Events

Mar 30 - Mar 24, 2019

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  Deep Predictive Models for Mining Electronic Health Records

  Fenglong Ma, Research Assistant, State University of New York at Buffalo, USA

  Mar 26, 12:00 - 13:00

  B1 L4 R4214

  machine learning data mining healthcare computational predictions

  There is an increasing growth in the amount of electronic health records (EHRs) being collected by healthcare facilities. Data mining techniques hold great potential to systematically use such data for identifying not only inefficiencies but also best practices that improve care and reduce costs. However, due to the complexity of EHR data, directly applying traditional machine learning techniques may yield unsatisfactory predictive performance. Recent advances in deep learning-based methods provide unprecedented ability to predict patients’ future health status, but they still suffer from the sparsity issue of EHR data.
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  CEMSE Tutorial in Quantum Computing

  Andrea Morello, Scientia Professor of Electrical Engineering and Telecommunications, UNSW Sydney

  Mar 24, 09:00 - 17:00

  B19 L3 H1

  quantum computing Quantum Measurements

  With the end of the Moore’s law, it became imperative to find new principles for computing that can avoid the current physical limitations. Among the promising approaches is Quantum Computing which has resulted in substantial national investments in research and development in this area by many nations. This first tutorial is designed to target scientists, engineers and mathematicians who are interested in this rapidly growing field but have not yet invested enough time in learning about it.

Mar 23 - Mar 17, 2019

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  On Nonlocal Keller-Segel Type Equations

  Dr. Suleyman Ulusoy, American University of Ras Al Khaimah, UAE

  Mar 20, 16:00 - 17:00

  B1 L3 R3119

  Applied Partial Differential Equations Mathematical Biology

  In the first part of the talk we investigate a Keller-Segel model with quorum sensing and a fractional diffusion operator. This model describes the collective cell movement due to chemical sensing with flux limitation for high cell densities and with anomalous media represented by a nonlinear, degenerate fractional diffusion operator. The purpose here is to introduce and prove the existence of a properly defined entropy solution. In the second part of the talk we will analyze an equation that is gradient flow of a functional related to Hardy-Littlewood-Sobolev inequality in whole Euclidean space of higher dimensions.
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  Locally Discrete Maximum Principle DG finite element method for the transport equation

  Manuel Quezada De Luna, Research Scientist, Numerical Mathematics

  Mar 20, 12:00 - 13:00

  B1 L4 R4214

  flux-corrected transport discrete maximum principle transport equation

  In this talk we propose a Flux Corrected Transport (FCT)-like stabilization suitable for high-order Discontinuous Galerkin (DG) finite element discretizations. As a model problem we consider the multi-dimensional transport equation. The method guarantees that the solution satisfies a local Discrete Maximum Principle (DMP). A solution is said to satisfy a DMP locally if any degree of freedom is bounded with respect to the solution at the previous time step in some defined neighborhood.
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  The Evolution of Machine Learning

  Professor George Karypis, University of Minnesota

  Mar 18, 12:00 - 13:00

  B4 B5 A0215

  University of Minnesota machine learning

  This talk presents an overview of recent methodological advances in developing item-based nearest-neighbor-based recommender systems that have substantially improved their performance. Biography George Karypis is a Distinguished McKnight University Professor and an ADC Chair of Digital Technology at the Department of Computer Science & Engineering at the University of Minnesota, Twin Cities. His research interests span the areas of data mining, high performance computing, information retrieval, collaborative filtering, bioinformatics, cheminformatics, and scientific computing. His research has
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  Design and Control of Multi-Agent Robotic Systems

  Dr. Shinkyu Park, Massachusetts Institute of Technology, USA

  Mar 17, 12:00 - 13:00

  B9 H1 R2322

  MIT

  In this talk, I will present my research on designing multi-agent robotic systems and developing theory and algorithms for system control. The first part of the talk will present my research on designing an animal-borne remote imaging system and developing theory and algorithm for distributed sensor fusion that enables estimation and detection of animal group movements using the system. I will explain data collection capability of the system and share system deployment experiences in monitoring a wild life of African animals In the second part, I will describe my current work on developing a fleet of autonomous robotic vessels.

Mar 16 - Mar 10, 2019

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  Truth, Knowledge, P-Values, Bayes, & Inductive Inference

  Edsel A. Peña, iProfessor in the Department of Statistics, University of South Carolina

  Mar 14, 16:00 - 17:00

  B1 L4 R4102

  statistics Bayesian computational statistics inductive inference

  The talk will concern the role of statistical thinking in the search for Truth. This will bring us to a discussion of P-values, which has been, and still is, a central concept in Statistics and is a critical and highly controversial tool of scientific research. Recently it has elicited much, sometimes heated, debates and discussions. The American Statistical Association (ASA) was even compelled to release an official statement in early March 2016 regarding this issue, and a psychology journal gone to the extreme of banning the use of P-values in articles appearing in its journal!
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  Asymptotic properties of composites

  Rita A. Ferreira, Research Scientist, Mean-field Games and Nonlinear PDE

  Mar 14, 12:00 - 13:00

  B9 L2 R2322 H1

  gamma-convergence Asymptotic Performance Analysis

  In this talk, I will start with an overview of my research to date. Then, I will address in more detail the study of the asymptotic behavior of a two-dimensional variational model within finite crystal plasticity for high-contrast bilayered composites. More precisely, we consider materials arranged into periodically alternated thin horizontal strips of an elastically rigid component and a softer one with one active slip system. The energies arising from our modeling assumptions are of integral form, featuring linear growth and non-standard differential constraints.
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  Numerical Approximation of the Stochastic Cahn-Hilliard Equation and the (Stochastic) Hele-Shaw Problem

  Professor Lubomir Banas (Bielefeld University)

  Mar 13, 00:00

  B1 L2 R2202

  applied mathematics

  The Cahn-Hilliard equation is a fourth order parabolic partial differential equation (PDE) that is widely used as a phenomenological model to describe the evolution of interfaces in many practical problems, such as, the microstructure formation in materials, fluid flow, etc. It has been observed in the engineering literature that the stochastic version of the Cahn-Hilliard equation provides a better description of the experimentally observed evolution of complex microstructure.

Mar 9 - Mar 3, 2019

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  Promoters identification and analysis

  Ramzan Umarov, Ph.D. Student, Computer Science

  Mar 7, 16:00 - 18:00

  B1 L2 R2202

  promoters protein coding RNA genes TSS prediction tools

  Promoter is a key region that is involved in differential transcription regulation of protein-coding and RNA genes. The gene-specific architecture of promoter sequences makes it extremely difficult to devise the general strategy for their computational identification.

Mar 2 - Feb 24, 2019

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  Neuro-symbolic systems in the life sciences

  Robert Hoehndorf, Associate Professor, Computer Science

  Feb 25, 12:00 - 13:00

  B9 H1 R2322

  biomedical data data analysis symbolic methods

  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
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  KAUST Research Conference 2019: New Trends in Biosensors and Bioelectronics

  Khaled Salama, Professor, Electrical and Computer Engineering

  Feb 25, 07:00 - Feb 27, 23:00

  KAUST

  bioelectronics Biosensors nanoelectronics

  The “KAUST Research Conference on New Trends in Biosensors and Bioelectronics” aims to give an overview of the most recent efforts in bioelectronics that tackle the “interface” problem and overcome the limits of the current technologies by generating new materials/architectures/device components. With its truly interdisciplinary nature, this conference will bring scientists from different disciplines together.

Feb 16 - Feb 10, 2019

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  ML Hub Seminar Series | The Machine Learning (ML) Hub

  Bernard Ghanem, Professor, Electrical and Computer Engineering

  Feb 13, 12:00 - 13:00

  B9 H2 R2325

  machine learning

  The Machine Learning Hub @ KAUST is designed to be the one-stop-shop for machine learning (ML) and artificial intelligence (AI) at KAUST. It is an informal forum for exchanging ideas in these areas, including (but not limited to) theoretical foundations, systems, tools, and applications. It will be providing several offerings to the KAUST community interested in ML and AI, including a regular seminar series where new research in the field is presented, an online social forum dedicated to AI and ML discussions, announcements, brainstorming, collaborations, and hands-on activities (e.g

Feb 9 - Feb 3, 2019

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  Semantic Prioritization of Novel Causative Genomic Variants in Mendelian and Oligogenic Diseases

  Imane Boudellioua, Ph.D., Computer Science

  Feb 5, 14:00 - 17:00

  B9 R4223

  functional prediction machine learning genetic variation variant prioritization

  Abstract Recent advances in Next Generation Sequencing (NGS) technologies have facilitated the generation of massive amounts of genomic data which in turn is bringing the promise that personalized medicine will soon become widely available. As a result, there is an increasing pressure to develop computational tools to analyze and interpret genomic data. In this dissertation, we present a systematic approach for interrogating patients' genomes to identify candidate causal genomic variants of Mendelian and oligogenic diseases. To achieve that, we leverage the use of biomedical data available
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  Large-scale Text and Data Integration for Cancer Genomics Research

  Prof. D, Rebholz-Schuhmann (University Cologne, Germany​)​

  Feb 4, 13:00 - 14:00

  B19 L3 H2

  text mining data mining semantic technologies genomics cancer research

  Abstract This talk will give an overview of data and text mining in the life science domain, but will also point out the use of semantic technologies for other domains. Such solutions cover the integration of heterogeneous data sources based on Semantic Web standards (e.g., TCGA, Cosmic, others), the development of smart technologies (e.g., stream processing), graph and literature analytics, and open data initiatives. Application domains are biomedical solutions in cancer genomics and targeted projects together with industrial partners exploiting Semantic Web technologies in general. The

Jan 26 - Jan 20, 2019

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  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 22 - Dec 16, 2018

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  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 8 - Dec 2, 2018

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  Molecular Basis for p85 Dimerization and Allosteric Ligand Recognition

  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

Dec 1 - Nov 25, 2018

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  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[3] and implementation for heterogeneous supercomputers[2, 4-6]. MEGADOCK achieved excellent scalability on
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  AI4GH - Computational Modeling of Malaria Metabolism Reveals Different Stages and Species Nutrient Preferences and Drug Targets

  Alyaa M Mohamed, Ph.D., Bioscience

  Nov 25, 12:00 - 13:00

  B2 R5220

  Malaria kills nearly one-half million people a year and over 1 billion people are at risk of becoming infected by the parasite. Plasmodial infections are difficult to treat for a myriad of reasons, but the ability of the organism to remain latent in hosts and the complex life cycles greatly contributed to the difficulty in treat malaria.
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  AI4GH Seminar Series - Computational Modeling of Malaria Metabolism Reveals Different Stages and Species Nutrient Preferences and Drug Targets

  Alyaa M Mohamed, Ph.D., Bioscience

  Nov 25, 12:00 - 13:00

  B2 R5220

  genome plasmodial infections

  Malaria kills nearly one-half million people a year and over 1 billion people are at risk of becoming infected by the parasite. Plasmodial infections are difficult to treat for a myriad of reasons, but the ability of the organism to remain latent in hosts and the complex life cycles greatly contributed to the difficulty in treat malaria.

Nov 24 - Nov 18, 2018

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

  Fatima Zohra Smaili, Ph.D. Student, Computer Science

  Nov 18, 12:00 - 13:00

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

  Fatima Zohra Smaili, Ph.D. Student, Computer Science

  Nov 18, 12:00 - 13:00

  B2 R5220

  machine learning data analysis

  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.

Nov 17 - Nov 11, 2018

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  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.
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  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 10 - Nov 4, 2018

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  Everything you always wanted to know about single machine large-scale graph analytics

  Jasmina Malicevic, EPFL

  Nov 8, 12:00 - 13:00

  B9 L2 H1

  EPFL large-scale graph optimization

  Abstract: Graphs are a natural way to capture relations among objects or people. This has led to graph processing systems being used in a wide variety of fields, ranging from biology to social networks, and a large number of such systems have been described in the recent literature. The focus of our recent work is on single machine graph processing systems where the graph is processed from main memory or external storage. We perform a systematic comparison of various techniques proposed to speed up in-memory multicore graph processing. In addition, we take an end- to-end view of execution time
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  AI4GH Seminar Series - Genome-scale Regression Analysis Reveals a Linear Relationship for Promoters and Enhancers After Combinatorial Drug Treatment

  Trisevgeni Rapakoulia, Ph.D., Computer Science

  Nov 7, 12:00 - 13:00

  B2 R5220

  machine learning bioinformatics Drug Combinations drug effects cancer

  Drug combination therapy for the treatment of cancers and other multifactorial diseases has the potential of increasing the therapeutic effect while reducing the likelihood of drug resistance. In order to reduce the time and cost spent on comprehensive screens, methods are needed which can model additive effects of possible drug combinations.
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  Prediction of active and inactive chemical compounds from high-throughput assays

  Elaf Jameel Islam, M.S., Computer Science

  Nov 6, 10:15 - 11:45

  B5 L5 R5209

  machine learning

  Abstract This study considers chemical compounds that can exert their activity by interacting with a target protein or other molecular receptor. Our aim is to develop machine learning models that can predict if a chemical compound will be active in a particular test/assay. We will use data from assays that are present in the PubChem knowledge base, specifically in its segment called BioAssays which reports the results of many high-throughput screening experiments. PubChem BioAssays is a valuable resource that contains information from a large number of experiments. In one assay, sometimes many
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  Spatio-Temporal Data Analysis by Transformed Gaussian Processes

  Yuan Yan, PhD Student of Prof. Marc Genton, KAUST

  Nov 5, 17:15 - 18:15

  B2 L5 R5220

  spatio-temporal data analysis statistics

  In the analysis of spatio-temporal data, statistical inference based on the assumption that the data follow a Gaussian distribution is ubiquitous due to its many attractive properties. However, data collected from different fields of science, such as meteorology, astronomy, biology and medicine, rarely meet the assumption of Gaussianity. One option is to apply a monotonic transformation to the data such that the transformed data have a distribution that is close to Gaussian. In this thesis, we focus on a flexible two-parameter family of transformations, the Tukey g-and-h (TGH) transformation
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  Variant Prioritization in Cancer: Understanding and Prediction of Cancer Driver Genes and Mutations

  Sara Althubaiti, Ph.D. Student, Computer Science

  Nov 5, 10:30 - 12:30

  B3 R5209

  bioinformatics text mining machine learning Ontologies cancer

  Abstract Sequencing has identified millions of somatic mutations in human cancers. Identifying and distinguishing cancer driver genes amongst the millions of candidate mutations remains a major challenge. Accurate identification of driver genes and driver mutations is critical for advancing cancer research and personalizing treatment based on accurate stratification of patients. Due to inter-tumor genetic heterogeneity, many driver mutations within a gene occur at low frequencies, which make it challenging to distinguish them from other non-driver mutations. Motivated by these challenges, we
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  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

Nov 3 - Oct 28, 2018

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  Contributions to Computational Methods for Association Extraction from Biomedical Data: Applications to Text Mining and In Silico Toxicology

  Arwa Bin Raies, Ph.D., Computer Science

  Nov 1, 11:00 - 13:00

  B2 L5 R5220

  artificial intelligence High Performance Computing machine learning bioinformatics

  Abstract The task of association extraction involves identifying links between different entities. Here, we make contributions to two applications related to the biomedical field. The first application is in the domain of text mining aiming at extracting associations between methylated genes and diseases from biomedical literature. Gathering such associations can benefit disease diagnosis and treatment decisions. We developed the DDMGD database to provide a comprehensive repository of information related to genes methylated in diseases, gene expression, and disease progression. Using DEMGD, a
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  VSIM: Visualization and Simulation of Genomes for Premarital Testing

  Azza Althagafi, Ph.D. Student, Computer Science

  Nov 1, 08:00 - 10:00

  B3 L5 R5209

  bioinformatics machine learning artificial intelligence genomics

  Abstract Interpretation and simulation of the large-scale genomics data are very challenging, and currently, many web tools have been developed to analyze genomic variation which supports automated visualization of a variety of high throughput genomics data. We have developed VSIM an automated and easy to use web application for interpretation and visualization of a variety of genomics data, it identifies the candidate disease variants by referencing to four databases Clinvar, GWAS, DIDA, and PharmGKB, and predicted the pathogenic variants. Moreover, it investigates the attitude towards
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  AI4GH Seminar Series - VSIM: Visualization and Simulation of Genomes for Premarital Testing

  Azza Althagafi, Ph.D. Student, Computer Science

  Oct 31, 12:00 - 13:00

  B2 R5220

  bioinformatics machine learning artificial intelligence genomics

  Interpretation and simulation of the large-scale genomics data are very challenging, and currently, many web tools have been developed to analyze genomic variation which supports automated visualization of a variety of high throughput genomics data.

Oct 27 - Oct 21, 2018

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  Towards rational design of biosynthesis pathways

  Meshari Alazmi, Ph.D., Computer Science

  Oct 24, 17:00 - 18:30

  B3 R5209

  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.
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  AI4GH Seminar Series - Machine Learning with Biological Knowledge Graphs

  Mona Alshahrani, Ph.D., Computer Science

  Oct 24, 12:00 - 13:00

  B2 R5220

  bioinformatics data integration machine learning knowledge graphs human health

  Abstract Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval, and federated queries. In the past years, feature learning methods that are applicable to graph-structured data are becoming available, but have not yet widely been applied and evaluated on structured biological knowledge. We develop novel methods for feature learning on biological knowledge graphs and apply them to the prediction of edges in the knowledge graph representing problems of finding candidate genes of diseases, or drugs
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  Hihooi: Fusing Workload Scalability with Strong Consistency for Transactional Databases

  Michael Sirivianos, Assistant Professor of Computer Engineering, Cyprus University of Technology

  Oct 22, 11:45 - 13:00

  B9 H1

  With the advent of the Internet and Internet-connected devices, modern business applications can experience rapid increases as well as variability in transactional workloads. Database replication has been employed to scale performance and improve availability of relational databases but past approaches have suffered from various issues including limited scalability, performance versus consistency tradeoffs, and requirements for database or application modifications. This talk presents Hihooi, a replication-based middleware system that is able to achieve workload scalability, strong consistency

Oct 20 - Oct 14, 2018

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  AI4GH Seminar Series - Variant Prioritization in Cancer: Understanding and Prediction of Cancer Driver Genes and Mutations

  Oct 17, 12:00 - 13:00

  B2 R5220

  bioinformatics text mining machine learning cancer Ontologies

  Sequencing has identified millions of somatic mutations in human cancers. Identifying and distinguishing cancer driver genes amongst the millions of candidate mutations remains a major challenge.
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  Solution structure and high order numerical methods for time-dependent problems

  Dr. Hailiang Liu, Professor of Mathematics, Iowa State University (ISU)

  Oct 15, 16:00 - 17:00

  B1 L3 R3422

  The course discusses the solution structure and high order structure-preserving numerical methods for several time-dependent problems, modeled by partial differential equations balanced with diffusion, convection, and various interactions. The main topic focuses on the model structure and the construction of stable, accurate approximate algorithms for these problems, and the interplay between analytical theory and computational aspects of such algorithms with real applications. Here is a list of topics to be discussed: (1) Optimization, entropy and differential equations (2) Critical threshold

Oct 13 - Oct 7, 2018

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  EE Seminar: AMMONO-GaN: ultimate substrate solution for energy saving GaN power electronics

  Dr. Robert Dwiliński (University of Warsaw, Poland)

  Oct 10, 15:00 - 16:00

  B3 L5 R5220

  photonics

  Bulk gallium nitride (GaN) crystals are regarded as the most promising candidates for substrates for optoelectronic, high power and high frequency electronic devices. After dacedes of extensive research on a numer of methods incluging HNPS, HVPE and Flux - ammonothermal (AMMONO) method of bulk GaN growth offers the best combination of crystal quality and size, within foreseen scalability able to meet huge demands of lighting and power electronic markets. Developed as analogy of hydrothermal method commonly used for multi-ton production of synthetic quartz, AMMONO method currently provides 2-inch crystals of excellent structural properties and wide spectrum of electrical parameters.
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  AI4GH Seminar Series | Predicting Protein Function and Phenotype Associations

  Maxat Kulmanov, Research Scientist, Bio-Ontology Research Group

  Oct 10, 12:00 - 13:00

  B2 R5220

  biology biomedicine artificial intelligence knowledge discovery data integration protein function prediction

  The amount of available protein sequences is rapidly increasing, mainly as a consequence of the development and application of high ​throughput sequencing technologies in the life sciences.

Oct 6 - Sep 30, 2018

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  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 22 - Sep 16, 2018

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  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

Aug 4 - Jul 29, 2018

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  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 28 - Jul 22, 2018

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  AI in Cancer Precision Medicine Workshop

  Dr. Paul Schofield

  Jul 25, 08:00 - Jul 26, 13:00

  B3 R5220

  We will investigate how novel AI technologies, including progress in machine learning, knowledge representation and reasoning can be applied to improving diagnosis and ​treatment of cancer in the era of genomic medicine.
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  AI in Cancer Precision Medicine

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

  B3 R5220

  About The workshop on " AI in Cancer Precision Medicine" is jointly organized by researchers from the CBRC, the University of Birmingham, and the University of Cambridge, as part of the KAUST-sponsored Center Partnership program. At the workshop, we will investigate how novel AI technologies, including progress in machine learning, knowledge representation, and reasoning can be applied to improving diagnosis and treatment of cancer in the era of genomic medicine. Speakers Dr. Paul Schofield, Dr. Adeeb Noor, Dr. Andreas Karwath. Agenda The workshop is open to all on Wednesday the 25th of July

Apr 28 - Apr 22, 2018

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  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 21 - Apr 15, 2018

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  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 14 - Apr 8, 2018

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  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 31 - Mar 25, 2018

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  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