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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
  1. JAN



    Computational Science Initiative Event

    "Materials Design and Discovery in the Era of Watson: Challenges and Opportunities in Data Science"

    Presented by Venkat Venkatasubramanian, Department of Chemical Engineering Columbia University

    2 pm, Seminar Room, Bldg. 725

    Friday, January 19, 2018, 2:00 pm

    "Who is Bram Stoker?" – With this $1 million prize winning final question in the game show Jeopardy, IBM's Watson supercomputer using DeepQA technology ushered in a new era in artificial intelligence and informatics. Welcome to the era of deep neural networks and self-driving cars! This has far reaching implications for knowledge modeling and reasoning in a number of fields including materials engineering. Designing new materials and formulations with desired properties is an important and difficult problem, encompassing a wide variety of products in the specialty chemicals and pharmaceuticals industries. Traditional trial-and-error design approaches are laborious and expensive, and cause delays time-to-market as well as miss some potential solutions. Furthermore, the growing avalanche of high throughput experimentation data has created both an opportunity, and a major modeling and informatics challenge, for material design and discovery. Such a data deluge is coming from smart sensors in process plants, ab initio quantum calculations, molecular dynamics simulations, and so on. We are moving from an era of limited data obtained through time consuming experiments and simulations to one of a tsunami enabled by high throughput experiments and TeraGrid computing environments— it's a dramatic transition from a "data poor" to a "data rich" era. A systematic way to convert raw data into information and first-principles knowledge that can be used ‎for real-time decision making is very much lacking. A new paradigm is needed that increases the idea flow, broadens the search horizon, and archives the knowledge from today's successes to accelerate those of tomorrow. Data science, loosely defined as a body of knowledge comprising of machine learning, natural language processing, databases and informatics, will play a crucial role in materials design and discovery, process development and commercial scale manufa

  1. FEB



    PAM 2018: Performance Analysis and Modeling Workshop

    February 14-15, 2018

  2. FEB



    Intel Knights Landing (KNL) Hackathon 2018

    February 26 - March 2, 2018