Events

Feb 16 - Feb 23, 2020

Towards Accurate Biomedical Genomics Anywhere Anytime - Graduate Seminar

Xin Gao, Program Chair, Computer Science

Feb 17, 12:00 - 13:00

B9 L2 H1 R2322

biomedicine DNA sequencing

Abstract Current genetic diagnosis by next-generation sequencing requires a large investment of resources and offers little point-of-care portability. Furthermore, it is unable to detect many types of genetic variations - including large deletions, duplications, and balanced translocations - that are relevant to human diseases and health. Comparing to other sequencing technologies, Nanopore sequencing owns the advantages of point-of-care (i.e., sequencing anywhere anytime), long reads (i.e., assembly-free to detect various genetic variations), and PCR free (i.e., sample preparation is easy)

Feb 9 - Feb 23, 2020

Novel computational methods for promoter identification and analysis

Ramzan Umarov, Ph.D. Student, Computer Science

Feb 16, 16:00 - 18:00

B2 L5 R5209

machine learning promoters protein coding RNA genes TSS prediction tools

In this dissertation, I present the methods I have developed for prediction of promoters for different organisms. Instead of focusing on the classification accuracy of the discrimination between promoter and non-promoter sequences, I predict the exact positions of the TSS inside the genomic sequences, testing every possible location. The developed methods significantly outperform the previous promoter prediction programs by considerably reducing the number of false positive predictions. Specifically, to reduce the false positive rate, the models are adaptively and iteratively trained by changing the distribution of samples in the training set based on the false positive errors made in the previous iteration. The new methods are used to gain insights into the design principles of the core promoters. Using model analysis, I have identified the most important core promoter elements and their effect on the promoter activity. I have developed a novel general approach to detect long range interactions in the input of a deep learning model, which was used to find related positions inside the promoter region. The final model was applied to the genomes of different species without a significant drop in the performance, demonstrating a high generality of the developed method.

Feb 9 - Feb 16, 2020

Optimal Reference Frames, Scaling and Features in Visualization

Prof. Holger Theisel, Visual Computing, Magdeburg University

Feb 10, 12:00 - 13:00

B9 L2 H1 R2322

visual computing visualization

In Visualization, the success or failure of an analysis often depends on the choice of some subtle parameters or design choices. While simple heuristics are often sufficient, in some cases they make the analysis miserably fail. We present three approaches in visualization where a careful choice of optimal parameters results in completely new algorithms: 1) the choice of a reference frame for finding objective vortices in flow visualization, 2) the choice of a scaling of high-dimensional data sets for finding linear projections to 2D in information visualization, and 3) the choice of a feature definition along with numerical extraction methods for visualizing recirculation phenomena in flows.

Feb 2 - Feb 9, 2020

Jan 26 - Feb 9, 2020

Architectural Defenses against Indirect Threats

Yasser Shalabi, Graduate Student, Computer Science, University of Illinois Urbana-Champaign

Feb 2, 12:00 - 13:00

B9 L2 H2

Computer architecture

The potential of indirect execution attacks – e.g. Return-Oriented-Programming and Transient-Execution based Side-channels – threaten all modern computing platforms. Standard security policies are unable to eliminate these threats. What is the role of hardware in mitigating these threats? Why are the latest processor designs no longer proactively eliminating threats? In this talk, we will explore these questions and reconsider the role of hardware in securing systems. I will present Record-and-Replay as a fundamental solution that can enable a hardware-software co-design that can strengthen the security of modern computing platforms.

Jan 26 - Feb 2, 2020

Jan 19 - Feb 2, 2020

Dictionary Attacks on Biometrics

Prof. Nasir Memon, Vice Dean for Academics and Student Affairs and Professor of Computer Science and Engineering at the New York University Tandon School of Engineering

Jan 26, 16:00 - 17:00

B9 L2 H2

biometrics dictionary attacks user authentication payment authorization

Contrary to the prevailing belief, we show that user authentication based on biometrics is vulnerable to dictionary attacks. We show the problem is particularly significant for partial prints used in smartphones and increasingly adopted for authentication tasks ranging from unlocking the devices screen up to payment authorization. We also show that speaker verification systems are also vulnerable to dictionary attacks. We then discuss ways to mitigate such attacks.

Jan 19 - Jan 26, 2020

KAUST Research Conference Digital Health 2020

Takashi Gojobori, Ibn Sina Distinguished Professor, Bioscience

Jan 20, 08:00 - Jan 22, 17:00

B19 L2 H1

digital health artificial intelligence computational science big data analytics Omics

Computational Bioscience Research Center at King Abdullah University of Science and Technology is pleased to announce the KAUST Research Conference on Digital Health 2020.

Jan 12 - Jan 19, 2020

BBDS: A Privacy-preserving Blockchain-Based Data Sharing System

Prof. Jin Li, Computer Science, Guangzhou University

Jan 15, 12:00 - 13:00

B1 L4 R4214

internet of things security

Abstract Nowadays, the devices in the Internet of Things have been widely used. These devices generate a huge amount of sensitive data, which are susceptible to cyber attacks. In this talk, we introduce an efficient blockchain based approach with several computational models of physically unclonable functions (denoted by PUF model). The presented protocol aims to guarantee the authentication of the devices with fast verification process. Furthermore, the combination of Blockchain and PUF motivates us to propose an efficient protocol that guarantees data provenance and data integrity in IoT

Jan 5 - Jan 12, 2020

PREMA: Principled Tensor Data Recovery from Multiple Aggregated Views

Faisal M. Almutairi, Ph.D. Candidate, Electrical and Computer Engineering, University of Minnesota

Jan 8, 12:00 - 13:00

B1 L4 R4214

data disaggregation algorithms University of Minnesota

The proposed method, called PREMA, leverages low-rank tensor factorization tools to provide recovery guarantees under certain conditions. PREMA is flexible in the sense that it can perform the disaggregation task on data that have missing entries, i.e., partially observed. The proposed method considers challenging scenarios: i) the available views of the data are aggregated in two dimensions, i.e., double aggregation, and ii) the aggregation patterns are unknown. Experiments on real data from different domains, i.e., sales data from retail companies, crime counts, and weather observations, are presented to showcase the effectiveness of PREMA.

Dec 29 - Jan 5, 2019

Practical Cryptographic Framework to Manage Security and Privacy for IoT Endpoint devices

Prof. Chunhua Su, Computer Science, University of Aizu

Jan 1, 12:00 - 13:00

B1 L4 R4214

IoT cybersecurity Cryptography machine learning user authentication user's bio-features device capabilities

In this talk, the speaker will provide a high-level introduction to his recent research on IoT endpoint security. Firstly, he will introduce requirements followed by a discussion on cryptographic algorithm implementation. He will mainly focus on an overview of efficient cryptography for IoT endpoints and system privacy issues. Then he will discuss security management approaches, positives, negatives and challenges to resolve, linking to the endpoint device security section with regards to realistic device needs/capabilities.

Dec 8 - Dec 15, 2019

SLATE: Design of a Modern Distributed and Accelerated Dense Linear Algebra Library

Dalal Sukkari, Ph.D., Applied Mathematics and Computational Sciences

Dec 11, 16:00 - 17:00

B2 L5 R5220

The SLATE (Software for Linear Algebra Targeting Exascale) library is being developed to provide fundamental dense linear algebra capabilities for current and upcoming distributed high-performance systems, both accelerated CPU–GPU based and CPU based.

Dec 1 - Dec 8, 2019

Proximal Splitting Methods for Convex Optimization: An Introduction

Laurent Condat, Senior Research Scientist, Computer, Electrical and Mathematical Sciences and Engineering

Dec 2, 12:00 - 13:00

B9 L2 H1 R2322

This talk will be a gentle introduction to proximal splitting algorithms to minimize a sum of possibly nonsmooth convex functions. Several such algorithms date back to the 60s, but the last 10 years have seen the development of new primal-dual splitting algorithms, motivated by the need to solve large-scale problems in signal and image processing, machine learning, and more generally data science. No background will be necessary to attend the talk, whose goal is to present the intuitions behind this class of methods.

Nov 24 - Dec 8, 2019

Enabling smart and connected systems

Ahmed Eltawil, Associate Dean for Research, Computer, Electrical and Mathematical Sciences and Engineering

Dec 1, 12:00 - 13:00

B9 L2 H1 R2322

The talk will discuss how recent advances in wireless computing and communication nodes can be harnessed to serve the multitude of deployment scenarios required to empower communities of the future with smart and connected systems. In this talk, we address fundamental questions that should be asked when contemplating future smart and connected systems, namely, How, Where and What? (1) How can we design computing and communication nodes that best utilize resources in a way that is cognizant of both the abilities of the platform, as well as the requirements of the network? (2) Where are the nodes deployed? By understanding the context of deployment, one can architect unique solutions that are currently unimaginable. With the transformation to diverse applications such as body area networking, critical infrastructure monitoring, precision agriculture, autonomous driving, etc., the need for innovative solutions becomes even more amplified. (3) What benefit can be inferred from the data gathered by nodes in the capacity of computing, communication, and sensing?

Nov 24 - Dec 1, 2019

Hidden Backdoors in Deep Learning Systems

Prof. Ben Zhao, Computer Science, University of Chicago, USA

Nov 25, 12:00 - 13:00

B9 L2 H1 R2322

In this talk, I will describe two recent results on detecting and understanding backdoor attacks on deep learning systems. I will first present Neural Cleanse (IEEE S&P 2019), the first robust tool to detect a wide range of backdoors in deep learning models. We use the idea of perturbation distances between classification labels to detect when a backdoor trigger has created shortcuts to misclassification to a particular label. Second, I will also summarize our new work on Latent Backdoors (CCS 2019), a stronger type of backdoor attack that is more difficult to detect and survives retraining in commonly used transfer learning systems. Latent backdoors are robust and stealthy, even against the latest detection tools (including neural cleanse).

Nov 17 - Dec 1, 2019

Network Function Virtualization: What, Why and How?

Ahmed E. Kamal, Department of Electrical and Computer Engineering, Iowa State University, Ames, IA, USA.

Nov 24, 11:00 - 12:00

B1 L3 R3119

Virtualized Network Function

The European Telecom Standards Institute (ETSI) introduced the concept of Network Function Virtualization (NFV) with the aim of efficient network architecture and network system operation. In traditional networks, network functions are implemented in dedicated physical machines which are designed for single functionalities. Network services have been provided by connecting these physical machines, so the network architecture has been highly rigid and hard to change. NFV environment provides a more flexible and scalable network configuration and implementation through the softwarization of physical network functions. Network functions are transformed to Virtual Machines (VMs) so that Virtualized Network Functions (VNFs) can be implemented in commodity servers built for common uses, including public clouds.

Nov 17 - Nov 24, 2019

Eliminating Waiting Time Inequities for Deceased Donor Kidney Transplantation in the USA

Dr. Joris van de Klundert, Professor of Operations Management, Prince Mohammad Bin Salman College (MBSC) of Business & Entrepreneurship

Nov 18, 12:00 - 13:00

B9 L2 H1 R2322

organ transplantation wait time modeling Equity Maximization network flows queuing theory Rawls theory

This research formally models, analyzes and maximizes equity of transplant waiting times and probabilities using queuing theory and network flows, based on Rawls' theory of justice. The presented formal models address inequities resulting from blood type incompatibilities, which are interrelated to ethnic differences in patient and donor rates.

Nov 10 - Nov 17, 2019

Langevin Monte Carlo as an optimization algorithm

Adil Salim, Postdoctoral Research Fellow, Computer Science

Nov 11, 12:00 - 13:00

B9 L2 H1 R2322

machine learning Langevin Monte Carlo optimization

Adil Salim is mainly interested in stochastic approximation, optimization, and machine learning. He is currently a Postdoctoral Research Fellow working with Professor Peter Richtarik at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST).

Nov 3 - Nov 10, 2019

Oct 20 - Oct 27, 2019

Selected applications of splines of arbitrary manifold topology

Pieter Barendrecht, PhD Student, Computer Science, University of Groningen, The Netherlands

Oct 24, 14:00 - 15:00

B1 L4 R4214

Abstract There are many intriguing aspects and applications of splines, in particular in the setting of arbitrary manifold topology. In this talk we will take a look at a recently published spline-based subdivision scheme for three-valent meshes (i.e. meshes composed of mostly hexagons), providing us with a good understanding of subdivision surfaces in general. We then move on to study improved quadrature rules for the well-known Catmull-Clark subdivision scheme for quad-dominant meshes, something long-awaited by spline-based numerical methods such as isogeometric analysis. Finally, we shift
Finding the linchpins of the dark web: Understanding and detecting malicious infrastructure

Dr. Sumayah Alrwais, Assistant Professor, King Saud University, Riyadh, KSA

Oct 21, 12:00 - 13:00

B9 L2 H1 R2322

Cyber attacks and defenses Web and Network security cybersecurity malicious infrastructure detection

In this talk, Sumayah will survey a number of malicious hosting infrastructures for different services and approaches to detecting them. Among them are works on an emerging trend of Bulletproof hosting services reselling infrastructure from lower-end service providers, use of residential proxy as a service to avoid server-side blocking and DNS based hosting infrastructure.

Oct 13 - Oct 20, 2019

Oct 6 - Oct 13, 2019

Learning for Visual Data Synthesis and Analysis

Dr. Timo Ropinski, Professor, Visual Computing, Ulm University, Germany

Oct 7, 12:00 - 13:00

B9 L2 H1 R2322

Deep learning visual data synthesis visual data analysis estimation and segmentation Monte carlo methods

Abstract In this talk, I will present our recent advances in deep learning to synthesize and analyze visual data, such as images, volumes, and point clouds. The presented approaches are loosely aligned along the classical computer graphics rendering pipeline, whereby both structured and unstructured data are handled. I will first present concepts for learning in object space, i.e., directly on the data to be rendered. To realize different visual tasks, such as normal estimation and segmentation, I will discuss how Monte Carlo integration can be used to realize convolutions on point cloud data

Sep 29 - Oct 6, 2019

Task-driven Automated Data Visualization

Dr. Yunhai Wang, Professor, Computer Science, Shandong University, China

Oct 2, 12:00 - 13:00

B9 L2 H2 R2325

data visualisation visualisation techniques parameters time series data high dimensional data EdWordle ShapeWordle

Abstract By providing visual representations of data, visualization can help people to carry out many data analysis tasks more effectively. Given a data set, however, there are often too many possible visualization techniques, with each technique having many parameters to be tuned. This arises the question is if it is possible to automatically design a visualization that is best suited to pursue a given task on given input data. With my group, I developed a number of techniques to achieve this goal for different data sets such as time-series data, high-dimensional data, text as well as graphs
BGP hijacks: the next generation stealthy Command and Control for botnets

Dr. Marc Dacier, Chair of the Digital Security department and a full Professor at Eurecom, France

Sep 30, 12:00 - 13:00

B9 L2 H1 R2322

botnets C2C malware command and control

Abstract It is well known that malware spreading over the Internet aim at transforming vulnerable devices into bots that can be misused by attackers. These armies of bots constitute what is commonly called "botnets" and they are given tasks (such as spamming, dosing, etc..) to do through a "command and control" infrastructure (C2C). Identifying and neutralizing these C2C has been the subject of an arms race between white and black hats for years. In this talk, we will briefly explain how C2C works and how they have been (and still are being) detected. We will then present some very strange

Sep 22 - Sep 29, 2019

Point cloud and volume data reconstruction and visualization

Dr. Ciril Bohak, Postdoctoral Researcher, Faculty of Computer and Information Science, University of Ljubljana, Slovenia

Sep 25, 13:00 - 14:00

B1 L2

point cloud volume data reconstruction visualization real-time rendering web-based framework

Abstract Our ongoing research on the reconstruction of aerial point cloud data includes reconstructing the parts of the data that are not captured by the airplane mounted LiDAR system due to the shadowing effect and lack of reflections. We are developing methods for reconstructing (1) building walls, (2) reconstruction of bridges and underlying terrain; (3) reconstruction of water surfaces of lakes and rivers, and (4) reconstruction of terrain overhangs. In all the cases we are merging the point cloud data with cadaster data or digital terrain model data for filling the gaps in the point cloud
Exploring FPGAs for Virtualized In-Network Acceleration

Dr. Suhaib Fahmy, Associate Professor, Computer Engineering, University of Warwick, UK

Sep 24, 12:00 - 13:00

B2 B3 A0215

FPGA data centers streaming applications accelerators big data cloud computing

Abstract With increasing connectivity and reliance on machine intelligence to process ever-growing amounts of data, the question of how to arrange the required connectivity and computation arises. Traditional cloud computing approaches that centralize compute capability in a data center do not scale well to large scale distributed data sources that must then transmit data over constrained networks. Similarly, computing at the very edge of the network is often constrained by limited computational capacity and a lack of access to shared data. In-network computing has been proposed as a way of

Sep 15 - Sep 22, 2019

Representation Learning for Personalized Recommendation

Prof. Xiangliang Zhang

Sep 16, 12:00 - 13:00

B9 L2 H1 R2322

recommender systems e-commerce industry personalized recommendations learning representations user privacy

Abstract Recommender systems have already been successfully used in e-commerce industry and have been influencing our daily lives. We may all have got (personalized) recommendation of news, music, movies, videos, books, restaurants, hotels, etc. This talk will introduce the recent research in my group about promoting personalized recommendation by learning representations for users/objects to recommend. Examples of applications will be given for recommendation of the next movie to watch, the interesting research papers to read, the useful datasets to explore, and interesting places to visit

Sep 8 - Sep 15, 2019

Distributed Algorithms for Computing a Common Fixed Point of a Group of Nonexpansive Operators

Dr. Xiuxian Li, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore

Sep 9, 12:00 - 13:00

B9 L2 H1

Distributed algorithms nonexpansive operators

This talk is concerned with the problem of seeking a common fixed point for a finite collection of nonexpansive operators over time-varying multi-agent networks in real Hilbert spaces. Each operator is assumed to be only privately and approximately known to each individual agent, and all agents need to cooperate to solve this problem by local communications over time-varying networks. To handle this problem, inspired by the centralized inexact Krasnosel’ski˘ı-Mann (IKM) iteration, two distributed algorithms, called distributed inexact Krasnosel’ski˘ı-Mann (D-IKM) iteration and distributed inexact block-coordinate Krasnosel’ski˘ı-Mann (D-IBKM) iteration, are proposed. It is shown that the two algorithms can converge weakly to a common fixed point of the family of nonexpansive operators.

Sep 1 - Sep 8, 2019

Zero in on computational challenges in Nanopore sequencing

Xin Gao, Program Chair, Computer Science

Sep 2, 12:00 - 13:00

B9 L2 H1

bioinformatics machine learning Nanopore Sequencing DNA sequences

In this talk, I will first give an overview of the research activities in Structural and Functional Bioinformatics Group (http://sfb.kaust.edu.sa). I will then focus on our efforts on developing computational methods to tackle key open problems in Nanopore sequencing. In particular, I will introduce our recent works on developing a collection of computational methods to decode raw electrical current signal sequences into DNA sequences, to simulate raw signals of Nanopore, and to efficiently and accurately align electrical current signal sequences with DNA sequences. Then, I will further introduce their applications in clinical and environmental fields.

Aug 25 - Sep 1, 2019

Knowledge-based methods in biomedicine

Robert Hoehndorf, Associate Professor, Computer Science

Aug 26, 12:00 - 13:00

B9 L2 H1

Biostatistics artificial intelligence gene-disease associations drug targets

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 apply knowledge-based methods for the analysis of biological and biomedical data, in particular identification of gene-disease associations and drug targets.

Jul 14 - Jul 21, 2019

Computational Analysis of Transcriptional Regulation after Single and Multiple Drug Administration.

Trisevgeni Rapakoulia, Ph.D., Computer Science

Jul 17, 10:00 - 12:00

B3 L5 R5209

Transcriptomics RNA genes drug effects breast cancer cell conversion

With the advances in transcriptomic analysis, the monitoring of genome-wide gene expression provides a powerful approach for determining the action of drugs. In this thesis, we analyzed the transcriptional responses of cells treated with drugs either alone or in combinations to explore their effects in two different applications: breast cancer therapy and cell conversion.

Jul 7 - Jul 14, 2019

Publishing Advanced Materials Science – How to maximize your success!

Dr. Jos Lenders, Deputy Editor, Advanced Materials, Wiley

Jul 9, 14:00 - 15:00

B3 L5 R5209

materials science wiley advanced materials

Materials science is a multidisciplinary field of research with many different scientists and engineers having various backgrounds active in it. The literature landscape consequently is populated currently by a wide range of journals which greatly differ in purpose, scope, quality, and readership. Jos Lenders, Deputy Editor of Advanced Materials, Advanced Functional Materials, and Advanced Optical Materials, will track some of the most important developments and trends in the research field and the Advanced journals program. Last year, Advanced Materials reached an Impact Factor of 21.95 and received over 8,300 submissions – and Advanced Functional Materials over 9,200. Only around 15% of all those papers made it to publication in the journal, and this rate is similar for all other Advanced journals. So, what do editors do to select the very best papers, and what can authors do to optimize their chances of having their manuscripts accepted?

Jun 30 - Jul 7, 2019

Abstract Writing

Prof. Liching Chiu, Graduate Program of Teaching Chinese as a Second Language (TCSL), National Taiwan University

Jul 2, 10:00 - 11:00

B3 L5 R5209

Scientific Writing

This series of lectures guide students to the preparation and analysis of a well-organized abstract. We will discuss the proper language (tense, voice, and person) for abstract writing, and learn how to meet the purposes of different abstracts. Finally, students will have a chance to compose and evaluate their writing. Topics: Overview of abstract writing; Conference abstract journal abstract; Organization of an abstract; Language conventions of abstract writing; Disciplinary abstract analysis; Frequent mistakes of abstract writing.