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Building on its capabilities in data-intensive computing and computational science, Brookhaven National Laboratory is embarking upon a major new Computational Science Initiative.

Advances in computational science, data management and analysis have been a key factor in the success of Brookhaven Lab's scientific programs at the Relativistic Heavy Ion Collider (RHIC), the National Synchrotron Light Source (NSLS), the Center for Functional Nanomaterials (CFN), and in biological, atmospheric, and energy systems science, as well as our collaborative participation in international research endeavors, such as the ATLAS experiment at Europe's Large Hadron Collider.

The Computational Science Initiative (CSI) brings together under one umbrella the expertise that has driven this success to foster cross-disciplinary collaborations to address the next generation of scientific challenges posed by facilities such as the new National Synchrotron Light Source II (NSLS II). A particular focus of CSI's work will be the research, development and deployment of novel methods and algorithms for the timely analysis and interpretation of high volume, high velocity, heterogeneous scientific data created by experimental, observational and computational facilities to accelerate and advance scientific discovery. CSI is hereby taking an integrated approach, providing capabilities from leading edge research to multi-disciplinary teams that deliver operational data analysis capabilities to the scientific user communities.

Enabling Capabilities

Computer Science and Mathematics—fundamental research into novel methods and algorithm in support of large-scale, multi-modal, and streaming data analysis. Novel solutions for long term data curation and active reuse. Approaches to enable energy efficient, extreme-scale numerical modeling specifically in computational materials science, chemistry, lattice quantum chromo dynamics and fusion.

The BNL Scientific Data and Computing Center, housing the latest systems in high-performance and data-intensive computing, data storage, and networking, offering everything from novel research platforms to highly reliable production services.

Translational Capabilities

The Computational Science Laboratory, a collaborative space for the development of advanced algorithms and their characterization and optimization, also brings together computer scientists, mathematicians, and leading computational scientists to develop next-generation numerical simulation models

The Center for Data Driven Discovery (C3D), a multi-disciplinary center for the development, deployment, and operation of data-intensive discovery services for science, national security, and industry

A Multi-disciplinary, Collaborative Approach

The CSI philosophy is a multi-disciplinary and collaborative approach to scientific research and development, with research targeted at and informed by the key challenges observed in close interactions with our clients in science, national security agencies, and industry. Our success is measured in equal parts by the advancement we can bring to computer science and mathematics, as well as by the transformational impact we have on our clients’ mission space.

The CSI brings together under one umbrella the expertise that fosters cross-disciplinary collaboration and makes optimal use of existing technologies, while also leading the development of new tools and methods that will benefit science both within and beyond the Laboratory. Key partners include nearby universities such as Columbia, Cornell, New York University, Stony Brook, and Yale, as well as IBM Research.

Strategic Partnerships

Computational scientists at Brookhaven will also seek to establish partnerships with key players in academia and industry (e.g. Stony Brook University’s Institute for Advanced Computational Science, Rensselaer Polytechnic Institute, Oak Ridge National Laboratory, IBM, and Intel). One existing example of a successful partnership is the collaboration of Brookhaven Lab’s high-energy and nuclear physics research groups with IBM that led to the development of the BlueGene supercomputing architecture now used on the world’s most powerful commercially available supercomputers.

More about strategic partnerships CSI All Hands Meeting 2016

Computational Science Initiative first all-hands meeting.

  1. AUG



    Computational Science Initiative Event

    "CSI Seminar: User-Centered Visualization"

    Presented by Ji Hwan Park, Stony Brook University

    11 am, Seminar Room, Bldg. 725

    Tuesday, August 22, 2017, 11:00 am

    Hosted by: 'Kerstin Kleese van Dam'

    A vast amount of information and data is generated every day from various sources relevant to work or to personal lives. There are several types of data such as 2D spatial and non-spatial data, 3D volumetric data, multivariate data, and crowdsourced data. Depending on the type of the input data, different data operations, encoding, and interaction techniques are needed. Visualizing such data is targeted to various users, including both visualization novices and experts. Not every user desires the same level of data analysis or has the same background knowledge and experience in visualization. Depending on the data characteristics and the target users, several different factors such as usage context and aesthetics are considered in visualizing the data effectively. We present user-centric visualization frameworks, each of which was designed to visualize and/or process various data by understanding target users (non-experts, experts) in a different perspective. First, visualizing personal data from various sources such as sensors has different requirements from traditional visualization applications. One of these requirements in personal visualization is aesthetics. We present two frameworks (Ambienizer and Graphoto) using photos to help users understand their personal data with aesthetically pleasing displays. In Ambienzier, information is mapped through image processing techniques, which are applied to a photo. In Graphoto, we generate a photo or adjust an existing one to match a line graph as a visual embellishment of the graph. We also introduce a visual framework to explore multivariate spatio-temporal data for domain experts. In the framework, the input data is encoded into glyphs and several interaction techniques are provided to help experts obtain an overview of all regions and insight into selected local regions. Compressing and visualizing volumetric data generated by certain devices such as CT and MRI scanners on mobile devices is still challenging bec

There are no conferences scheduled at this time.