data analysis

Nizar Ajeeb

Full Time Researcher Full at King Abdullah University of Science and Technology.

Computational Methods for ChIP-seq Data Analysis and Applications

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B3 L5 5209

Abstract The development of Chromatin immunoprecipitation followed by sequencing (ChIP-seq) technology has enabled the construction of genome-wide maps of protein-DNA interaction. Such maps provide information about transcriptional regulation at the epigenetic level (histone modifications and histone variants) and at the level of transcription factor (TF) activity. This dissertation presents novel computational methods for ChIP-seq data analysis and applications. The work of this dissertation addresses four main challenges. First, I address the problem of detecting histone modifications from

Ontology Design Patterns for Combining Pathology and Anatomy: Application to Study Ageing and Longevity in Inbred Mouse Strains

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B9 R3120

Abstract In biomedical research, ontologies are widely used to represent knowledge as well as annotate datasets. Many of the existing ontologies cover a single type of phenomena, such as a process, cell type, gene, pathological entity or anatomical structure. Consequently, it is required to use multiple ontologies to fully characterize the observations in the datasets. Although this allows precise annotation of different aspects of a given dataset, it limits our ability to use the ontologies in data analysis, as the ontologies are usually disconnected and their combination cannot be exploited

AI4GH Seminar Series - Vector Representation of Biological Entities Based on Ontologies and Their Annotations

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B2 R5220

Biological knowledge is widely represented in the form of ontology-based annotations: ontologies describe the phenomena assumed to exist within a domain, and the annotations associate a biological entity with a set of phenomena within the domain.

High-dimensional Q-learning for Dynamic Treatment Regimes

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B3 L5 R5220

Dynamic treatment regimes are a set of decision rules and each treatment decision is tailored over time according to patients’ responses to previous treatments as well as covariate history. There is a growing interest in development of correct statistical inference for optimal dynamic treatment regimes to handle the challenges of nonregularity problems in the presence of nonrespondents who have zero-treatment effects, especially when the dimension of the tailoring variables is high.

Neuro-symbolic systems in the life sciences

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B9 H1 R2322

Abstract The life sciences have invested significant resources in the development and application of semantic technologies to make research data accessible and interlinked, and to enable the integration and analysis of data. Utilizing the semantics associated with research data in data analysis approaches is often challenging. Now, novel methods are becoming available that combine symbolic methods and statistical methods in Artificial Intelligence. In my talk, I will describe how to generate knowledge graph embeddings for analysis of biological and biomedical data. Brief Biography Robert

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".

Student life: studying and working at the Computational Bioscience Research Center (KAUST)

1 min read ·
For Ph.D. students Arturo Magana Mora, Haitham Ashoor and Olaa Motwalli, April 2017 was a particularly nerve-wracking month. After years of hard work and sleepless nights, they finally defended their thesis, bringing them one step closer to graduation.

Superior seismic imaging for better drilling

1 min read ·
Software could transform underground imaging of fossil fuel reserves by providing unprecedented detail in record time.

2018 Workshop on Statistics and Data Science

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The Statistics Program at KAUST is proud to host the 2018 Workshop on Statistics and Data Science. This workshop gathers the leading experts on statistical data science to discuss the current needs, challenges and opportunities of modeling massive and high dimensional data, predicting complex biological and physical processes. The workshop will run from November 12-14. Talks and posters presentations will take place in Auditorium 0215 (between Buildings 4 & 5). Details on the workshop (speakers, abstracts and schedule) can be found here.

SRSI student success story at Ibdaa 2022

1 min read ·
Faisal Fadi Hasan Almulla, who worked last year under the mentorship of Dr. Fouzi Harrou and Professor Ying Sun as part of the annual KAUST Saudi Research Science Institute (SRSI) summer program, won two awards at the National Olympiad for Scientific Creativity — Ibdaa 2022.

Zhedong Liu

Ph.D. Student, Statistics
Ph.D. degree in Statistics at the King Abdullah University of Science and Technology (KAUST).

Yang Liu

Ph.D. Student, Computer Science
Yang Liu is an M.S./Ph.D. candidate in the KAUST Bio-Ontology research group under the supervision of Professor Robert Hoehndorf. Before joining KAUST. Research Interests Yang's interests include bioinformatics, machine learning and human genomics. She is interested in building models for human disease diagnosis and prognosis, especially for cancer, and applying statistical methods to analyze human omics data. Education Profile B.Sc, Bioengineering, Harbin Institute of Technology, Harbin, China, 2016-2020. M.Sc, Bioengineering, King Abdullah University of Science and Technology, Thuwal, Saudi

Vasiliki Kordopati

Vasiliki Kordopati obtained Master degree in Computer Science under the co-supervision of Professor Boon Ooi at Photonics Laboratory and Prof. Xin Gao at Structural and Functional Bioinformatics Group (SFB) from King Abdullah University of Science and Technology (KAUST). Vasiliki is a co-founder of Oæsis, a King Abdullah University of Science and Technology (KAUST) startup spinout, where she is responsible for product development. A computer engineer by training, she received a bachelor's degree in Computer Engineering from the University of Patras, Greece. Research Interest Vasiliki's

Ronell Sicat

Visualization Scientist, Core labs and Research Infrastructure
Ronell Sicat is a scientist with more than 10 years of experience in scientific research and software development. His main interests are scalable visualization and analysis of large-scale data (e.g., images, volumes, meshes), and immersive analytics that leverage augmented/virtual reality. He is passionate about helping domain scientists solve their problems using my wide-ranging experience in programming, image processing, segmentation, quantitative analysis, and computer graphics.

Olga Chechekina

Visiting Student, Bio-Ontology Research Group
Olga Chechekina is a visiting student in the Bio-Ontology Research Group under the supervision of Prof. Robert Hoehndorf. Prior to visiting KAUST, she graduated from "Biology and Biotechnology" department at HSE University in 2023, and completed an internship at Genome Institute of Singapore in 2022. Here research interests are in metagenomics, molecular biology and data analysis.

Martins Bruvelis

Senior Information Technology Specialist, Computer, Electrical and Mathematical Sciences and Engineering
Martins is a Tier 3 technical support specialist. He specializes in resolving complex IT infrastructure challenges, provides advanced support, and develops, maintains, and enhances critical software applications and tools. His quantum physics and data science background has refined an investigative, analytical, and data-driven perspective, enabling insightful decision-making and innovative interdisciplinary solutions when addressing complex issues across a broad spectrum of expertise.

Haitham M. Ashoor

Research Interests Haitham Ashoor is a Ph.D. candidate in the Computer Science program at King Abdullah University of Science and Technology (KAUST). He is working under the supervision of Prof. Vladimir Bajic. He has obtained his BSc degree in Computer Engineering from the University of Jordan, Amman Jordan in 2008. He joined KAUST as an MSc student in 2009. In 2011, He finished his master studies under the supervision of Prof. Vladimir Bajic. His main research interests are developing computational methods for next-generation sequencing data analysis and applications of machine learning in

Francesco Napolitano

​Francesco is a Computer Scientist with a background in Mathematical Models and Machine Learning. He focused his M.Sc. (University of Salerno, SA, Italy) and Ph.D. (University of Salerno and the University of California, Irvine, CA, USA) on supervised and unsupervised data analysis, particularly for clustering of complex, high-dimensional data. He developed and applied data analysis techniques to problems from different fields, including fault detection in avionics and computational pharmacology, before focusing on bioinformatics and systems biology. Research Interests Francesco's main

Anass B. El-Yaagoubi

Ph.D. (former), Statistics
Ph.D. Degree at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Hernando Ombao in the Biostatistics group.

Abdulmajeed Aljaloud

Visiting Student, Computer Science
Visiting Student Research Program ,Research Intern at King Abdullah University of Science and Technology (KAUST)

Abdullah Alharthi

Postdoctoral Research Fellow, Electrical and Computer Engineering
Dr. Abdullah Alharthi's research centers around pattern recognition as a means to tackle a diverse array of new challenges, including how to learn intelligent behavior in complex, dynamic environments. His areas of expertise include cognition, perception, theory of mind, gait, and human-robot interaction. His work delves into both human kinetics and the comprehension and resolution of how humans and robots interact with one another in a dynamic setting. Dr. Alharthi employs Deep Learning techniques to tackle problems related to image, object, and signal recognition and classification