Cyber-Enabled Discovery and Innovation (CDI)

							     Cyber-enabled
Discovery and Innovation
         (CDI)
                     Objective:
   Enhance American competitiveness by enabling
innovation through the use of computational thinking
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
 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
            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
           NSF Review Criteria

Intellectual Merit
Broader Impacts

Additional Review Criteria:
   Transformative Research:
       To what extent does the proposed activity suggest and
        explore creative, original, or potentially transformative
        concepts?
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.
  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:
Request     FY 2009 FY 2010      FY2011    FY 2012
FY 2008

$52M          $100M   $150M      $200M     $250M
($26M in
solicitation)
            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.
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
  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
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
       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
              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
              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
             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
 Broadening Participation

Diversity of sciences
and engineering,
academic departments
Underrepresented
minorities in STEM
disciplines
Collaborations with
industry in order to
match scientific insights
with technical insights
   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     NSF awards are, in
environments to prepare a globally-engaged       principle, limited to
workforce                                        support of the U.S.
In conducting research in all of the major       side of an
components of the CDI                            international
                                                 collaboration. In
Create more systematic knowledge about           almost all cases,
the intertwined social and technical issues of   international
effective VOs, changing both the practice        partners
and the outcomes of science and                  should obtain their
engineering research and education.              own funding for
                                                 participation.
                 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: Examples are included for purposes of
illustration only; the list 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/
                      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
More Information on CDI:

Contact members of CDIIT.
   Contact the CDI Co-chairs
      Sirin Tekinay (CISE)
      Tom Russell (MPS)
      Eduardo Misawa (ENG)
      or other members of the team listed in the
       solicitation
cdi@nsf.gov ; (703) 292-8080
http://www.nsf.gov/crssprgm/cdi/
Questions? Comments?
Example 1: Multiscale Systems
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.
            Example 2: Materials
The semiconductors and magnetic memory materials from which
today’s 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 Moore’s 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, Moore’s 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.
     Example 3: Infrastructure
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.
Example 4: Manufacturing
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/nsf07522.htm).
CDI themes: Complexity, Virtual Organizations.
Domains: Engineering, materials science, mathematics, chemistry,
biological sciences, computer science, education.
        Example 5: Genetics
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.
       Example 6: Networks
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.
Example 6: Networks (con’t)
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.
      Example 7: Geophysics
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.
Themes: Data to Knowledge, Complexity.
Domains: Geosciences, ecology, mathematical sciences, social
sciences, engineering.
Example 7: Geophysics (con’t)
•   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 Earth’s climate (due to atmospheric carbon
      dioxide) and the U.S. economy (due to the federal funds interest rate).
Example 8: Social, Behavior &
    Economic Sciences
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.
       Example 9: Learning
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.
Example 10: Virtual Laboratories
  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 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