Events

Jan 20 - Jan 27, 2019

Biomedical Signal Processing using the Squared Eigenfunctions of the Schrödinger Operator and Machine Learning

Abderrazak Chahid, Ph.D. Student, Electrical and Computer Engineering

Jan 24, 13:00 - 15:00

B1 B4 B4214

Signal processing machine learning image processing biomedicine

Abstract The health of a human body is monitored through several physiological measurements such as the heart rate, the blood pressure, the oxygen saturation levels, brain activity, etc. These measurements are taken at predefined points in the body and recorded as temporal signals or colorful images. During the diagnosis phase, physicians analyze these records visually (sometimes it is not visual with the progress in medicine, better to say: sometimes visually) to take treatment decisions. These records are usually contaminated with noise. The origin of this noise may be diverse. For instance

Dec 16 - Dec 23, 2018

Network Engineering as Distinct Academic Discipline Beyond the TEE Movement

Professor Tarek Al-bawab, Jackson State University

Dec 19, 14:00 - 16:00

B1 B3 R3119

Network Engineering communications

Abstract: Network Engineering as Distinct Academic Discipline Beyond the TEE Movement Network Engineering is now recognized as distinct education discipline in the USA and in many other countries. This development came about with ABET’s1 2015 (current) Criteria for Electrical, Computer, Communications, Telecommunication(s) and Similarly Named Engineering Programs. Few academic programs focused on this area of study in the past. More programs are expected to emerge now with progress in this field and with ABET recognition of it. Such programs have a great potential to prepare a new generation

Dec 2 - Dec 9, 2018

Molecular Basis for p85 Dimerization and Allosteric Ligand Recognition

Safia Aljedani, Ph.D., Bioscience

Dec 6, 13:00 - 15:00

B3 L5 R5220

Abstract The phosphatidylinositol-3-kinase α (PI3Kα) is a heterodimeric enzyme that is composed of a p85α regulatory subunit and a p110α catalytic subunit. PI3Kα plays a critical role in cell survival, growth and differentiation, and is the most frequently mutated pathway in human cancers. The PI3Kα pathway is also targeted by many viruses, such as the human immunodeficiency virus (HIV-1), the herpes simplex virus 1 (HSV-1) or the influenza A virus, to create favorable conditions for viral replication. The regulatory p85α stabilizes the catalytic p110α but keeps it in an inhibited state

Nov 25 - Dec 2, 2018

Exploring Protein-Protein Interactions with MEGADOCK: Parallelization, Application, and Open Database

Masahito Ohue Department of Computer Science, School of Computing, Tokyo Institute of Technology, Japan

Nov 27, 10:00 - 11:00

B3 L5 R5220

Abstract Protein-protein interaction (PPI) plays a core role in cellular functions. In recent years, PPI prediction methods based on protein docking have been developed and have been applied for large-scale PPI network prediction based on tertiary structures. We have developed an ultra-high-throughput PPI prediction system based on rigid-body protein-protein docking and supercomputing, named MEGADOCK[1, 2]. MEGADOCK can perform faster docking based on its improved scoring function and implementation for heterogeneous supercomputers[2, 4-6]. MEGADOCK achieved excellent scalability on

Nov 18 - Dec 2, 2018

Nov 11 - Nov 25, 2018

Nov 11 - Nov 18, 2018

2018 Workshop on Statistics and Data Science

Nov 12, 08:30 - Nov 15, 17:00

Auditorium 0215 between Buildings 4 & 5

data science high dimensional statistics modeling biological processes physical processes

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). View Workshop's Agenda.

Nov 4 - Nov 18, 2018

AI4GH Seminar Series - Towards Rational Design of Biosynthesis Pathways

Meshari Alazmi, Ph.D., Computer Science

Nov 11, 12:00 - 13:00

B2 R5220

machine learning bioinformatics structural biology systems biology

Recent advances in genome editing and metabolic engineering enabled a precise construction of de novo biosynthesis pathways for high-value natural products. One important design decision to make for the engineering of heterologous biosynthesis systems is concerned with which foreign metabolic genes to introduce into a given host organism.

Nov 4 - Nov 11, 2018

Oct 28 - Nov 11, 2018

Electrocatalytic and Photoelectrochemical Conversion of Energy and Fuels and Storing Solar Energy in Solar Flow Batteries

Professor Song Jin, University of Wisconsin-Madison

Nov 4, 14:00 - 15:00

B2 L5 R5220

solar energy photoelectrochemical electrocatalytic

Due to the intermittent nature of most renewable energy sources (such as solar and wind), practical large scale renewable energy deployment and utilization demand both efficient energy conversion and large scale energy storage or alternative usage. Earth-abundant but highly active electrocatalysts need to be developed to enable efficient and sustainable production of energy using electrocatalytic and photoelectrochemical (PEC) water splitting. We report nanostructures of earth-abundant electrocatalysts, such as exfoliated nanosheets of MoS2, WS2, MoSe2, for the hydrogen evolution reaction (HER

Oct 28 - Nov 4, 2018

Oct 21 - Oct 28, 2018

Oct 14 - Oct 21, 2018

Oct 7 - Oct 14, 2018

Sep 30 - Oct 7, 2018

AI4GH Seminar Series| Artificial Intelligence for Genomics and Health: Introduction and Overview

Robert Hoehndorf, Associate Professor, Computer Science

Oct 3, 12:00 - 13:00

B2 R5220

biology biomedicine bioinformatics Ontologies

Abstract Genomics and health are prime areas for the development and application of novel intelligent algorithms due to a large amount of available research data, the high complexity of the problem, and the significant social and economic benefits of improving health outcomes in humans. In the past, genomics and health have been one of the key drivers in the development of Artificial Intelligence methods, including machine learning, expert systems, or graph-based algorithms. I will introduce the new seminar series on Artificial Intelligence for Genomics and Health (AI4GH), and then discuss our

Sep 16 - Sep 23, 2018

CS Graduate Seminar by visitor Professor Vadim Lozin

Vadim Lozin, Professor, University of Warwick, UK

Sep 17, 14:00 - 15:00

KAUST

Abstract In this talk, we introduce a new graph parameter under the name functionality. We show that functionality generalizes simultaneously several other graph parameters, such as degeneracy or clique-width, by proving that bounded degeneracy or bounded clique-width imply bounded functionality. Moreover, we show that this generalization is proper by revealing classes of graphs of unbounded degeneracy and clique-width, where functionality is bounded by a constant. This includes permutation graphs and unit interval graphs. We also observe that bounded functionality implies bounded VC-dimension

Jul 29 - Aug 5, 2018

Similarity Algorithms by Anas Ismail

Aug 2, 09:00 - 11:00

B3 R5209

Abstract Here we provide two similarity algorithms each for a specific type of data. First, we provide a new algorithm to calculate the Gromov hyperbolicity constant which is a measure of how similar a metric is to a tree metric. We also provide a new algorithm determining how similar two spatial trajectories are.

Jul 22 - Jul 29, 2018

Apr 22 - Apr 29, 2018

Bio2Vec Kickoff Meeting

Apr 25, 08:00 - Apr 26, 17:00

Paris France

big data machine learning Data Analytics

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

Apr 15 - Apr 22, 2018

Neural Inductive Matrix Factorization for Predicting Disease-Gene Associations

Siqing Hou, M.S., Computer Science

Apr 18, 10:00 - 11:30

B3 R5208

bioinformatics machine learning Disease-Gene Associations

In silico prioritization of undiscovered associations can help find causal genes of newly discovered diseases. Some existing methods are based on known associations and side information of diseases and genes. We exploit the possibility of using a neural network model, Neural Inductive Matrix Completion (NIMC) in disease-gene prediction.

Apr 8 - Apr 15, 2018

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

Sarah Alghamdi, Ph.D. Student, Computer Science

Apr 10, 13:00 - 14:30

B9 R3120

biomedicine Ontologies data analysis semantic analysis computation techniques

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

Mar 25 - Apr 1, 2018

Big Data in Biodiversity and Health

Mar 26, 09:30 - Mar 28, 13:30

B3 L5 5209

About We are witnessing today an enormous increase in the volume and complexity of data across a variety of domains, including bioscience. Extracting useful information from such data is challenging. Although many approaches have already been developed, efficient analysis of big data in bioscience domain is far from satisfactory. Biodiversity and health are prominently characterized by a high volume of data with great complexity of information contained, which lead to various approaches to data analyses. The goal of this workshop is to present a selection of efforts currently being made at

Mar 18 - Mar 25, 2018

Computational and Statistical Interface to Big Data

Xin Gao, Program Chair, Computer Science

Mar 19, 08:00 - Mar 21, 17:00

B9 L2 H2

We are now in the fourth paradigm of science: Data Science. The massive amount of structured and unstructured data has posed new challenges and opportunities to the fields of computer science and statistics. Traditional computational and statistical methods for data storage, curation, sharing, querying, updating, visualization, analysis, and privacy have been shown to fail in the big data scenario due to the unprecedented volume, velocity, variety, veracity and value of the big data. This conference will bring together a number of prominent researchers in Computer Science and Statistics with common interests and active research in big data, as well as the researchers at KAUST who regularly generate or face big data, such as those in bioscience and red sea research.

Mar 11 - Mar 18, 2018

Symbolic AI in Computational Biology

Robert Hoehndorf, Associate Professor, Computer Science

Mar 12, 12:00 - 13:00

B9 H1 R2322

About 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 combine symbolic and statistical Artificial Intelligence approaches for the analysis of biological and biomedical

Mar 4 - Mar 18, 2018

Causality-based new drug development: Some successful cases and a new challenge

Naoyuki Kamatani, MD, PhD

Mar 11, 12:00 - 13:00

B2 L5 R5209

artificial intelligence biomedicine drug development

Abstract The success rate of new drug development is extremely low. It is even lower when the new drug has a novel mechanism of action. From my experience, I propose that the success rate can be dramatically increased by predicting the effects of a drug in humans based on the causality-confirmed data. It is dangerous to develop new drugs based on the data in which causality is not confirmed. In biology, there are three different types of relationships in which the causality is confirmed, i.e. the relationships between parent and child, between gene and phenotype and between intervention and

Feb 25 - Mar 4, 2018

Symbolic AI in Computational Biology: Applications to Disease Gene and Drug Target Identification

Robert Hoehndorf, Associate Professor, Computer Science

Feb 26, 16:30 - 17:30

The University of Cambridge in the United Kingdom

Abstract KAUST Assistant Professor Robert Hoehndorf will give a seminar on " Symbolic AI in Computational Biology: Applications to Disease Gene and Drug Target Identification" at the University of Cambridge in the United Kingdom. More Information 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

Feb 18 - Feb 25, 2018

Feb 11 - Feb 25, 2018

Fifth KAUST-NVIDIA Workshop on Accelerating Scientific Applications Using GPUs

Timothy Lanfear , Brent Leback

Feb 18, 08:00 - Feb 20, 17:00

B4 B5 A0215

supercomputing

The KAUST Supercomputing Laboratory is co-organizing with NVIDIA, a leader in accelerated computing and artificial intelligence, a full-day workshop on accelerating scientific applications using GPUs on Tuesday, February 20th, 2018 in the auditorium between buildings 4 and 5.

Feb 4 - Feb 11, 2018

KAUST Research Workshop on Optimization and Big Data

Peter Richtarik, Professor, Computer Science

Feb 5, 08:00 - Feb 7, 05:00

B19 L3 H2

optimization machine learning Social Network Analysis asynchronous algorithms

The age of "big data" is here: data of unprecedented sizes is becoming ubiquitous, which brings new challenges and new opportunities. With this comes the need to solve optimization problems of unprecedented sizes.

Dec 10 - Dec 17, 2017

Novel Computational Methods to Predict Drug–target Interactions Using Graph Mining and Machine Learning Approaches

Rawan Olayan, Ph.D., Computer Science

Dec 11, 10:00 - 12:00

B3 L5 R5220

bioinformatics data integration data mining graph mining machine learning

Abstract Computational drug repurposing aims at finding new medical uses for existing drugs. The identification of novel drug-target interactions (DTIs) can be a useful part of such a task. Finding computationally DTIs is a convenient strategy to identify potentially new DTIs at low cost with reasonable accuracy. However, the current DTI prediction methods suffer a high false positive prediction rate. Here, we present a comprehensive review of the recent progress in the field of DTI prediction from data-centric and algorithmic-centric perspectives that can help in constructing novel reliable

Dec 3 - Dec 10, 2017

Big Data Analyses in Evolutionary Biology

Dec 4, 08:00 - Dec 6, 17:00

B9 H2

big data Big data analysis evolutionary biology

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

Nov 5 - Nov 12, 2017

Contributions to In Silico Genome Annotation

Manal Kalkatawi, Ph.D., Computer Science

Nov 9, 10:00 - 13:00

B3 L5 R5209

bioinformatics data mining machine learning Deep learning genomics

Abstract Genome annotation is an important topic since it provides information for the foundation of downstream genomic and biological research. It is considered as a way of summarizing part of existing knowledge about the genomic characteristics of an organism. Annotating different regions of a genome sequence is known as structural annotation while identifying functions of these regions are considered as a functional annotation. In silico approaches can facilitate both tasks that otherwise would be difficult and time-consuming. This study contributes to genome annotation by introducing

Jun 11 - Jun 18, 2017

Computational Methods for Large Spatio-temporal Datasets and Functional Data Ranking

Jun 13, 09:00 - 10:00

B1 L4 R4102

computational statistics Environmental Statistics

In this thesis defense, I will talk about two topics—computational methods for large spatial datasets and functional data ranking. Both are tackling the challenges of big and high-dimensional data.

May 21 - May 28, 2017

PCCFD - Predictive Complex Computational Fluid Dynamics

David Keyes, Senior Associate to the President, King Abdullah University of Science and Technology

May 22, 08:45 - May 24, 05:00

B9 L2 H1

CFD algorithms applied mathematics numerical analysis Computer science

The PCCFD workshop will focus on cutting-edge research in the field of algorithmic development for CFD and multi-scale complex flow simulations.

May 14 - May 21, 2017

Mining Genome-Scale Growth Phenotype Data through Constant-Column Biclustering

Majed Alzahrani, Ph.D., Computer Science

May 17, 15:00 - 17:00

B3 L5 R5209

data mining machine learning Computational biology

Growth phenotype profiling of genome-wide gene-deletion strains overstresses conditions can offer a clear picture that the essentiality of genes depends on environmental conditions. In this dissertation, we first demonstrate that detecting such "co-fit" gene groups can be cast as a less well-studied problem in biclustering, i.e., constant-column biclustering. Despite significant advances in biclustering techniques, very few were designed for mining in growth phenotype data.

Apr 16 - Apr 23, 2017

Breaking the Boundaries: from Structure to Algorithms

Vadim Lozin, Professor, University of Warwick, UK

Apr 17, 14:00 - 15:00

KAUST

maximum independent set line graphs boundary classes of graphs

Abstract Finding a maximum independent set in a graph is an NP-hard problem. However, restricted to the class of line graphs this problem becomes polynomial-time solvable due to the celebrated matching algorithm of Jack Edmonds. What makes the problem easy in the class of line graphs and what other restrictions can lead to an efficient solution? To answer these questions, we employ the notion of boundary classes of graphs. In this talk, we shed some light on the structure of the boundary separating difficult instances of the problem from polynomially solvable ones and analyze algorithmic tools