young age, Alabdulkarim was passionate about computers. Studying computer science was his drive and motivation to continue his university studies. He explained that he chose KAUST for