Interim Associate Director,
Computational Bioscience Research Center
Wednesday, October 03, 2018, 12:00
- 13:00
Building 2, Room 5220
Contact Person
Abstract
Genomics and health are prime areas
Thursday, August 02, 2018, 09:00
- 11:00
Building 3, Room 5209
Abstract
Here we provide two similarity algo
Wednesday, July 25, 2018, 08:00
- 17:00
Building 3, Room 5220
About
The workshop on "AI in Cancer
MS Degree,
Computer Science
Wednesday, April 18, 2018, 10:00
- 11:30
Building 3, Room 5208
Contact Person
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.
PhD Student,
Computer Science
Tuesday, April 10, 2018, 13:00
- 14:30
Building 9, Room 3120
Contact Person
Abstract
In biomedical research, ontologies are widely used to represent knowledge as well as anno
Monday, March 26, 2018, 09:30
- 13:30
Building 3, Level 5, 5209
About
We are witnessing today an enormous increase in the volume and complexity of data across a
Program Chair,
Computer Science
Monday, March 19, 2018, 08:00
- 17:00
Building 9, Level 2, Hall 2
Contact Person
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.
Interim Associate Director,
Computational Bioscience Research Center
Monday, March 12, 2018, 12:00
- 13:00
Building 9, Lecture Hall 1, Room 2322
Contact Person
About
The life sciences have invested signif
Naoyuki Kamatani, MD, PhD
Sunday, March 11, 2018, 12:00
- 13:00
Building 2, Level 5, Room 5209
Contact Person
Abstract
The success rate of new drug develo
Interim Associate Director,
Computational Bioscience Research Center
Monday, February 26, 2018, 16:30
- 17:30
The University of Cambridge in the United Kingdom
Contact Person
Abstract
KAUST Assistant Professor Robert Ho
Interim Associate Director,
Computational Bioscience Research Center
Friday, February 23, 2018, 09:00
- 16:30
The University of Birmingham in the United Kingdom
Contact Person
Abstract
KAUST Assistant Professor Robert Hoehndorf will be a keynote s
Professor,
Computer Science
Monday, February 05, 2018, 08:00
- 05:00
Conference Center Hall, B19 L3
Contact Person
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.
PhD Degree,
Computer Science
Monday, December 11, 2017, 10:00
- 12:00
Building 3, Level 5, Room 5220
Contact Person
Abstract
Computational drug repurposing aims
Monday, December 04, 2017, 08:00
- 17:00
Engineering Science Hall (building.9), Level 2, Lecture Hall 2
Contact Person
This event is organized by CBRC with financial support from the KAUST Office of Sponsored Research
PhD Degree,
Computer Science
Thursday, November 09, 2017, 10:00
- 13:00
Building 3, Level 5, Room 5209
Contact Person
Abstract
Genome annotation is an important t
Tuesday, June 13, 2017, 09:00
- 10:00
B1 L4 Room 4102
Contact Person
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.
PhD Degree,
Computer Science
Wednesday, May 17, 2017, 15:00
- 17:00
Building 3, Level 5, Room 5209
Contact Person
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.
PhD Degree,
Computer Science
Monday, April 10, 2017, 16:00
- 17:30
Building 3, Level 5, 5209
Contact Person
Abstract
The development of Chromatin immuno
PhD Degree,
Computer Science
Monday, April 10, 2017, 13:00
- 15:00
Building 3, Level 5, Room 5209
Contact Person
Abstract
Machine-learning (ML) techniques
PhD Degree,
Computer Science
Sunday, April 09, 2017, 16:00
- 17:00
Building 3, Level 5, Room 5209
Contact Person
Abstract
Finding a source from which high-en
PhD Degree,
Computer Science
Wednesday, November 16, 2016, 14:00
- 15:00
Lecture Hall 2, Building 9
Contact Person
Abstract
Drug discovery is a process that ta
Sunday, May 22, 2016, 12:00
- 12:00
B9, Hall 1, Room 2322
Contact Person
KAUST UQ SCHOOL 2016 is an annual base thematic conference at King Abdullah University of Science and Techno
Sunday, March 13, 2016, 09:00
- 15:00
B1 L4 Room 4102
Contact Person
LIST OF SPEAKERS
KAUST
Dorit Hammerling, National Center for Atmospheric Research (NCAR)
Wednesday, February 10, 2016, 15:30
- 16:30
B1 L4 Room 4102
Contact Person
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.
Xiaohui Chang, Assistant Professor, College of Business at Oregon State University
Monday, November 09, 2015, 15:30
- 16:00
B1 L4 Room 4102
Contact Person
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.