Events

Dec 10 - Dec 16, 2017

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  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 9, 2017

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  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 11, 2017

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  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 17, 2017

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  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 27, 2017

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  PCCFD - Predictive Complex Computational Fluid Dynamics

  David Keyes, Professor, Applied Mathematics and Computational Sciences

  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 20, 2017

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  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 22, 2017

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  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 15, 2017

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  Computational Methods for ChIP-seq Data Analysis and Applications

  Haitham M. Ashoor, Ph.D., Computer Science

  Apr 10, 16:00 - 17:30

  B3 L5 5209

  computation techniques machine learning bioinformatics data analysis

  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
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  Genetic Algorithms for Optimization of Machine-learning Models and their Applications in Bioinformatics

  Arturo Magana Mora, Ph.D., Computer Science

  Apr 10, 13:00 - 15:00

  B3 L5 R5209

  machine learning data mining biology genetics bioinformatics

  Abstract ​​​Machine-learning (ML) techniques have been widely applied to solve different problems in biology. However, biological data are large and complex, which often results in extremely intricate ML models. Frequently, these models may have poor performance or may be computationally unfeasible. This study presents a set of novel computational methods and focuses on the application of genetic algorithms (GAs) for the simplification and optimization of ML models and their applications to biological problems. The dissertation addresses the following three challenges. The first challenge is
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  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 19, 2016

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  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 22 - May 28, 2016

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  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 13 - Mar 19, 2016

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  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 13, 2016

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  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 14, 2015

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  Disease Risk Estimation by Combining Case-Control Data with Aggregated Information on the Population at Risk

  Xiaohui Chang, Assistant Professor, College of Business at Oregon State University

  Nov 9, 15:30 - 16:00

  B1 L4 R4102

  statistics

  We propose a novel statistical framework by supplementing case–control data with summary statistics on the population at risk for a subset of risk factors. Our approach is to first form two unbiased estimating equations, one based on the case–control data and the other on both the case data and the summary statistics, and then optimally combine them to derive another estimating equation to be used for the estimation.
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  Kriging Asymptotics

  William Kleiber, Assistant Professor, University of Colorado

  Nov 9, 15:00 - 16:30

  B1 L4 R4102

  spatial statistics

  Spatial analyses often focus on spatial smoothing using the geostatistical technique known as kriging. Theoretical results regarding large sample convergence rates of kriging predictors remain elusive. By casting kriging as a variational problem, we develop an equivalent kernel approximation technique that can also lead to computational feasibility for large data problems.

Oct 4 - Oct 10, 2015

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  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 14, 2015

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  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 29, 2014

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  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 13, 2014

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  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 9, 2013

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  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 - May 5, 2012

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  Scalable Hierarchical Algorithms for eXtreme Computing Workshop

  David Keyes, Professor, Applied Mathematics and Computational Sciences

  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.