Title: Cyber-enabled Discovery and Innovation (CDI)
1Cyber-enabledDiscovery and Innovation (CDI)
- Objective
- Enhance American competitiveness by enabling
innovation through the use of computational
thinking
2Cyber-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
3CDI 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
4CDI 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
5NSF 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?
6Additional 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.
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9Long-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
10Three 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.
11From 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
12Understanding 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
13Virtual 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
14Types 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
15Type 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
16Type 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
17Type 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
18Broadening Participation
- diversity of sciences and engineering, academic
departments - underrepresented minorities in STEM
- collaborations with industry in order to match
- scientific insights with
- technical insights
19International 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.
20Examples
- 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/
21Key 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
22More 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/
23Questions? Comments?
24Example
- 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.
25Example
- 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.
26Example
- 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.
27Example
- 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.
28Example
- 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.
29Example
- 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.
30Example
- 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.
31Example
- 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.
32Example
- 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.
33Example
- 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.