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Cyber-enabled Discovery and Innovation (CDI)

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Title: Cyber-enabled Discovery and Innovation (CDI)


1
Cyber-enabledDiscovery and Innovation (CDI)
  • Objective
  • Enhance American competitiveness by enabling
    innovation through the use of computational
    thinking

2
Cyber-Enabled Discovery and Innovation
  • Multi-disciplinary research seeking contributions
    to more than one area of science or engineering,
    by innovation in, or innovative use of
    computational thinking
  • Computational thinking refers to computational
  • Concepts
  • Methods
  • Models
  • Algorithms
  • Tools

3
CDI is Unique within NSF
  • five-year initiative
  • all directorates, programmatic offices involved
  • to create revolutionary science and engineering
    research outcomes
  • made possible by innovations and advances in
    computational thinking
  • emphasis on bold, multidisciplinary activities
  • radical, paradigm-changing science and
    engineering outcomes through computational
    thinking

4
CDI Philosophy
  • Business as usual need not apply
  • Projects that make straightforward use of
    existing computational concepts, methods, models,
    algorithms and tools to significantly advance
    only one discipline should be submitted to an
    appropriate program in that field instead of to
    CDI.  
  • No place for incremental research
  • Untraditional approaches and collaborations
    welcome

5
NSF Review Criteria
  • Intellectual Merit
  • Broader Impacts
  • New on Transformative Research to what extent
    does the proposed activity suggest and explore
    creative, original, or potentially transformative
    concepts?

6
Additional CDI Review Criteria
  • The proposal should define a bold
    multidisciplinary research agenda that, through
    computational thinking, promises
    paradigm-shifting outcomes in more than one field
    of science and engineering.
  • The proposal should provide a clear and
    compelling rationale that describes how
    innovations in, and/or innovative use of,
    computational thinking will lead to the desired
    project outcomes. 
  • The proposal should draw on productive
    intellectual partnerships that capitalize upon
    knowledge and expertise synergies in multiple
    fields or sub-fields in science or engineering
    and/or in multiple types of organizations.
  • potential for extraordinary outcomes, such as,
  • revolutionizing entire disciplines,
  • creating entirely new fields, or
  • disrupting accepted theories and perspectives
  • as a result of taking a fresh,
    multi-disciplinary approach. 
  • Special emphasis will be placed on proposals that
    promise to enhance competitiveness, innovation,
    or safety and security in the United States.

7
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8
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9
Long-term Funding for Cyber-enabled Discovery and
Innovation
  • All NSF directorates are participating in this
    activity (subject to budget approval) estimated
    750M investment in 5 years

RequestFY 2008 FY 2009 FY 2010 FY2011 FY 2012
52M (26M in the solicitation) 100M 150M 200M 250M
10
Three CDI Themes
  • CDI seeks transformative research in the
    following general themes, via innovations in,
    and/or innovative use of, computational thinking
  •  
  • From Data to Knowledge enhancing human cognition
    and generating new knowledge from a wealth of
    heterogeneous digital data
  • Understanding Complexity in Natural, Built, and
    Social Systems deriving fundamental insights on
    systems comprising multiple interacting elements
     and
  • Building Virtual Organizations enhancing
    discovery and innovation by bringing people and
    resources together across institutional,
    geographical and cultural boundaries.   

11
From Data to Knowledge
  • Knowledge extraction, noise, statistics
  • Modeling, data assimilation, inverse problems
  • Validation model/cyber/domain feedbacks
  • Algorithms for analysis of large data sets,
    dimension reduction
  • Visualization, pattern recognition

12
Understanding Complexity in Natural, Built, and
Social Systems
  • Identifying general principles and laws that
    characterize complexity and capture the essence
    of complex systems
  • Attaining the breakthroughs, to overcome these
    challenges, requires transformative ideas in the
    following areas
  • Simulation and Computational Experiments
  • Methods, Algorithms, and Tools
  • Nonlinear couplings across multiple scales

13
Virtual Organizations (VOs)
  • Design, development, and assessment of VOs
  • Bringing domain needs together with algorithm
    development, systems operations, organizational
    studies, social computing, and interactive design
  • Flexible boundaries, memberships, and lifecycles,
    tailored to particular research problems, users
    and learner needs or tasks of any community,
    providing opportunities for
  • Remote access
  • Collaboration
  • Education and training

14
Types of Projects
  • CDI defines research modalities
  • Project size not measured by
  • Projects classified by magnitude of effort
  • Three types are defined Types I, II, and III
  • Type III, center-scale efforts, will not be
    supported in the first year of CDI

15
Type I Projects
  • focused aims that tackle discrete, high-risk
    problems that, once resolved, may enable
    transformative breakthroughs in multiple fields
    of science or engineering through computational
    thinking
  • research and education efforts roughly comparable
    to that of up to two investigators with summer
    support, two graduate students, and their
    research needs (e.g., materials, supplies,
    travel), for a duration of three years

16
Type II projects
  • multiple major aims that tackle complementary
    facets of complex solutions for advancing
    multiple fields of science and engineering
    through computational thinking.
  • several intellectual leaders, multidisciplinary
    teams
  • significant education component
  • likely to be distributed collaborative projects
    with more extensive project coordination needs
  • greater effort than in Type I, and, for example,
    roughly comparable to that of up to three
    investigators with summer support, three graduate
    students, one or two other senior personnel
    (post-doctoral researchers, staff), and their
    research needs (e.g., materials, supplies,
    travel), for a duration of four years

17
Type III Projects
  • collaborative research, potentially distributed
    across several institutions
  • may involve center-type activities, demanding
    substantial coordination efforts
  • greater effort than in Type II in terms of scope
    and in the order of magnitude of expected
    outcomes
  • Type III projects will not be supported in FY08,
    but in the future years, subject to the
    availability of funds

18
Broadening Participation
  • diversity of sciences and engineering, academic
    departments
  • underrepresented minorities in STEM
  • collaborations with industry in order to match
  • scientific insights with
  • technical insights

19
International Collaborations
  • involve true intellectual partnership in which
    successful outcomes depend on the unique
    contributions of all partners, U.S. and foreign
  • engage junior researchers and students in the
    collaboration, taking advantage of cyber
    environments to prepare a globally-engaged
    workforce
  • in conducting research in all of the major
    components of the CDI
  • create more systematic knowledge about the
    intertwined social and technical issues of
    effective VOs, changing both the practice and the
    outcomes of science and engineering research and
    education.

NSF awards are, in principle, limited to support
of the U.S. side of an international
collaboration. In almost all cases, international
partners should obtain their own funding for
participation.
20
Examples
  • Cyber-enabled discovery and innovation in any
    field of science or engineering is appropriate
    for the CDI program.
  • Examples illustrate desired outcomes of potential
    successful CDI projects.
  • Note included for purposes of illustration
    only it is neither exhaustive, nor indicative of
    preference regarding research areas.
  • The listed examples represent contributions in
    one or more CDI themes, via multidisciplinary
    approaches that hinge on innovations in, or
    innovative use of, computational concepts,
    methods, models, algorithms, and tools.
  • http//www.nsf.gov/crssprgm/cdi/

21
Key Dates
  • Letters of Intent (required) due Nov 30, 07
  • Preliminary Proposals due
  • Jan 8, 08
  • Full proposals due
  • April 29, 08
  • Full proposals by invitation only!
  • Awards no later than October 2008

22
More Information on CDI
  • Contact members of CDIIT.
  • Contact the CDI Co-chairs Sirin Tekinay (CISE),
    Tom Russell (MPS), Eduardo Misawa(ENG) or members
    of the team listed in the solicitation
  • cdi_at_nsf.gov (703) 292-8080
  • http//www.nsf.gov/crssprgm/cdi/

23
Questions? Comments?
24
Example
  • Systems with many interacting parts on multiple
    scales require major advances in modeling to
    limit the interactions to the essential ones, in
    algorithms to solve the models efficiently and
    accurately, in software implementation of the
    algorithms, and in analysis of the massive
    generated data if the challenges of length and
    time scales are to be overcome.
  • Examples. Researchers have visualized the
    changing atomic structure of a simple,
    plant-infecting virus and revealed key physical
    properties by calculating how each of the virus'
    one million atoms interacted with each other
    every femtosecond. A few additional examples of
    other domains with analogous challenges in
    chemistry, in analyses of molecular structure and
    the dynamics of excited states in astronomy, in
    modeling of galactic interactions and
    condensed-matter physics and materials
    engineering, in custom design and synthesis of
    tailor-made molecules for new materials such as
    fibers, coatings, ceramics, and electronic
    materials.
  • Simulation results may in turn suggest that the
    theory underlying a model requires revision the
    Einstein equations of general relativity may be
    an example. The challenges are compounded by
    stochastic behavior, which may arise because of
    the fundamental nature of a system at some scale
    (for example, a quantum mechanical model), data
    uncertainties or noise, or incorporation of
    effective small-scale phenomena into large-scale
    models.
  • Themes Complexity, Data to Knowledge.
  • Domains all fields of science and engineering.

25
Example
  • The semiconductors and magnetic memory materials
    from which todays computers are built, and the
    nanoscale physical processes used to fabricate
    them, are the results of fundamental research in
    the physical sciences. This research has fueled
    Moores law. Moreover, new materials, like
    photonic bandgap materials and higher density
    memories, hold great promise. Even more striking
    are the completely new approaches to computer
    design on the horizon, for example, quantum
    computing, molecular computing, and spintronics.
    These offer the possibility of revolutionary
    advances, and constitute the best hope for
    continuing, or accelerating, Moores law of
    hardware into the future. Novel large-scale
    simulations based on advances in computational
    models, methods, and algorithms play a key role
    in implementing these approaches, through
    fundamental understanding of the nanoscale and of
    the emergence of macroscopic properties. This
    creates a virtuous circle cyber-enabled
    discovery that, in turn, enables cyber.
  • Themes Complexity, Data to Knowledge.
  • Domains physical sciences, engineering,
    computer science, mathematical sciences.

26
Example
  • Physical, electrical and cyber infrastructures,
    such as drinking water and wastewater treatment
    facilities electrical energy generation,
    transmission, and distribution systems chemical
    production and distribution systems
    communications networks transportation systems
    agriculture and food production and public
    health networks, are critical to the nation's
    welfare, security, and ability to compete in a
    global economy. Research to date has separately
    considered the issues of resiliency,
    sustainability, and interdependence. Complexity
    issues include cyber-enabled methodologies to
    analyze and forecast how infrastructures grow,
    self-organize, interact, renew, and operate as
    interdependent resilient and sustainable systems.
    Interdisciplinary, geographically diverse,
    virtually connected, nonlinear dynamic networks
    that predict and control changes across multiple
    infrastructures, length and time scales, with
    fidelity and the ability to handle huge volumes
    of data could involve a large number of
    disciplines and organizations.
  • CDI themes Data to Knowledge, Complexity,
    Virtual Organizations.
  • Domains engineering, computer science, social
    sciences, physical sciences, biological sciences.

27
Example
  • Manufacturing in the U.S. is affected by
    globalization, environmental and safety
    restrictions, and competition from an improved
    foreign scientific workforce. Some recent
    developments of interest to researchers in
    engineering include just-in-time production,
    assembly and/or delivery, shortened product life
    cycles, and demand for zero-tolerance operational
    incidents. Simultaneously, analogies from the
    life sciences are motivating the design of
    self-assembling and self-repairing materials.
    This could lead to the design and manufacture of
    new materials and devices, such as artificial
    skin and self-optimizing fuel cells. Interaction
    between researchers in these and other
    potentially relevant fields would benefit from
    novel mathematical and computational thinking,
    from complexity analysis, and from geographically
    disparate virtual organizations. Combining
    research in multi-scale dynamic modeling and
    simulation for synthesis, design, prediction and
    control large scale optimization product
    allocation data interoperability sensor
    networks organic and inorganic chemistry
    materials synthesis and device fabrication are
    relevant CDI topics. Research and education
    projects in some of these areas are ideally
    suited for industry/academic collaborations,
    which might be, but are not limited to, GOALI
    projects (http//www.nsf.gov/pubs/2007/nsf07522/ns
    f07522.htm).
  • CDI themes Complexity, Virtual Organizations.
  • Domains engineering, materials science,
    mathematics, chemistry, biological sciences,
    computer science, education.

28
Example
  • Living systems function through the encoding,
    exchange, and processing of information. The
    discovery of genetic code and the ability to
    capture it in digital form has transformed
    biology by catalyzing the creation of databases
    and applications for understanding the meaning of
    genetic code, to compare it and to predict its
    function. New research seeking similar
    understanding of the communication flowing at
    other systemic levels such as chemical pathways,
    cell signaling, mate selection, or ecosystem
    services feedback poses a challenge to
    information science to develop more advanced
    cyber tools for digitally representing and
    manipulating the increasingly complex data
    structures found in natural and social systems.
  • Themes Complexity, Data to Knowledge.
  • Domains information science, biological
    sciences, social sciences, physical sciences,
    mathematical sciences.

29
Example
  • Theoretical foundations offering tools for
    understanding, modeling, and analysis of
    large-scale, complex, heterogeneous networks of
    signaling, signal processing, computing, decision
    making, communicating, sensing, controlling nodes
    with multi-scale interactions need to be
    developed. Network science, drawing from
    economic theory, multi-scale analysis, and
    network information theory, is currently in its
    infancy. The Internet, with its billions of
    interfaces, and mobile, wireless devices,
    spanning from personal area networks to satellite
    communications, cuts across man-made, social, and
    natural systems. Specifically, communication
    networks, wired and wireless, span the globe and
    have become an indispensable tool for modern
    society, including science and engineering. New
    models and analysis tools are needed to
    understand spatial and temporal behavior of
    interactions in the electromagnetic medium, or in
    the routing and resource allocation level.
    Another area is biological networks, whose
    understanding remains rudimentary. New,
    realistic models of signal transduction pathways,
    incorporating interactions with other pathways
    and behavior under prolonged stimulus or lack
    thereof, are needed. Other topics involving
    complex coupled networks include communication
    systems, the human brain, and social networks.
    All of these cases call for better understanding
    of network structure, function, and evolution.
  • This example spans all three CDI themes massive
    sets of network data should produce knowledge of
    patterns across many temporal and spatial scales
    networks, man-made, social, or natural,
    embodiments of complex systems of interaction
    finally, VOs themselves consist of networks at
    different scales of interaction and, in turn,
    study networks.
  • Domains computer science, engineering,
    biological sciences, social sciences, physical
    sciences, mathematical sciences.

30
Example
  • Develop techniques to forecast critical events in
    geophysics and predict their impact on society.
    Central is the ability to adaptively configure
    the resolution of numerical models and real-time
    observing networks to zoom in and follow
    important dynamic features (ocean eddies,
    earthquakes, volcanic eruptions, landslides,
    storms, flash floods, hurricanes, algal blooms,
    etc.) and to predict their impact on human
    society, infrastructure, and ecosystem services.
    Capabilities such as the tracking of hurricanes
    necessarily involve uncertainty, due to the
    intrinsic nature of the dynamics, limited
    understanding of features such as the coupling
    between the ocean and the atmosphere, and
    constraints on resolution of practical
    computations quantifying and managing the
    uncertainty is of critical importance. Themes
    Complexity, Data to Knowledge. Domains
    geosciences, ecology, mathematical sciences,
    social sciences, engineering.Model, simulate,
    analyze, and validate complex systems with large
    data sets. Extraction of significant features
    and patterns from high-dimensional data, which
    can be noisy, is crucial in a great variety of
    settings. Examples include the Earth system
    (geosciences), gravitational waves (physics),
    galaxy formation (astronomy), highly complex
    dynamical systems simulation, health monitoring,
    prediction, design and control (engineering),
    communication and network control and
    optimization (information technology), human and
    social behavior simulation (social sciences),
    disaster response simulation and anti-terrorism
    preparation (homeland defense), design of smart
    systems for mitigation of exogenous threats using
    autonomic response (homeland security),
    predictive understanding of ecological and
    evolutionary processes at multiple scales
    (biological sciences), software development
    (information technology), and risk analysis. A
    key issue for some systems is understanding
    whether they will enter a fundamentally different
    mode of behavior when an input crosses a tipping
    point examples include the Earths climate (due
    to atmospheric carbon dioxide) and the U.S.
    economy (due to the federal funds interest rate).
  • Themes Data to Knowledge, Complexity.
  • Domains all fields of science and engineering.

31
Example
  • As hypotheses in the social, behavioral, and
    economic sciences have become more sophisticated,
    so have basic data needs. Merging biomedical
    data with survey and administrative data is a
    relatively untested area, but it is becoming more
    crucial for understanding hypotheses emerging
    from behavioral economics and other fields.
    Understanding human/environmental interactions
    requires the merging of data across multiple
    scales, such as remote sensing data, surveys of
    households, and ecological data. The creation
    and use of these sophisticated data sets raises
    many issues. For example, more and more of our
    data are geocoded. This raises serious questions
    regarding data confidentiality. How do
    researchers maintain the usability of data while
    protecting confidentiality when the identifying
    variables also are variables in the analysis?
    Research in this area lends itself to potential
    advances in the social, behavioral, and economic
    sciences, computer science, and the mathematical
    sciences.
  • CDI Theme From Data to Knowledge.

32
Example
  • The introduction of cyberinfrastructure into
    formal and informal learning environments is
    already beginning to provide learners at all
    levels (K to grey) with the skills and literacies
    needed to operate effectively in those
    environments. In order to take full advantage of
    the opportunity to learn in these environments,
    their design must be based on our best
    understanding of human cognitive and interactive
    styles and capacities. That understanding, in
    turn, can be sharpened considerably by the data
    now becoming available from observations of
    students and teachers interacting with each other
    and with the cyber-environment.
  • CDI themes Data to Knowledge, Virtual
    Organizations.
  • Domains human-computer interaction, cognitive
    science, developmental and learning sciences.

33
Example
  • High school teachers and students can explore
    science through a virtual laboratory that gives
    them access to sophisticated modeling and
    simulation systems. Impacts of global phenomena
    such as climate warming, and local ones such as
    earthquakes in susceptible communities, can be
    investigated. They can also participate in
    simultaneous virtual experiments with classes at
    remote locations, underscoring how actions in one
    region impact another. This innovative approach
    to science education depends on breakthroughs in
    secure virtual organizations for collaboration
    and shared control, models and simulations of
    natural and built complex systems that are
    accessible in real time and can be used and
    understood by students, and interdisciplinary
    approaches to complexity that help the public
    understand the relevance of science to daily
    life.
  • Themes Virtual Organizations, Complexity.
  • Domains education.
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