Events

Nov 3 - Nov 9, 2019

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.
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.
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.
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
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 20 - Oct 26, 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 19, 2019

Oct 6 - Oct 12, 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 5, 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 28, 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 21, 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 14, 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 7, 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 - Aug 31, 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 20, 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 13, 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 6, 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.

May 26 - Jun 1, 2019

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 18, 2019

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 11, 2019

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 4, 2019

Apr 21 - Apr 27, 2019

Apr 14 - Apr 20, 2019

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 13, 2019

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 6, 2019

Mar 24 - Mar 30, 2019

Mar 17 - Mar 23, 2019

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 9, 2019

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 2, 2019

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 9, 2019

Nov 25 - Dec 1, 2018

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.