Roy Maxion, Research Professor, Computer Science Department, Carnegie Mellon University
Wednesday, November 06, 2019, 16:00
- 17:00
Building 9, Level 3, Room 3223

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.

Roy Maxion, Research Professor, Computer Science Department, Carnegie Mellon University
Tuesday, November 05, 2019, 16:00
- 17:00
Building 9, Level 3, Room 3223

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.

Tuesday, November 05, 2019, 14:00
- 15:00
Building 2, Level 5, Room 5209
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.
Dr. 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.
Roy Maxion, Research Professor, Computer Science Department, Carnegie Mellon University
Monday, November 04, 2019, 16:00
- 17:00
Building 9, Level 3, Room 3223

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.

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.
Monday, November 04, 2019, 10:00
- 11:00
Building 3, Level 5 , Room 5209
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.
Sunday, November 03, 2019, 12:00
- 13:00
Building 9, Level 2, Hall 1, Room 2322
Hakan Bagci is an Associate Professor of Electrical Engineering (EE) and Principal Investigator of the Computational Electromagnetics Laboratory (CEML).  His scientific contribution are in advancing high-speed and long-distance communication, energy transfer, and medical imaging. Bagci’s research interests are in various aspects of applied and theoretical computational electromagnetics with emphasis on Time-domain integral-equations and their fast marching-on-in-time-based solutions and solvers to the characterization of wave interactions on complex integrated and electrically large system of photonics and optics. 
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

Chung-An Shen, Associate Professor, National Taiwan University of Science and Technology
Tuesday, October 22, 2019, 12:00
- 13:00
B9 L2 Hall 2

Abstract

The 5th Generation (5G) wireless communication provides considerably enhanced user experiences and offers possibilitie

Monday, October 21, 2019, 14:30
- 15:30
B3 L5 Room 5220
Compact, autonomous computing systems with integrated transducers are imperative to deliver advances in healthcare, navigation, livestock monitoring, point of care diagnostics, remote sensing, internet-of-things applications, smart cities etc. Reflecting this need, there has been sustained growth in the market for transducers. Polymer based transducers, which meld highly desirable properties such as low cost, light weight, high manufacturability, biocompatibility and flexibility, are quite attractive. Doping polymers with magnetic materials results in the formation of magnetic composite polymers, enhancing the attractive traits of polymer transducers with magnetic properties. This dissertation is dedicated to the development of magnetic polymer transducers, which are suitable for energy harvesting and saline fluid transduction.
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.
Sunday, October 20, 2019, 12:00
- 13:00
Building 9, Level 2, Hall 1, Room 2322
Semiconductors are pervasive in consumer electronics and optoelectronics, and the related optical devices are deemed disruptive that Nobel Prize in Physics in 2014 was awarded to the inventors of blue light-emitting diodes (LEDs), which “has enabled bright and energy-saving white light sources”. While AlInGaN-based lasers and LEDs, and silicon-based photodetectors are currently matured, unconventional usage based on the materials has demonstrated their further potential, including solar-hydrogen generation, indoor-horticulture, and high-speed communication.
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