Events

May 26 - Jun 2, 2019

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  Modern Techniques of Statistical Optimization for Machine Learning

  Tong Zhang, Professor of Computer Science and Mathematics, HKUST

  May 29, 12:00 - 13:00

  B9 H1

  HKUST statistics nonconvex stochastic optimization machine learning

  Many problems in machine learning rely on statistics and optimization. To solve these problems, new techniques are needed. I will show some of these new techniques through selected machine learning problems I have recently worked on, such as nonconvex stochastic optimization, distributed training, adversarial attack, and generative models.

May 12 - May 19, 2019

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  Adaptive Intrusion Management System for Databases

  Muhamad Felemban, Assistant Professor, Computer Engineering Department, KFUPM

  May 13, 12:00 - 13:00

  B9 L2 H1

  cybersecurity

  With the growing cyber-security threats to governmental and organizational infrastructures, the need to develop high resilient systems that preserve the security and privacy of data is becoming increasingly important. Although there is a large body of work on security and privacy countermeasures, cyber-attacks still persist. A prominent type of such attacks is intrusion attack that aims at data tampering, which can impair the availability and the integrity of data.

May 5 - May 12, 2019

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  A Storage-Optimal Convex Optimization Framework with Applications to Semidefinite Programming

  Alp Yurtsever, PhD Candidate, EPFL

  May 6, 12:00 - 13:00

  B9 L2 H2

  semidefinite programming convex optimization

  With the ever-growing data sizes along with the increasing complexity of the modern problem formulations, there is a recent trend where heuristic approaches with unverifiable assumptions are overtaking more rigorous, conventional optimization methods at the expense of robustness. This trend can be overturned when we exploit dimensionality reduction at the core of optimization. I contend that even the classical convex optimization did not reach yet its limits of scalability.

Apr 28 - May 5, 2019

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  On Knowledge Graph and Causality for Recommender Systems

  Prof. Guandong Xu, School of Software and Advanced Analytics Institute, University of Technology Sydney

  Apr 30, 14:00 - 15:00

  B1 L4 R4214

  text and data analytics data science data mining University of Technology Sydney

  Knowledge Graph (KG) is a large-scale semantic network consisting of entities/concepts as well as the semantic relationships among them, which could be considered as a concise version of Semantic Web. Recently KG is emerging as a hot topic of knowledge discovery and management under artificial intelligence, facilitating semantic computing. Causal relation is a reflection of user behaviours with backend intention, which is related another emerging hot topic – recommendation interpretability. This talk will cover the recent research progresses in these two areas and highlight some open research challenges in recommender systems.
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  OmniLedger: A Secure, Scale-Out, Decentralized Ledger via Sharding

  Dr Philipp Jovanovic, École Polytechnique Fédérale de Lausanne (EPFL)

  Apr 29, 12:00 - 13:00

  B9 L2 H1

  scale out security transactions

  Designing a secure permissionless distributed ledger that performs on par with centralized payment processors such as Visa is challenging. Most existing distributed ledgers are unable to "scale-out" -- growing total processing capacity with number of participants -- and those that do compromise security or decentralization. This work presents OmniLedger, the first scale-out distributed ledger that can preserve long-term security under permissionless operation. OmniLedger ensures strong correctness and security by using a bias-resistant public randomness protocol to choose large statistically representative shards to process transactions, and by introducing an efficient cross-shard commit protocol to handle transactions affecting multiple shards atomically.

Apr 21 - Apr 28, 2019

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  Day 2 - Deep learning and the latest algorithms in artificial intelligence

  Prof. Xavier Bresson, Data Science and AI Research Centre at Nanyang Technological University (NTU) Singapore

  Apr 25, 09:00 - 17:00

  B4 B5 A0215

  Nanyang Technological University Deep learning machine learning

  The ML Hub offers a 2-day short course on deep learning and the latest algorithms in artificial intelligence. The course will be given by Professor Xavier Bresson from the Nanyang Technological University (NTU) in Singapore, who is a leading researcher in the field of deep learning. The course will include the theory of deep learning techniques as well as practical exercises. Prerequisite knowledge: Basic knowledge of linear algebra (e.g. matrix multiplication) and script programming (e.g. Python, Matlab, R) are needed. The coding will be done in Python. Note, that this course has limited seating and filling registration form does not guarantee acceptance. If you are selected, you will receive a confirmation e-mail.
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  Introduction to High Performance Computational Chemistry with an Application of Hierarchical Matrices

  Associate Professor Edmond Chow, School of Computational Science and Engineering, College of Computing, Georgia Institute of Technology

  Apr 24, 14:00 - 15:30

  B2 B3 A0215

  multipole quantum chemistry Georgia Tech

  This talk begins with an introduction to quantum chemistry for HPC researchers. We discuss the main computational kernels, how their performance is limited by various bottlenecks, and algorithms and implementations that improve performance. One particular kernel is the calculation of the Coulomb matrix. Whereas the fast multipole method (FMM) can be used to rapidly compute the Coulomb potential for sets of point charges, continuous variants of FMM were developed in the 1990s for sets of charge distributions that arise in quantum chemistry.
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  Day 1 - Deep learning and the latest algorithms in artificial intelligence

  Prof. Xavier Bresson, Data Science and AI Research Centre at Nanyang Technological University (NTU) Singapore

  Apr 24, 09:00 - 17:00

  B4 B5 A0215

  Nanyang Technological University Deep learning machine learning

  The ML Hub offers a 2-day short course on deep learning and the latest algorithms in artificial intelligence. The course will be given by Professor Xavier Bresson from the Nanyang Technological University (NTU) in Singapore, who is a leading researcher in the field of deep learning. The course will include the theory of deep learning techniques as well as practical exercises. Prerequisite knowledge: Basic knowledge of linear algebra (e.g. matrix multiplication) and script programming (e.g. Python, Matlab, R) are needed. The coding will be done in Python. Note, that this course has limited seating and filling registration form does not guarantee acceptance. If you are selected, you will receive a confirmation e-mail.
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  Unstructured Computations on Emerging Architectures

  Mohammed AlFarhan

  Apr 23, 13:00 - 14:00

  B3 L5 R5209

  This dissertation describes detailed performance engineering and optimization of an unstructured computational aerodynamics software system with irregular memory accesses on a wide variety of multi- and many-core emerging high-performance computing scalable architectures, which are expected to be the building blocks of energy-austere exascale systems, and on which algorithmic- and architecture-oriented optimizations are essential for achieving worthy performance.
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  Convolutional Neural Networks on Graphs

  Prof. Xavier Bresson, NTU, Singapore

  Apr 23, 12:00 - 13:00

  B9 H2

  Nanyang Technological University Deep learning machine learning

  In the past years, deep learning methods have achieved unprecedented performance on a broad range of problems in various fields from computer vision to speech recognition. So far research has mainly focused on developing deep learning methods for grid-structured data, while many important applications have to deal with graph-structured data.
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  Big Data Approaches to Scientific Output Prediction

  Prof. Yannis Manolopoulos, Open Univ. of Cyprus

  Apr 22, 12:00 - 13:00

  B9 L2 H1

  During the past two decades the availability of big scholarly data repositories such as Google Scholar, Elsevier Scopus offered tremendous opportunities to analyze scientific production and help develop models for it. The development of computerized analysis methods for these voluminous scholarly data allows to understand, quantify and predict research activities and the corresponding outcomes. The focus of this talk is on techniques to forecast future impact of a scientist; this is a very interesting problem because it allows for making effective hiring/promotion decisions and research fund allocation, among others.

Apr 14 - Apr 28, 2019

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  Singularity formation of Nonlinear Evolution Equations

  Dr. Van Tien Nguyen, NYU Abu Dhabi

  Apr 21, 12:00 - 13:00

  B3 L5 R5220

  Many central problems in geometry, mathematical physics and biology reduce to questions regarding the behavior of solutions of nonlinear evolution equations. The global dynamical behavior of bounded solutions for large times is of significant interest. However, in many real situations, solutions develop singularities in finite time. The singularities have to be analyzed in detail before attempting to extend solutions beyond their singularities or to understand their geometry in conjunction with globally bounded solutions. In this context we have been particularly interested in qualitative descriptions of blowup.

Apr 14 - Apr 21, 2019

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  Visualizing Trees

  Prof. Xiaoru Yuan, Peking University

  Apr 15, 12:00 - 13:00

  B9 L2 H1 R2322

  Peking University tree visualisation GoTree visual analytics map metaphor Social media

  In this talk, I will introduce a few recent works on tree visualization. First I will present a visualization technique for comparing topological structures and node attribute values of multiple trees. I will further introduce GoTree, a declarative grammar supporting the creation of a wide range of tree visualizations. In the application side, visualization and visual analytics on social media will be introduced. The data from social media can be considered as graphs or trees with complex attributes. A few approaches using map metaphor for social media data visualization will be discussed.

Apr 7 - Apr 14, 2019

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  From Bioinformatics to Translational and Health Informatics: Whitening the Black Box

  Prof. Yasser EL-Manzalawy, Information Sciences and Technology, Pennsylvania State University

  Apr 8, 12:00 - 13:00

  B9 H1

  Pennsylvania State University machine learning

  Machine learning has been extensively used in developing predictive models for a variety of bioinformatics tools. In many of these applications, to achieve the highest possible predictive performance, black box models (e.g., SVMs and Neural Networks) had been preferred over white box models (e.g., Decision Trees and Rule-based models). First, I argue that sacrificing interpretability for the sake of performance is a reasonable decision for many bioinformatics applications. However, I will demonstrate that uncareful utilization of black box models can lead to misinterpretations of the results.

Mar 31 - Apr 7, 2019

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  The Discrete Charm of Geometry

  Prof. Alexander I. Bobenko, Technische Universität Berlin

  Apr 3, 13:15 - 14:45

  B9 L2 H2

  mathematics discrete models geometric structures continuous shapes

  How is modern mathematical teamwork carried out? The multiple award-winning film The Discrete Charm of Geometry by Ekaterina Eremenko will screen on April 3rd after the CEMSE Dean's Distinguished Lecture Discrete conformal mappings and Riemann Surfaces: Theory and Applications by Prof. Alexander I. Bobenko, Technische Universität Berlin. Following the screening, Prof. Bobenko will be available for a Q&A session.
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  Discrete conformal mappings and Riemann Surfaces: Theory and Applications

  Prof. Alexander I. Bobenko, Technische Universität Berlin

  Apr 3, 12:00 - 13:00

  B9 L2 H2

  discrete conformal mappings differential geometry mathematics

  The general idea of discrete differential geometry is to find and investigate discrete models that exhibit properties and structures characterisitic of the corresponding smooth geometric objects. This is a challenging problem, since equivalent points of view in the smooth setting may lead to a number of inequivalent treatments in the discrete setting. We will illustrate the paradigm of structure-preserving discretizations on the example of conformal maps by showing how simple definitions lead to surprisingly rich theories.

Mar 24 - Mar 31, 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.

Mar 17 - Mar 31, 2019

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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 17 - Mar 24, 2019

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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

Mar 3 - Mar 10, 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.

Feb 24 - Mar 3, 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

Feb 3 - Feb 10, 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

Nov 18 - Dec 2, 2018

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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 11 - Nov 25, 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 4 - Nov 18, 2018

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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 4 - Nov 11, 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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  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

Oct 28 - Nov 4, 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 21 - Oct 28, 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 14 - Oct 21, 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.

Oct 7 - Oct 14, 2018

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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.

Sep 30 - Oct 7, 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 16 - Sep 23, 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

Jul 29 - Aug 5, 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 22 - Jul 29, 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.

Apr 15 - Apr 22, 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 8 - Apr 15, 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 25 - Apr 1, 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

Mar 18 - Mar 25, 2018

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  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

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  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

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  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

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  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