Events

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

Apr 9 - Apr 16, 2017

Apr 2 - Apr 16, 2017

Novel Computational Methods that Facilitate Development of Cyanofactories for Free Fatty Acid Production by Olaa Motwalli

Olaa A. Motwalli, Ph.D., Computer Science

Apr 9, 16:00 - 17:00

B3 L5 R5209

machine learning bioinformatics graph mining genomics

Abstract Finding a source from which high-energy-density biofuels can be derived at an industrial scale has become an urgent challenge for renewable energy production. Some microorganisms can produce free fatty acids (FFA) as precursors towards such high-energy-density biofuels. In particular, photosynthetic cyanobacteria are capable of directly converting carbon dioxide into FFA. However, current engineered strains need several rounds of engineering to reach the level of FFA production for it to be commercially viable. Thus, new chassis strains that require less engineering are needed

Nov 13 - Nov 20, 2016

Novel Data Mining Methods for Virtual Screening of Biological Active Chemical Compounds by Othman Soufan

Othman Soufan, Ph.D., Computer Science

Nov 16, 14:00 - 15:00

H2 B9

machine learning data mining Computational biology biomedical applications Chemical compounds visualization

Abstract Drug discovery is a process that takes many years and hundreds of millions of dollars to reveal a con dent conclusion about a specific treatment. Part of this sophisticated process is based on preliminary investigations to suggest a set of chemical compounds as candidate drugs for the treatment. Computational resources have been playing a significant role in this part through a step known as virtual screening. From a data mining perspective, the availability of rich data resources is key in training prediction models. Yet, the difficulties imposed by big expansion in data and its

May 15 - May 29, 2016

Welcome at Kaust UQ School On Numerical Methods For Direct And Inverse Problems 2016 22-28 May

Raul Tempone, Professor, Applied Mathematics and Computational Sciences

May 22, 12:00 - May 28, 12:00

B9 H1 R2322

numerical methods stochastic differential equations statistics

KAUST UQ SCHOOL 2016 is an annual base thematic conference at King Abdullah University of Science and Technology held by Raul Tempone, Professor of Applied Mathematics and Computational Sciences at CEMSE (Computer, Electrical and Mathematical Sciences & Engineering Division). Tempone’s interests in the mathematical foundation of computational science and engineering are reflected in this summer school. The school’s goal is to provide participants with an overview on the most recent research progress in the field of uncertainty quantification, with emphasis to • Multi-Level and Multi-Index

Mar 6 - Mar 20, 2016

Workshop on Statistical Process Monitoring and Risk Assessment for Engineering and Spatial Environmental Applications

Mar 13, 09:00 - Mar 15, 15:00

B1 L4 R4102

Environmental Statistics spatial statistics

LIST OF SPEAKERS KAUST Environmental Statistics Group, CEMSE Division - Ying Sun (ying.sun@kaust.edu.sa), PI - Fouzi Harrou (fouzi.harrou@kaust.edu.sa), Postdoc - Huang Huang (huang.huang@kaust.edu.sa), PhD Student - Tianbo Chen (tianbo.chen@kaust.edu.sa), PhD Student - Rui Meng (rui.meng@kaust.edu.sa), Master Student - Sulaiman Binkhamis (sulaiman.binkhamis@kaust.edu.sa), Master Student Hydrology and Land Observation Group, BESE Division - Gaohong Yin (gaohong.yin@kaust.edu.sa), Master Student Collaborators - NorEddine Ghaffour (noreddine.ghaffour@kaust.edu.sa), co-I, WDRC - Matthew McCabe

Feb 7 - Feb 14, 2016

A Distributed Implementation of the Multi-resolution Approximation for Very Large Spatial Data

Dorit Hammerling, National Center for Atmospheric Research (NCAR)

Feb 10, 15:30 - 16:30

B1 L4 R4102

spatial statistics

With data of rapidly increasing sizes in the environmental and geosciences such as satellite observations and high-resolution climate model runs, the spatial statistics community has recently focused on methods that are applicable to very large data. One such state-of-the-art method is the multi-resolution approximation (MRA), which was specifically developed with high performance computer architecture in mind.

Nov 8 - Nov 15, 2015

Sep 27 - Oct 11, 2015

Workshop on Computational Space-Time Statistics

Oct 4, 09:45 - Oct 6, 10:45

B1 L4 R4102

Statistics of extremes Environmental Statistics

Workshop on Computational Space-Time Statistics

Mar 8 - Mar 15, 2015

Collective Estimation of Multiple Bivariate Density Functions with Application to Angular-sampling-based Protein Structure Prediction

Mehdi Moodaaliat, Assistant Professor, Marquette University

Mar 10, 15:00 - 16:00

B1

statistics

In this talk we develop a method for simultaneous estimation of density functions for a collection of populations of protein backbone angle pairs. Each log density function in the collection is modeled as a linear combination of a common set of basis functions. The shared basis functions are modeled as bivariate splines on triangulations and are estimated using data. The circular nature of angular data is taken into account by imposing appropriate smoothness constraints across boundaries of the triangles.

Nov 23 - Nov 30, 2014

Bayesian Regression Trees, Nonparametric Heteroscedastic Regression Modeling and MCMC Sampling

Matthew Pratola, Assistant Professor of Statistics, The Ohio State University

Nov 24, 15:00 - 16:00

B1 L2

nonparametric statistics Bayesian Statistics

In this talk, we introduce a new Bayesian regression tree model that allows for possible heteroscedasticity in the variance model and devise novel MCMC samplers that appear to adequately explore the posterior tree space of this model.

Sep 7 - Sep 14, 2014

Uncertainty Quantification of Tsunami Models

Serge Guillas, Professor of Statistics, University College London (UCL)

Sep 8, 15:00 - 16:00

B1

uncertainty quantification Environmental Statistics

In this talk, we first show various strategies for the efficient emulation of simulators having uncertain inputs, with applications to tsunami wave modelling. A fast surrogate of the simulator's time series of outputs is provided by the outer product emulator.

Mar 3 - Mar 10, 2013

Parametric Problems, Stochastic, and Identification By Prof. Hermann Matthies (ISCTUB, Germany)

Prof. Hermann Matthies, Institute of Scientific Computing TU Braunschweig, Geramany

Mar 6, 15:00 - 16:00

B1 R4102

Parameter identification problems are formulated in a probabilistic language, where the randomness reflects the uncertainty about the knowledge of the true values. This setting allows conceptually easily incorporating new information, e. g. through a measurement, by connecting it to Bayes's theorem. The unknown quantity is modelled as a (may be high-dimensional) random variable. Such a description has two constituents, the measurable function and the measure.

Apr 22 - Apr 29, 2012

Scalable Hierarchical Algorithms for eXtreme Computing Workshop

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

Apr 28, 08:00 - Apr 30, 16:00

KAUST

scientific computing

The 2012 SHAX-C workshop focuses international expert attention on the prospects for the three great hierarchical algorithms of scientific computing: multigrid, fast transforms, and fast multipole methods. These methods are kernels in simulations based on formulations of partial differential equations, integral equations, and interacting particles – in short, they are scientific and engineering workhorses.