Roy Maxion, Research Professor, Computer Science Department, Carnegie Mellon University
Thursday, November 07, 2019, 09:00
- 10:00
TBD

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.

Registration is mandatory and will determine the time of the workshop (i) 9:00 AM - 10:00 AM or (ii) 4:00 PM - 5:00 PM. To register please click here.

Roy Maxion, Research Professor, Computer Science Department, Carnegie Mellon University
Wednesday, November 06, 2019, 09:00
- 10:00
TBD

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.

Registration is mandatory and will determine the time of the workshop (i) 9:00 AM - 10:00 AM or (ii) 4:00 PM - 5:00 PM. To register please click here.

Prof. William Kleiber, Associate Professor of Applied Mathematics, University of Colorado, USA
Tuesday, November 05, 2019, 14:00
- 15:00
Building 1, Level 4, Room 4102
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.
Dr. Michel Dumontier, Distinguished Professor of Data Science at Maastricht University, The Netherlands
Monday, November 04, 2019, 12:00
- 13:00
Building 9, Level 2, Hall 1, Room 2322
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.
Roy Maxion, Research Professor, Computer Science Department, Carnegie Mellon University
Monday, November 04, 2019, 09:00
- 10:00
TBD

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.

Registration is mandatory and will determine the time of the workshop (i) 9:00 AM - 10:00 AM or (ii) 4:00 PM - 5:00 PM. To register please click here.

Pieter Barendrecht, PhD Student, Computer Science, University of Groningen, The Netherlands
Thursday, October 24, 2019, 14:00
- 15:00
Building 1, Level 4, Room 4214

Abstract

There are many intriguing aspects and applications of splines, i

Dr. Sumayah Alrwais, Assistant Professor, King Saud University, Riyadh, KSA
Monday, October 21, 2019, 12:00
- 13:00
Building 9, Level 2, Hall 1, Room 2322
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.
Prof. Paulo Esteves-Veríssimo, University of Luxembourg, SnT, CritiX
Thursday, October 17, 2019, 11:00
- 12:00
Building 9, Level 3, Room 3223
This talk will try to clarify some misconceptions about what digital health (DH) is, and what it should not be.
Prof. Paulo Esteves-Veríssimo, University of Luxembourg, SnT, CritiX
Wednesday, October 16, 2019, 12:00
- 13:00
Building 9, Level 2, Hall 2, Room 2325
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.
Monday, October 14, 2019, 12:00
- 13:00
Building 9, Level 2, Hall 1, Room 2322
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.
Dr. Timo Ropinski, Professor, Visual Computing, Ulm University, Germany
Monday, October 07, 2019, 12:00
- 13:00
Building 9, Level 2, Hall 1, Room 2322

Abstract

In this talk, I will present our recent advances in deep learnin

Dr. Yunhai Wang, Professor, Computer Science, Shandong University, China
Wednesday, October 02, 2019, 12:00
- 13:00
Building 9, Level 2, Hall 2, Room 2325

Abstract

By providing visual representations of data, visualization can h

Dr. Marc Dacier, Chair of the Digital Security department and a full Professor at Eurecom, France
Monday, September 30, 2019, 12:00
- 13:00
Building 9, Level 2, Hall 1, Room 2322

Abstract

It is well known that malware spreading over the Internet aim at

Dr. Ciril Bohak, Postdoctoral Researcher, Faculty of Computer and Information Science, University of Ljubljana, Slovenia
Wednesday, September 25, 2019, 13:00
- 14:00
Building 1, Level 2, VCC Lecture Room

Abstract

Our ongoing research on the reconstruction of aerial point cloud

Dr. Suhaib Fahmy, Associate Professor, Computer Engineering, University of Warwick, UK
Tuesday, September 24, 2019, 12:00
- 13:00
Auditorium 0215 (between Buildings 2 & 3)

Abstract

With increasing connectivity and reliance on machine intelligenc

Dr. Xiuxian Li, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore
Monday, September 09, 2019, 12:00
- 13:00
Building 9, Level 2, Hall 1
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.
Monday, September 02, 2019, 12:00
- 13:00
Building 9, Level 2, Hall 1
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.
Monday, August 26, 2019, 12:00
- 13:00
Building 9, Level 2, Hall 1
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.
Dr. Jos Lenders, Deputy Editor, Advanced Materials, Wiley
Tuesday, July 09, 2019, 14:00
- 15:00
B3 L5 Room 5209
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?
Prof. Liching Chiu, Graduate Program of Teaching Chinese as a Second Language (TCSL), National Taiwan University
Tuesday, July 02, 2019, 10:00
- 11:00
B3 L5 Room 5209
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.
Tong Zhang, Professor of Computer Science and Mathematics, HKUST
Wednesday, May 29, 2019, 12:00
- 13:00
Building 9, Hall 1
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.
Muhamad Felemban, Assistant Professor, Computer Engineering Department, KFUPM
Monday, May 13, 2019, 12:00
- 13:00
B9 L2 Hall 1
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.
Alp Yurtsever, PhD Candidate, EPFL
Monday, May 06, 2019, 12:00
- 13:00
B9 L2 Hall 2
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.