1. Computational Thinking: Two and a Half Years Later
Jeannette M. Wing
President’s Professor of Computer Science Carnegie Mellon University and Assistant Director Computer and Information Science and Engineering Directorate National Science Foundation
 2008 Jeannette M. Wing

2. Outline Computational Thinking Research and Education Implications
A Vision for our Field
The Two A’s to CT
Research and Education Implications
Two and a Half Years Later…
External Response and Impact
Reality Check
CT: 1.5 Years Later
Jeannette M. Wing

3. My Grand Vision for the Field
Computational thinking will be a fundamental skill used by everyone in the world by the middle of the 21st Century.
Just like reading, writing, and arithmetic.
Imagine every child knowing how to think like a computer scientist!
Incestuous: Computing and computers will enable the spread of computational thinking.
In research: scientists, engineers, …, historians, artists
In education: K-12 students and teachers, undergrads, …
Thinking computationally not programming
Fundamental skill not rote skill
Computing not computers: pervasive computing/computers is passe: 20th Century!
“used by” ambitious. Certainly needed to function in modern society
In research: long-term; in education: nearer-term (outreach, diversity, changing society’s view of our field)
J.M. Wing, “Computational Thinking,” CACM Viewpoint, March 2006, pp
CT: 1.5 Years Later
Jeannette M. Wing

4. Examples of Computational Thinking
How difficult is this problem and how best can I solve it?
Theoretical computer science gives precise meaning to these and related questions and their answers.
C.T. is thinking recursively.
C.T. is reformulating a seemingly difficult problem into one which we know how to solve.
Reduction, embedding, transformation, simulation
C.T. is choosing an appropriate representation or modeling the relevant aspects of a problem to make it tractable.
C.T. is interpreting code as data and data as code.
C.T. is using abstraction and decomposition in tackling a large complex task.
C.T. is judging a system’s design for its simplicity and elegance.
C.T. is type checking, as a generalization of dimensional analysis.
C.T. is prevention, detection, and recovery from worst-case scenarios through redundancy, damage containment, and error correction.
C.T. is modularizing something in anticipation of multiple users and prefetching and caching in anticipation of future use.
C.T. is calling gridlock deadlock and avoiding race conditions when synchronizing meetings.
C.T. is using the difficulty of solving hard AI problems to foil computing agents.
C.T. is taking an approach to solving problems, designing systems, and understanding human behavior that draws on concepts fundamental to computer science.
Please tell me your favorite examples of computational thinking!
CT: 1.5 Years Later
Jeannette M. Wing

5. The First A to Computational Thinking
Abstractions are our “mental” tools
The abstraction process includes
Choosing the right abstractions
Operating simultaneously at multiple layers of abstraction
Defining the relationships the between layers
CT: 1.5 Years Later
Jeannette M. Wing

6. The Second A to Computational Thinking
The power of our “mental” tools is amplified by our “metal” tools.
Automation is mechanizing our abstractions, abstraction layers, and their relationships
Mechanization is possible due to precise and exacting notations and models
There is some “computer” below (human or machine, virtual or physical)
CT: 1.5 Years Later
Jeannette M. Wing

7. Two A’s to C.T. Combined
Computing is the automation of our abstractions
They give us the audacity and ability to scale.
Computational thinking
choosing the right abstractions, etc.
choosing the right “computer” for the task
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Jeannette M. Wing

8. Research Implications
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9. CT in Other Sciences, Math, and Engineering
Biology - Shotgun algorithm expedites sequencing of human genome DNA sequences are strings in a language Protein structures can be modeled as knots Protein kinetics can be modeled as computational processes Cells as a self-regulatory system are like electronic circuits
Credit: Wikipedia
Brain Science - Modeling the brain as a computer - Vision as a feedback loop - Analyzing fMRI data with machine learning
Credit: LiveScience
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10. CT in Other Sciences, Math, and Engineering
Credit: University of Minnesota
Chemistry [Madden, Fellow of Royal Society of Edinburgh]
Atomistic calculations are used to explore chemical phenomena
Optimization and searching algorithms identify best chemicals for improving reaction conditions to improve yields
Geology - Modeling the earth’s surface to the sun, from the inner core to the surface
- Abstraction boundaries and hierarchies of complexity model the earth and our atmosphere
Credit: NASA
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11. CT in Other Sciences, Math, and Engineering
Astronomy
- Sloan Digital Sky Server brings a telescope to every child - KD-trees help astronomers analyze very large multi-dimensional datasets
Credit: SDSS
Mathematics - Discovering E8 Lie Group: mathematicians, 4 years and 77 hours of supercomputer time (200 billion numbers) Profound implications for physics (string theory) - Four-color theorem proof
Credit: Wikipedia
Engineering (electrical, civil, mechanical, aero & astro,…) - Calculating higher order terms implies more precision, which implies reducing weight, waste, costs in fabrication - Boeing 777 tested via computer simulation alone, not in a wind tunnel
Credit: Boeing
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12. CT for Society
Economics - Automated mechanism design underlies electronic commerce, e.g., ad placement, on-line auctions, kidney exchange - Internet marketplace requires revisiting Nash equilibria model
Social Sciences - Social networks explain phenomena such as MySpace, YouTube - Statistical machine learning is used for recommendation and reputation services, e.g., Netflix, affinity card
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13. CT for Society
Medicine - Robotic surgery - Electronic health records require privacy technologies - Scientific visualization enables virtual colonoscopy
Credit: University of Utah
Humanities - What do you do with a million books? Nat’l Endowment for the Humanities Inst of Museum and Library Services
Law - Stanford CL approaches include AI, temporal logic, state machines, process algebras, petri nets - POIROT Project on fraud investigation is creating a detailed ontology of European law - Sherlock Project on crime scene investigation
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14. CT for Society Entertainment Arts Sports
- Games
- Movies Dreamworks uses HP data center to renderShrek and Madagascar Lucas Films uses 2000-node data center to produce Pirates of the Caribbean.
Credit: Dreamworks SKG
Credit: Carnegie Mellon University
CT for Society
Sports
- Lance Armstrong’s cycling computer tracks man and machine statistics
- Synergy Sports analyzes digital videos NBA games
Credit: Wikipedia
Arts
- Art (e.g., Robotticelli) - Drama - Music - Photography
Credit: Christian Moeller
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15. Educational Implications
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16. Pre-K to Grey K-6, 7-9, 10-12 Undergraduate courses
Freshmen year
“Ways to Think Like a Computer Scientist” aka Principles of Computing
Upper-level courses
Graduate-level courses
Computational arts and sciences
E.g., entertainment technology, computational linguistics, …, computational finance, …, computational biology, computational astrophysics
Post-graduate
Executive and continuing education, senior citizens
Teachers, not just students
CT: 1.5 Years Later
Jeannette M. Wing

17. Question and Challenge to Community
What are effective ways of learning (teaching) computational thinking by (to) children?
- What concepts can students best learn when? What should we teach when? What is our analogy to numbers in K, algebra in 7, and calculus in 12?
- We uniquely also should ask how best to integrate The Computer with learning and teaching the concepts.
CT: 1.5 Years Later
Jeannette M. Wing

18. Simple Daily Examples
Looking up a name in an alphabetically sorted list
Linear: start at the top
Binary search: start in the middle
Standing in line at a bank, supermarket, customs & immigration
Performance analysis of task scheduling
Putting things in your child’s knapsack for the day
Pre-fetching and caching
Taking your kids to soccer, gymnastics, and swim practice
Traveling salesman (with more constraints)
Cooking a gourmet meal
Parallel processing: You don’t want the meat to get cold while you’re cooking the vegetables.
Cleaning out your garage
Keeping only what you need vs. throwing out stuff when you run out of space.
Storing away your child’s Lego pieces scattered on the LR floor
Using hashing (e.g., by shape, by color)
Doing laundry, getting food at a buffet
Pipelining the wash, dry, and iron stages; plates, salad, entrée, dessert stations
Even in grade school, we learn algorithms (long division, factoring, GCD, …) and abstract data types (sets, tables, …).
The Central-Queue policy is provably optimal when job size variability is
low.  WFM (in NY) goes with the bank-line scheme because (maybe) there is less variability in job size because prices are so high, people buy less. Still should have an express lane for < 8 items or less.
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19. Two and A Half Years Later
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20. External Community Response
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21. External Community … Outside of CMU Outside of Computer Science
Outside of Science and Engineering
Outside of US
Impact on research and education through NSF
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22. Research Impact
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23. “Computational Thinking,” Andrew Hebert (Director, MSR/Cambridge), p
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24. Volume 440 Number 7083 pp 383-580, March 23, 2006 CT: 1.5 Years Later
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25. CT: 1.5 Years Later
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26. Spearheaded by Alan Bundy
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27. Also, report by Conrad Taylor on my talk at
Grand Challenges in Computing Conference, British Computer Society, London, March 2008
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28. CACM Viewpoint Translated into Spanish and Chinese CT: 1.5 Years Later
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29. Reach Through NSF
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30. CDI: Cyber-Enabled Discovery and Innovation
Paradigm shift
Not just our metal tools (transistors and wires) but also our mental tools (abstractions and methods)
It’s about partnerships and transformative research.
To innovate in/innovatively use computational thinking; and
To advance more than one science/engineering discipline.
Fortuitous timing for me …
Computational Thinking for Science and Engineering
~650 Data to Knowledge, 500 Understanding Complexity, < 200 Virtual Organizations
90 PDs (~35 from CISE) and 6 (4 from CISE) admin staff across the agency helped
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Jeannette M. Wing

31. CDI Response
1800 Letters of Intent, 1300 Preliminary Proposals, 200 Final Proposals, 36 Awards
FY08: ~$50M invested by all directorates and offices
= 36
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Jeannette M. Wing

32. Range of Disciplines in CDI Awards
Aerospace engineering
Atmospheric sciences
Biochemistry
Biophysics
Chemical engineering
Communications science and engineering
Computer science
Geosciences
Linguistics
Materials engineering
Mathematics
Mechanical engineering
Molecular biology
Nanocomputing
Neuroscience
Robotics
Social sciences
Statistical physics
… advances via Computational Thinking
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Jeannette M. Wing

33. Range of Societal Issues Addressed
Cancer therapy
Climate change
Environment
Visually impaired
Water
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34. Educational Impact
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Jeannette M. Wing

35. Colleges and Universities are Revisiting Curricula
Carnegie Mellon: Tom Cortina’s
MIT: John Guttag’s 6.00 (for freshmen)
Georgia Tech:
UG: “Threads”, Mark Guzdial, “Learning Computing with Robots,” Tucker Balch and Deepak Kumar (Bryn Mawr)
Grad: Alexander Gray and Nick Feamster
Columbia: Al Aho
Princeton: PICASso, for non-CS graduate students
Harvard: Alyssa Goodman, Institute for Innovative Computing
Northwestern: Center for Connected Learning and Computer-Based Modeling …
Villanova, Haverford, Bryn Mawr, Georgetown, …
U Wisconsin-La Crosse, …
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36. CT: 1.5 Years Later
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37. © 2008 Microsoft Corporation
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© 2008 Microsoft Corporation
Jeannette M. Wing

38. Reach Through NSF
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39. CISE CPATH Broadening Participation in Computing
Revisiting undergrad curricula
Enlarge scope to include outreach to K-12
Broadening Participation in Computing
Women, underrepresented minorities, people with disabilities
Alliances and demo projects
CT: 1.5 Years Later
Jeannette M. Wing

40. CPATH Awards Specific to CT
Brown
De Paul
Georgia State
North Carolina Agricultural and Technical State University
Middle Tennessee State University
Penn State University
Towson University
University of Illinois, Urbana-Champaign
University of Nebraska
University of Texas, El Paso
Utah State
Villanova
Virginia Tech
Washington State
CT: 1.5 Years Later
Jeannette M. Wing

41. Broadening Participation
AP Revision
Academic Advisory group includes NSF, ACM, CSTA, university reps (e.g., Cortina), and high school teachers
New Image for Computing
Working with Image of Computing and WGBH (Boston)
Alliances
e.g., ARSTI (HBCU/R1 Robotics, Touretzky, et al.)
CT: 1.5 Years Later
Jeannette M. Wing

42. Beyond CISE
Challenge to Community: What is an effective way of teaching (learning) computational thinking to (by) K-12?
Computational Thinking for Children
National Academies Computer Science and Telecommunications Board (CSTB): Workshops on CT for Everyone. Collaborating with Board on Science Education.
Cyber-enabled Learning
Education and Human Resources (EHR) Directorate, Office of Cyberinfrastructure (OCI), Social, Behavioral, and Economic Sciences (SBE), and CISE
CT: 1.5 Years Later
Jeannette M. Wing

43. Other Educational and Outreach Activities
Peter Denning’s “Rebooting Computing Summit”, Jan 2009
Andy van Dam is CRA-E “Education Czar”
ACM Ed Council
CS4HS: Lenore Blum’s vision: “CS4HS in every state!”
Roadshow
Image of Computing Task Force: Jill Ross, Rick Rashid, Jim Foley
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44. Two and a Half Years Later: Research
Computing Community
NSF
CMU
Microsoft
NEH, ILMS
Computational Thinking
CDI
all sciences and engineering
Center for CT
computer science, arts, humanities, …
CT: 1.5 Years Later
Jeannette M. Wing

45. Two and a Half Years Later: Education
ACM-Ed
CRA-E
CSTA
Computing Community
NSF
Rebooting
K-12
National Academies
workshops
Computational Thinking
CSTB “CT for Everyone” Steering Committee
Marcia Linn, Berkeley
Al Aho, Columbia
Brian Blake, Georgetown
Bob Constable, Cornell
Yasmin Kafai, U Penn
Janet Kolodner, Georgia Tech
Uri Wilensky, Northwestern
Bill Wulf, UVA
CPATH
BPC AP
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Jeannette M. Wing

46. Challenges and Opportunities for Computer Science
Reality Check:
Challenges and Opportunities for Computer Science
Looking Ahead:
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Jeannette M. Wing

47. Big Data and Big Systems
Big Science
means
Big Data and Big Systems
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Jeannette M. Wing

48. LIGO and its successor, Advanced LIGO, are instruments whole sole goal is to detect gravitational waves (of cosmic origin). Einstein’s Theory of General Relativity predicted the existence of gravitational waves but we have never observed them. These instruments are made up of lots and lots of mirrors in such a way that simplistically speaking if a gravitational wave passed through it would perturb a light beam from a laser.
The international LIGO Scientific Collaboration (LSC) is a growing group of researchers, some 600 individuals at roughly 40 institutions, working to analyze the data from LIGO and other detectors.
LIGO's mission is to directly observe gravitational waves of cosmic origin. These waves were first predicted by Einstein's Theory of General Relativity in 1916, when the technology necessary for their detection did not yet exist.
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49. The program processes data from the LIGO and GEO instruments using Fast Fourier Transforms. The resulting signals are then analyzed using a method called matched filtering. This method involves the computation of hypothetical signals that might result if there were a physically plausible source of gravitational waves in the part of the sky being examined. The measured signal is then compared to the hypothetical signal. A matching signal is a candidate for further examination by more sophisticated analysis.
CT: 1.5 Years Later
Jeannette M. Wing

50. Examples
The goal is to detect neutrinos in the high energy range. The instrument is made up of an array of strings of optical sensors, each string inserted into a a big hole in the ice.
The IceCube Neutrino Detector is a neutrino telescope currently under construction at the South Pole. Like its predecessor, the Antarctic Muon And Neutrino Detector Array (AMANDA), IceCube is being constructed in deep Antarctic ice by deploying thousands of spherical optical sensors (photomultiplier tubes, or PMTs) at depths between 1,450 and 2,450 meters. The sensors are deployed on "strings" of sixty modules each, into holes in the ice melted using a hot water drill.
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51. CT: 1.5 Years Later
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52. It is estimated to generate 15 PB annually once in operation.
One hope is that the LHC will be able to observe the Higgs Boson which would then confirm predictions and fill in the gaps in the Standard Model of theoretical physics. The verification of the Higgs Boson would be a step in the search for a Grand Unified Theory (in particular the Higgs boson would help explain why gravitation is weak compared to the other three forces).
The LHC is a huge circular tunnel of superconducting magnets where you shoot two proton beams in opposite directions toward each other to cause the collision.
It is estimated to generate 15 PB annually once in operation.
The Large Hadron Collider (LHC) is a particle accelerator complex intended to collide opposing beams of 7 TeV protons. Its main purpose is to explore the validity and limitations of the standard model, the current theoretical picture for particle physics. This model is known to break down at a certain high energy level.
CT: 1.5 Years Later
Jeannette M. Wing

53. ALMA is a telescope made up of 80 antennas looking into the celestial sky, to probe the cold Universe (regions that are optically dark but shine brightly in the millimeter portion of the electromagnetic spectrum), making observations possibly only now in space. At its finest resolution, it will reach a factor of ten better than the Hubble Space Telescope.
The Atacama Large Millimeter/submillimeter Array (ALMA), will be a single research instrument composed of up to 80 high-precision antennas, located on the Chajnantor plain of the Chilean Andes in the District of San Pedro de Atacama, 5000 m above sea level. ALMA will enable transformational research into the physics of the cold Universe, regions that are optically dark but shine brightly in the millimeter portion of the electromagnetic spectrum. Providing astronomers a new window on celestial origins, ALMA will probe the first stars and galaxies, and directly image the formation of planets.
ALMA will operate at wavelengths of 0.3 to 9.6 millimeters, where the Earths atmosphere above a high, dry site is largely transparent, and will provide astronomers unprecedented sensitivity and resolution. The up to sixty-four antennas of the 12 m Array will have reconfigurable baselines ranging from 150 m to 18 km. Resolutions as fine as 0.005" will be achieved at the highest frequencies, a factor of ten better than the Hubble Space Telescope.
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54. CT: 1.5 Years Later
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55. NEON is planned to be a continental scale set of distributed sensor networks linked by advanced cyberinfrastructure. It is to study impacts of climate change, land-use change, and invasive species – on ecology.
It’s actually a configuration of a set of fixed towers of sensors and a movable suite of sensor arrays that will sweep the continent.
CT: 1.5 Years Later
Jeannette M. Wing

56. OOI is a networked infrastructure of sensors to be deployed off the coast of the Pacific Northwest (Juan de Fuca strait) to look at global, regional, coastal effects due to changes in many aspects of the ocean: physical, biology, chemical, ecological changes.
CT: 1.5 Years Later
Jeannette M. Wing

57. Research Challenges and Opportunities
CT for other sciences and engineering and beyond
It’s inevitable
They need us, they want us
It’s about abstractions and symbolic “calculations” not just number-crunching
CT: 1.5 Years Later
Jeannette M. Wing

58. Educational Challenges and Opportunities
Science, Technology, Engineering, and Mathematics (STEM) Education continues to be a huge challenge
~15,000 school districts in the US
HS science and math teachers
Public perception of STEM disciplines
CT: 1.5 Years Later
Jeannette M. Wing

59. Bigger Picture: Societal and Political Issues
Climate Change
Energy
Environment
Economics
Human Behavior
Sustainability
Healthcare
National security …
Competitiveness, Innovation, Leadership
CT: 1.5 Years Later
Jeannette M. Wing

60. Thanks for Helping to Spread the Word!
Make computational thinking commonplace!
To fellow faculty, students, researchers, administrators, teachers, parents, principals, guidance counselors, school boards, teachers’ unions, congressmen, policy makers, …
Thinking computationally not programming
Fundamental skill not rote skill
Computing not computers: pervasive computing/computers is passe: 20th Century!
“used by” ambitious. Certainly needed to function in modern society
In research: long-term; in education: nearer-term (outreach, diversity, changing society’s view of our field)
CT: 1.5 Years Later
Jeannette M. Wing

61. Thank you!

62. Credits
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Jeannette M. Wing