Brookhaven Lab is establishing itself as a global leader in tackling the challenges of Big Data, building on our existing expertise, capabilities, and investments in computational science and data management, and enabling scientific discovery in large-scale experimental environments.
Computer Science and Mathematics
Developing advanced methods for large scale, multi-modal, and streaming data analysis
Computing for National Security
Understanding how architectures and systems can tackle the challenges posed by data-intensive computing
Scientific Data and Computing Center
Expertise in operating large-scale computational science, data management, and analysis infrastructure
Center for Data-Driven Discovery
Driving research and development of new methods and tools to extract knowledge from Big Data
Computational Science Laboratory
A collaborative laboratory for advanced algorithm development and optimization
About Us
Brookhaven Lab's Computational Science Initiative, known as CSI, excels at integrating computer science, applied mathematics, and computational science with broad domain expertise to tackle problems and advance knowledge impacting scientific discovery.
CSI has long focused on timely analysis and interpretation of high-volume, high-velocity heterogeneous data, providing solutions for the national and international scientific community. These efforts now are being augmented by CSI's growing high-performance computing capabilities.
Philosophy
CSI takes a multidisciplinary, collaborative approach to its research, targeting challenges in cooperation with fellow researchers in science, national security, and industry—both at home and abroad.
We measure success by the transformational impacts we bring to how research is conducted and the advances we introduce to fundamental disciplines such as mathematics, data analysis, and computer science.
Computational Science News
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FEB
6
Thursday
CFN Colloquium
"Progress in Trapped Ion Quantum Computing"
Presented by Prof. Jungsang Kim, Duke University
4 pm, CFN, Bldg 735, Seminar Room, 2nd Floor
Thursday, February 6, 2020, 4:00 pm
Hosted by: Chang-Yong Nam
Trapped ions are one of the leading candidates for realizing practically useful quantum computers. Introduction of advanced integration technologies to this traditional atomic physics research has provided an opportunity to convert a complex atomic physics experiment into a stand-alone programmable quantum computer. In this presentation, I will discuss the new enabling technologies that changes the perception of a trapped ion system as a scalable quantum computer, and the concrete progress made to date in this endeavor. Short Biography: Prof. Jungsang Kim's current research focus is practical realization of quantum computers. He received his B.S. degree from Seoul National University (1992) and his Ph.D. from Stanford University (1999), both in Physics. He worked at Bell Laboratories for five years, working on developing cutting-edge optical and wireless communication systems. He joined the Electrical and Computer Engineering department at Duke University in 2004, where he has worked on trapped ion quantum computing, high pixel-count imaging systems, and novel quantum device research. He has been serving as a principal investigator for many collaborative research projects on quantum computing and communications. In 2015, he co-founded IonQ, focusing on commercial development of ion trap based quantum computer.
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APR
8
Wednesday
CSI Q Seminar
"Quantum-driven classical optimization"
Presented by Helmut Katzgraber, Microsoft Research
1:30 pm, Training Room, Bldg 725
Wednesday, April 8, 2020, 1:30 pm
Hosted by: Layla Hormozi
The advent of the first useful quantum computing devices has resulted in an arms race with classical algorithms on traditional computing hardware. While near-term quantum devices might revolutionize, e.g., optimization and quantum chemistry, tackling many applications will directly depend on either hybrid or purely classical computing techniques. Inspired by these recent exciting developments, a variety of new classical algorithms have emerged. In this talk an overview on quantum inspired methods and their applications is given.
There are no conferences scheduled at this time.
