Cyber-enabled Discovery and Innovation (CDI)

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Cyber-enabledDiscovery and Innovation (CDI)

• Objective
• Enhance American competitiveness by enabling
  innovation through the use of computational
  thinking

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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

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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

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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

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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?

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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.

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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
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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.   

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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

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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

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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

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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

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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

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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

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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

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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

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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.
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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/

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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

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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 [phone]
• http//www.nsf.gov/crssprgm/cdi/

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Questions? Comments?
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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.