Events

Jan 4 - Dec 29, 2019

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  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 14 - Dec 8, 2019

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

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

  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 7 - Dec 1, 2019

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  Proximal Splitting Methods for Convex Optimization: An Introduction

  Laurent Condat, Senior Research Scientist, Computer Science

  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.
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  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 30 - Nov 24, 2019

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  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).
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  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 23 - Nov 17, 2019

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  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 16 - Nov 10, 2019

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  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 9 - Nov 3, 2019

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  Reproducibility in Computer Science - Part 3

  Roy Maxion, Research Professor, Computer Science Department, Carnegie Mellon University

  Nov 6, 16:00 - 17:00

  B9 L3 R3223

  reproducibility issues reliability testing Computer science complex models artificial intelligence machine learning scientific enterprise

  Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.
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  Exploranation: A New Science Communication Paradigm

  Prof. Anders Ynnerman, Linköping University, Sweden

  Nov 6, 09:00 - 10:00

  B9 H2 R2325

  Abstract Science communication is facing a paradigm shift, based on the convergence of exploratory and explanatory visualization. In this article, we coin the term exploranation to denote the way in which visualization methods from scientific exploration can be used to communicate results and how methods in explanatory visualization can enrich exploration. Brief Biography Professor Ynnerman is currently the head of the Media and Information Technology division at the Department of Science and Technology (ITN) at Linköping University and is the director of the Norrköping Visualization Center C
• 

  Reproducibility in Computer Science - Part 2

  Roy Maxion, Research Professor, Computer Science Department, Carnegie Mellon University

  Nov 5, 16:00 - 17:00

  B9 L3 R3223

  reproducibility issues reliability testing Computer science complex models artificial intelligence machine learning scientific enterprise

  Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.
• 

  Mixed graphical-basis models for large nonstationary and multivariate spatial data problems

  Dr. William Kleiber, Associate Professor of Applied Mathematics, University of Colorado, USA

  Nov 5, 14:00 - 15:00

  B1 L4 R4102

  multivariate spatial processes statistical climatology and statistics for energy sciences

  In this talk, we explore a graphical model representation for the stochastic coefficients relying on the specification of the sparse precision matrix. Sparsity is encouraged in an L1-penalized likelihood framework. Estimation exploits a majorization-minimization approach. The result is a flexible nonstationary spatial model that is adaptable to very large datasets.
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  Interactive High-Quality Visualization of Large-Scale Particle Data

  Mohamed Ibrahim, Postdoctoral Research Fellow, Computer Science

  Nov 5, 14:00 - 15:00

  B2 L5 R5209

  visualization high-quality rendering large-scale simulation particle data aliasing artifact sampling visible particles

  Large-scale particle data sets, such as those computed in molecular dynamics (MD) simulations, are crucial to investigating important processes in physics and thermodynamics. The simulated atoms are usually visualized as hard spheres with Phong shading, where individual particles and their local density can be perceived well in close-up views. However, for large-scale simulations with 10 million particles or more, the visualization of large fields-of-view usually suffers from strong aliasing artifacts, because the mismatch between data size and output resolution leads to severe under-sampling of the geometry.
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  Prototyping Multiscale Cellular Visualization & Modeling Techniques for Hypothesis Generation, Communication & Learning

  Graham Johnson, Allen Institute for Cell Science, USA

  Nov 5, 09:00 - 10:00

  B9 H2 R2325

  Abstract The visual analysis, assembly, and communication of molecule-scale events have benefited greatly from an explosion of available tools and data that has been provided and evolved by the structural biology community over the past 40 years. Compared to molecular visualization, however, the data, tools, and techniques available for cell-scale visualization remain in relative infancy. In structural biology, the details of a molecule can commonly be summarized with a few megabytes of data, which is heavily constrained by nanoscale physics. Could we ever uncover patterns and rules that will
• 

  Reproducibility in Computer Science - Part 1

  Roy Maxion, Research Professor, Computer Science Department, Carnegie Mellon University

  Nov 4, 16:00 - 17:00

  B9 L3 R3223

  reproducibility issues reliability testing Computer science complex models artificial intelligence machine learning scientific enterprise

  Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.
• 

  Accelerating biomedical discovery with an Internet of FAIR data and services

  Dr. Michel Dumontier, Distinguished Professor of Data Science at Maastricht University, The Netherlands

  Nov 4, 12:00 - 13:00

  B9 L2 H1 R2322

  biomedicine collective knowledge incompatible formats overlapping heterogenous access points computational standards effective representations semantic technologies FAIR automated scientific discovery

  In this talk, I will discuss our work to create computational standards, platforms, and methods to wrangle knowledge into simple, but effective representations based on semantic web technologies that are maximally FAIR - Findable, Accessible, Interoperable, and Reuseable - and to further use these for biomedical knowledge discovery. But only with additional crucial developments will this emerging Internet of FAIR data and services enable automated scientific discovery on a global scale.
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  Machine Learning Models for Context Aware Recommendation Systems

  Basmah Altaf, Ph.D., Computer Science

  Nov 4, 10:00 - 11:00

  B3 L5 R5209

  recommender systems machine learning Deep learning contextual constraints popularity bias effect modeling quality recommendation

  The goal of this thesis is to pave the way towards the next generation of recommendation systems tackling such real-world challenges to improve the user experience while giving good recommendations.
• 

  Envisioning the Molecular Cell: Mesoscale Modeling Comes of Age

  Prof. Arthur Olson, The Scripps Research Institute, USA

  Nov 4, 09:00 - 10:00

  B9 H2 R2325

  Abstract The ability to create structural models of cells and cellular components at the molecular level is being driven by advances ranging from proteomics and expression profiling to ever more powerful imaging approaches. It is being enabled by technology leaps in computation, informatics, and visualization. Our CellPACK program and web-based Mesoscope are tools for generating and curating structural models of cellular environments at molecular and atomic levels. Recently we have implemented a GPU-based implementation of CellPACK that speeds up the process by orders of magnitude, in what we
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  KAUST Cell Visualization Summit 2019

  Nov 4, 08:30 - Nov 6, 10:00

  B9 H2 R2325

  visual computing nanovisualization visualization

  You are invited to attend the KAUST Cell Visualization Summit 2019 organized by the Nanovisualization group from the Visual Computing Center (VCC). The summit will focus on the technological advancement that can lead to an in-silico virtual and visual representation by integrating current knowledge into a unified structural and dynamic model of a living cell. Each keynote speaker will relate to one of the technological aspects (modeling, visualization, and interaction). Keynote speakers Arthur Olson Graham Johnson Anders Ynnerman For more information about the summit, please click here. If you

Oct 26 - Oct 20, 2019

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  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
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  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 19 - Oct 13, 2019

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  Digital Health: promises and pitfalls

  Prof. Paulo Esteves-Veríssimo, University of Luxembourg, SnT, CritiX

  Oct 17, 11:00 - 12:00

  B9 L3 R3223

  global digital health strategy data science genomics owner privacy ICT risks

  This talk will try to clarify some misconceptions about what digital health (DH) is, and what it should not be.
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  Resilient Computing: when Dependability meets Cybersecurity

  Prof. Paulo Esteves-Veríssimo, University of Luxembourg, SnT, CritiX

  Oct 16, 12:00 - 13:00

  B9 L2 H2 R2325

  resilient computing dependability cybersecurity accidental faults dynamic protection sustainability SDN autonomous vehicles digital health blockchain

  Computing and communications infrastructures have become commodities that transact huge quantities of data and are pervasively interconnected, inside countries, and worldwide. Modern societies largely depend on them.
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  Use Mathematics and get a SPARQL Query Engine (almost) for Free! It’s MAGiQ!

  Panagiotis Kalnis, Professor, Computer Science

  Oct 14, 12:00 - 13:00

  B9 L2 H1 R2322

  sparse matrix algebra RDF engine MAGIQ SPARQL queries RDF graphs optimization CPU GPU

  Existing RDF engines are designed for specific hardware architectures; porting to a different architecture (e.g., GPUs) entails enormous implementation effort. We explore sparse matrix algebra as an alternative for designing a portable, scalable and efficient RDF engine.

Oct 12 - Oct 6, 2019

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  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

Oct 5 - Sep 29, 2019

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  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
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  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 28 - Sep 22, 2019

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  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
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  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 21 - Sep 15, 2019

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  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 14 - Sep 8, 2019

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  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 7 - Sep 1, 2019

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  Zero in on computational challenges in Nanopore sequencing

  Xin Gao, Professor, 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 31 - Aug 25, 2019

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  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 20 - Jul 14, 2019

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  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 13 - Jul 7, 2019

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  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?

Jul 6 - Jun 30, 2019

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  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.

Jun 1 - May 26, 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 18 - May 12, 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 11 - May 5, 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.

May 4 - Apr 28, 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 27 - Apr 21, 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.
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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 20 - Apr 14, 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 13 - Apr 7, 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.