Keynote chair: Edson Norberto Cáceres
Abstract: Quantum computing hardware has progressed tremendously in the past decade, and it is now possible to perform real quantum computations through the cloud, for example through services such as the IBM Q Experience. This has created an era of unprecedented innovation and excitement in the field. However, for the next few year, these devices will fall short of full fault-tolerance, which is required for many of the known applications. In this talk, I will talk broadly about near term quantum computers, the roadmap for scaling the hardware, the applications and computational power of these devices, and the software stack, including compilers, that are crucial to making the most out of the available hardware. I will also present some research directions and tools for doing research on quantum computers, notably through the Qiskit software package.
Short Bio: Ali Javadi-Abhari is a researcher at IBM Research and technical lead for quantum software. He has experience in quantum computing architecture and compilation, most notably by being a core architect of the Scaffold compiler and recently the Qiskit software package, which is the industry leading software for quantum computation. He holds a PhD from Princeton.
Keynote chair: Phelipe Navaux
Abstract: Reproducibility of experiments and analysis by others is one of the pillars of modern science. Yet, the description of experimental protocols, software, and analysis is often lacunar and rarely allows a third party to reproduce a study. Such inaccuracies has become more and more problematic and are probably the cause of the increasing number of article withdrawal even in prestigious journals and the realization by both the scientific community and the general public that many research results and studies are actually flawed and misleading. Open science is the umbrella term of the movement that strives to make scientific research, data and dissemination accessible to all levels of an inquiring society. Reproducible research encompasses the technical and social aspects of science allowing and promoting better research practices. In this talk, I will give a broad overview of the challenges at stake and of emerging solutions. I will also particularly discuss the role computer science can play in this topic.
Short Bio: Arnaud Legrand is a senior CNRS researcher at Grenoble University, France since 2004. He obtained his M.S. and Ph.D. from the Ecole Normale Supérieure de Lyon, France in 2000 and 2003, and his Habilitation Thesis in 2015 from Grenoble University, France. His research interests encompass the study of large scale distributed computing infrastructures such as clusters, grids, desktop grids, volunteer computing platforms, clouds, … when used for scientific computing. More specifically, his research focuses on theoretical tools for optimizing the exploitation of such platforms (scheduling techniques, combinatorial optimization and game theory) and on performance evaluation of such systems, in particular through simulation, visualization and statistical analysis. He is one of the leaders of the SimGrid project, an open source simulation toolkit whose specific goal is to facilitate research in the area of parallel and distributed system optimization. In the last four years, he has been actively promoting better experimental practices and scientific methodology of through numerous tutorials and keynotes in conferences and summer schools.
Keynote chair: Alfredo Goldman
Abstract: Write-optimized dictionaries (WODs), such as LSM trees and B^epsilon trees, are increasingly used in databases and file systems. Such data structures support very fast insertions without sacrificing lookup performance. This talk explains how WODs can substantially reduce the I/O cost of many workloads, enabling high-performance applications to scale by orders of magnitude. In contrast, traditional data structures, such as B-trees, are often I/O bound on these workloads. The talk explores write-optimization from the perspective of foundational theory, applications, and parallelization.
Short Bio: Michael A. Bender is a professor of computer science at Stony Brook University. He was Founder and Chief Scientist at Tokutek, Inc, an enterprise database company, which was acquired by Percona in 2015. Bender’s research interests span the areas of data structures and algorithms, I/O-efficient computing, scheduling, and parallel computing. He has coauthored over 130 articles on these and other topics. He has won several awards, including an R&D 100 Award, a Test-of-Time award, two Best Paper Awards, and five awards for graduate and undergraduate teaching. Bender received his B.A. in Applied Mathematics from Harvard University in 1992 and obtained a D.E.A. in Computer Science from the Ecole Normale Superieure de Lyon, France in 1993. He completed a Ph.D. on Scheduling Algorithms from Harvard University in 1998. He has held Visiting Scientist positions at both MIT and King’s College London.