Updating Computer Updating Computer Science EducationScience Education

Jacques CohenJacques CohenBrandeis UniversityBrandeis UniversityWaltham, MAWaltham, MAUSAUSAJanuary 2007January 2007

TopicsTopics Preliminary remarksPreliminary remarks Present state of affairs and Present state of affairs and

concernsconcerns Objectives of this talkObjectives of this talk Trends (Trends (hardware, software, networks, hardware, software, networks,

others)others) Illustrative examplesIllustrative examples SuggestionsSuggestions

Present state of affairs and Present state of affairs and concernsconcerns

Huge increase in PC and internet usage.Huge increase in PC and internet usage.

Decreasing enrollment.Decreasing enrollment. (USA mainly)(USA mainly)

Possible ReasonsPossible Reasons Previous high school preparation Previous high school preparation Bubble burst (Bubble burst (2000) + outsourcing2000) + outsourcing Widespread usage of computers Widespread usage of computers

by lay personsby lay persons Interest in interdisciplinary Interest in interdisciplinary

topics (e.g., biology, business, topics (e.g., biology, business, economics)economics)

Public perception about: Public perception about: What is Computer Science?What is Computer Science?

The Nature of Computer The Nature of Computer ScienceScience Two main components:Two main components: Theoretical Theoretical and and ExperimentalExperimental Mathematics Mathematics and and EngineeringEngineering

What characterizes CS is the notion of What characterizes CS is the notion of Algorithms Algorithms

Emphasis on the Emphasis on the discrete discrete and and logiclogic An interdisciplinary approach with other An interdisciplinary approach with other

sciences may well revive the interest on sciences may well revive the interest on the continuous (or use of qualitative the continuous (or use of qualitative reasoning)reasoning)

Related fieldsRelated fields Sciences in general Sciences in general (scientific (scientific

computing),computing), Management, Management, Psychology Psychology (human interaction),(human interaction), Business, Business, Communications,Communications, Journalism, Journalism, Arts, etc.Arts, etc.

The role of Computer The role of Computer Science among other Science among other sciencessciences((How we are perceived by the other sciencesHow we are perceived by the other sciences))

In physics, chemistry, biology, In physics, chemistry, biology, naturenature is the ultimate umpire.is the ultimate umpire.

DiscoveryDiscovery is paramount is paramount In math and engineering: In math and engineering: aestheticsaesthetics, ,

ease of use, acceptance, ease of use, acceptance, permanence,permanence, play key roles play key roles

Uneasy dialogue with Uneasy dialogue with biologistsbiologists It is not unusual to hear from a It is not unusual to hear from a

physicist, chemist or biologist:physicist, chemist or biologist: ““If computer scientists do not get If computer scientists do not get

involved in our field, we will do it involved in our field, we will do it ourselves!!”ourselves!!”

It looks very likely that the It looks very likely that the biological sciences (including, of biological sciences (including, of course, neuroscience) will course, neuroscience) will dominate the 21st centurydominate the 21st century

Differences in approachesDifferences in approaches Most scientific and creative discoveries Most scientific and creative discoveries

proceed in a proceed in a bottom-upbottom-up manner manner

Computer scientists are taught to Computer scientists are taught to emphasize emphasize top-downtop-down approaches approaches

Polya’s Polya’s ““How to solve it”How to solve it” often mentions often mentions First specialize then generalizeFirst specialize then generalize..

Hacking is beautiful Hacking is beautiful (mostly bottom-up)(mostly bottom-up)

ObjectivesObjectives Provide a bird’s eye view of what is Provide a bird’s eye view of what is

happening in CS educationhappening in CS education (USA) and (USA) and attempt to make recommendations attempt to make recommendations about possible directions. Hopefully, about possible directions. Hopefully, some of it would be applicable to some of it would be applicable to European universities.European universities.

PremisePremise Changes ought to be gradual and Changes ought to be gradual and

depend on resources and time depend on resources and time constraints constraints

First we have to observe current First we have to observe current trendstrendsGGenerality, Storage, Speed, Networks,enerality, Storage, Speed, Networks, oothers.thers. Trying to make sense of present Trying to make sense of present

directions.directions. Difficult and risky to foresee future, Difficult and risky to foresee future,

e.g., PC (windows, mouse), internet, e.g., PC (windows, mouse), internet, parallelismparallelism

Topics influencing computer Topics influencing computer science education.science education.

Trends in hardware, software, Trends in hardware, software, networks.networks.

Huge volume of data Huge volume of data (terabytes and petabytes(terabytes and petabytes))

Statistical nature of data Statistical nature of data Clustering, classificationClustering, classification Probability and Statistics Probability and Statistics

become increasingly become increasingly importantimportant

Trend towards generalityTrend towards generality Need to know more about what is Need to know more about what is

going on in related topicsgoing on in related topics A few examples:A few examples: Robotics and mechanical engineeringRobotics and mechanical engineering Hardware, electrical engineering, Hardware, electrical engineering,

material science, nanotechnologymaterial science, nanotechnology Multi-field visualization Multi-field visualization (e.g., medicine)(e.g., medicine) Biophysics and bioinformaticsBiophysics and bioinformatics

Nature of data structuresNature of data structures Sequences (strings), streamsSequences (strings), streams Trees, DAGs, and GraphsTrees, DAGs, and Graphs 3D structures3D structures Emphasis in discrete structuresEmphasis in discrete structures Neglect of the continuous Neglect of the continuous

should be corrected (should be corrected (e.g., use of e.g., use of MatLabMatLab))

Trends on data growthTrends on data growthHow Much Information Is There In the How Much Information Is There In the World?World? The The 20-terabyte size20-terabyte size of the of the

Library of Congress derived by Library of Congress derived by assuming that LC has 20 million assuming that LC has 20 million books and each requires 1 MB. Of books and each requires 1 MB. Of course, LC has much other stuff course, LC has much other stuff besides printed text, and this other besides printed text, and this other stuff would take much more space.stuff would take much more space.

From Lesk From Lesk http://www.lesk.com/mlesk/ksg97/ksg.htmlhttp://www.lesk.com/mlesk/ksg97/ksg.html

Library of Congress data Library of Congress data (cont)(cont)1. 1. Thirteen million photographsThirteen million photographs, even if , even if

compressed to a 1 MB JPG each, would be compressed to a 1 MB JPG each, would be 13 13 terabytes.terabytes.

2. The 2. The 4 million maps4 million maps in the Geography Division in the Geography Division might scan to might scan to 200 TB200 TB..

3. LC has over 3. LC has over five hundred thousand movies;five hundred thousand movies; at at 1 GB each they would be 1 GB each they would be 500 terabytes500 terabytes (most (most are not full-length color features).are not full-length color features).

4. Bulkiest might be the 4. Bulkiest might be the 3.5 million sound 3.5 million sound recordingsrecordings, which at one audio CD each, would , which at one audio CD each, would be almost be almost 2,000 TB2,000 TB..

This makes the total size of the Library perhaps This makes the total size of the Library perhaps about about 3 petabytes (3,000 terabytes3 petabytes (3,000 terabytes).).

How Much Information Is There In the How Much Information Is There In the World?World?

Lesk’s ConclusionsLesk’s Conclusions There will be enough disk space There will be enough disk space

and tape storage in the world to and tape storage in the world to store everything people store everything people write, write, say, performsay, perform or or photographphotograph.. For For writingwriting this is true already; for the this is true already; for the others it is only a year or two others it is only a year or two away.away.

Lesk’s Conclusions (cont)Lesk’s Conclusions (cont) The challenge for librarians and The challenge for librarians and

computer scientists is to let us computer scientists is to let us find the find the informationinformation we want in other people's we want in other people's work; and the challenge for the work; and the challenge for the lawyers and economists is lawyers and economists is to arrange to arrange the payment structuresthe payment structures so so

that we are encouraged to use the that we are encouraged to use the work of others rather than re-create it.work of others rather than re-create it.

The huge volume of data The huge volume of data impliesimplies:: LinearityLinearity of algorithms is a of algorithms is a mustmust Emphasis in Emphasis in pattern matchingpattern matching Increased Increased preprocessingpreprocessing Different levels of memory transfer Different levels of memory transfer

rates rates Algorithmic Algorithmic incrementalityincrementality (avoid redoing (avoid redoing

tasks)tasks) Need of Need of approximateapproximate algorithms algorithms

((optimizationoptimization)) Distributed computingDistributed computing Centralized parallelism Centralized parallelism (Blue Gene, Argonne)(Blue Gene, Argonne)

The importance of pattern The importance of pattern matching (searches) in large matching (searches) in large number of itemsnumber of itemsPattern matching has to be “tolerant” (approximate)Pattern matching has to be “tolerant” (approximate)Find closest matches (dynamic programming, Find closest matches (dynamic programming,

optimization)optimization)

SequencesSequences PicturesPictures 3D structures (e.g. proteins) 3D structures (e.g. proteins) SoundSound PhotosPhotos VideoVideo

Trends in computer cycles Trends in computer cycles (speed)(speed) Moore’s law appears to be applicable until at Moore’s law appears to be applicable until at

least 2020least 2020

Use of supercomputers Use of supercomputers (2006)(2006) Researchers at Los Alamos National Researchers at Los Alamos National

Laboratory have set a new world's record Laboratory have set a new world's record by performing the by performing the first million-atom first million-atom computer simulation in biologycomputer simulation in biology. Using . Using the "Q Machine" supercomputer, Los the "Q Machine" supercomputer, Los Alamos computer scientists have created Alamos computer scientists have created a molecular simulation of the cell's a molecular simulation of the cell's protein-making structure, the protein-making structure, the ribosomeribosome. . The project, simulating The project, simulating 2.64 million 2.64 million atoms in motionatoms in motion, is more than six , is more than six times larger than any biological times larger than any biological simulations performed to date. simulations performed to date.

Graphical visualization of the Graphical visualization of the simulation of a Ribosome at simulation of a Ribosome at workwork

Network transmission Network transmission speed (Lambda Rail Net)speed (Lambda Rail Net) USA backboneUSA backbone

Trends in Transmission SpeedTrends in Transmission Speed

The High Energy Physics The High Energy Physics team's demonstration team's demonstration achieved a peak throughput of achieved a peak throughput of 151 151 GbpsGbps and an official mark and an official mark of of 131.6131.6 Gbps Gbps beating their beating their previous mark for peak previous mark for peak throughput of throughput of 101101 Gbps Gbps by 50 by 50 percent. percent.

Trends in Transmission Trends in Transmission Speed IISpeed II The new record data transfer The new record data transfer

speed is also equivalent to speed is also equivalent to serving 10,000 MPEG2 HDTV serving 10,000 MPEG2 HDTV movies simultaneously in real movies simultaneously in real time, or time, or transmitting all of transmitting all of the printed content of the the printed content of the Library of Congress in 10 Library of Congress in 10 minutes.minutes.

Trend in LanguagesTrend in Languages Importance of scripting and Importance of scripting and

string processingstring processing XML, Java C++, Trend towards XML, Java C++, Trend towards

Python, Matlab, MathematicaPython, Matlab, Mathematica No ideal languages No ideal languages No agreement of what the first No agreement of what the first

language ought to belanguage ought to be

A recently proposed A recently proposed language (language (Fortress 2006Fortress 2006))

From Guy Steel, The Fortress Programming Language, Sun Micro-From Guy Steel, The Fortress Programming Language, Sun Micro-SystemsSystemshttp://iic.harvard.edu/documents/steeleLecture2006public.pdfhttp://iic.harvard.edu/documents/steeleLecture2006public.pdf

Fortress Language Fortress Language (Sun, Guy Steele)(Sun, Guy Steele)

Meta-level approach to Meta-level approach to teachingteaching Learn 2 or 3 languages and assume that Learn 2 or 3 languages and assume that

expertise in other languages can be expertise in other languages can be acquired on the fly.acquired on the fly.

Hopefully, the same will occur in learning a Hopefully, the same will occur in learning a topic in depth. Once in-depth research is topic in depth. Once in-depth research is taught using a particular area it can be taught using a particular area it can be extrapolated to other areas.extrapolated to other areas.

Increasing usage of Increasing usage of cannedcanned programs or programs or data banks Typical examples: data banks Typical examples: GraphViz, GraphViz, WordNet WordNet

Trends in Algorithmic Trends in Algorithmic ComplexityComplexity Overcoming the scare of NP Overcoming the scare of NP

problemsproblems ((it happened before with it happened before with

undecidabilityundecidability)) 3-SAT lessons 3-SAT lessons Mapping polynomial problems within Mapping polynomial problems within

NPNP Optimization, approximate or Optimization, approximate or

random algorithmsrandom algorithms

Three ExamplesThree Examples Example IExample I The lessons of BLAST The lessons of BLAST

(preprocessing, incrementability, (preprocessing, incrementability, approximationapproximation))

Example IIExample II The importance of analyzing The importance of analyzing very large networks.very large networks.

(probability, sensors, sociological implications)(probability, sensors, sociological implications)

Example IIIExample III Time Series. Time Series. (data mining, pattern searches, classification)(data mining, pattern searches, classification)

Example IExample I (History of BLAST)(History of BLAST)sequence alignmentsequence alignment

Biologists matched sequences of Biologists matched sequences of nucleotides or aminoacids nucleotides or aminoacids empirically using Dot Matrices empirically using Dot Matrices

Dot matricesDot matrices

No exact matchingNo exact matching

Alignment with GapsAlignment with Gaps

Dynamic Programming Dynamic Programming ApproachApproach

Dynamic Programming Dynamic Programming complexity O(ncomplexity O(n22))

Two solutions with gapsTwo solutions with gapsComplexity can be exponential Complexity can be exponential for determining all solutionsfor determining all solutions

The BLAST approachThe BLAST approachcomplexity is almost complexity is almost

linearlinearEquivalent Dot Matrices would have Equivalent Dot Matrices would have

the size the size 3 billion columns3 billion columns ((human genomehuman genome) )

andand Z rowsZ rows where Z is the size of the where Z is the size of the

sequence being matched against a sequence being matched against a genome (genome (possibly tens of thousandspossibly tens of thousands))

BLAST TricksBLAST Tricks

PreprocessingPreprocessing Compile the locations in a genome Compile the locations in a genome

containing all possible “seeds” containing all possible “seeds” (combinations of 6 nucleotides or (combinations of 6 nucleotides or aminoacids) aminoacids)

Hacking Hacking Follow diagonals as much as possible Follow diagonals as much as possible

(Blast strategy)(Blast strategy) Use dynamic programming as a last Use dynamic programming as a last

resortresort

Lots of approximations but a Lots of approximations but a very successful outcomevery successful outcome No multiple solutionsNo multiple solutions BLAST may not find best matchesBLAST may not find best matches The notion of The notion of p-valuesp-values becomes very becomes very

important (probability of matches in important (probability of matches in random sequences)random sequences)

Tuning of the BLAST algorithm Tuning of the BLAST algorithm parametersparameters

Mixture of Mixture of hackinghacking and and theorytheory Advantage: satisfies Advantage: satisfies incrementabilityincrementability

Example II Example II (Networks and Sociology)(Networks and Sociology)

Money travels (bills)Money travels (bills)

Probabilities Probabilities P(time,distance)P(time,distance)

Money travelsMoney travels The entire process could be The entire process could be

implemented using sensors.implemented using sensors. Mimics spread of disease.Mimics spread of disease. The impact of computing will The impact of computing will

go deeper into the sciences go deeper into the sciences and spread more into the and spread more into the social sciences (Jon Kleinberg, social sciences (Jon Kleinberg, 2006)2006)

Example III (Time Series)Example III (Time Series) Illustrates data mining and Illustrates data mining and

how much CS can help other how much CS can help other sciencessciences

Slides from Slides from Dr Eamonn KeoghDr Eamonn KeoghUniversity of California. University of California.

Riverside,CARiverside,CA

Examples of time Examples of time seriesseries

Time Series (cont 1)Time Series (cont 1)

Time Series (cont 2)Time Series (cont 2)

Time Series (cont 3)Time Series (cont 3)

Time Series (cont 4)Time Series (cont 4)

Time Series (cont 5)Time Series (cont 5)

Using Logic Programming inUsing Logic Programming in Multivariate Time Series (Sleep Multivariate Time Series (Sleep Apnea)Apnea) from from G GuimarG Guimarãães and L. Moniz Pereiraes and L. Moniz Pereira

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Back to curricula Back to curricula recommendationsrecommendationsPresent status (USA) Present status (USA) and suggested and suggested changes changes

Current recommended Current recommended curricula curricula ACM, SIGCSE 2001 (USA) ACM, SIGCSE 2001 (USA)

1. Discrete Structures (43 core hours)1. Discrete Structures (43 core hours) 2. Programming Fundamentals (54 core hours)2. Programming Fundamentals (54 core hours) 3. Algorithms and Complexity (31 core hours)3. Algorithms and Complexity (31 core hours) 4. Programming Languages (6 core hours)4. Programming Languages (6 core hours) 5. Architecture and Organization (36 core hours)5. Architecture and Organization (36 core hours) 6. Operating Systems (18 core hours)6. Operating Systems (18 core hours) 7. Net-Centric Computing (15 core hours)7. Net-Centric Computing (15 core hours) 8. Human-Computer Interaction (6 core hours) 8. Human-Computer Interaction (6 core hours) 9. Graphics and Visual Computing (5 core hours)9. Graphics and Visual Computing (5 core hours) 10. Intelligent Systems (10 core hours)10. Intelligent Systems (10 core hours) 11. Information Management (10 core hours)11. Information Management (10 core hours) 12. Software Engineering (30 core hours)12. Software Engineering (30 core hours) 13. Social and Professional Issues (16 core hours)13. Social and Professional Issues (16 core hours) 14. Computational Science (no core hours)14. Computational Science (no core hours)From From Domik G.: Glimpses into the Future of Computer Science Domik G.: Glimpses into the Future of Computer Science

Education University of Paderhor, GermanyEducation University of Paderhor, Germany

Changing CurriculaChanging Curricula

Two extremes Two extremes Increased GeneralityIncreased Generality and and Limited DepthLimited Depth

Limited GeneralityLimited Generality and and Increased Increased

DepthDepth

The two extremes in graphical The two extremes in graphical formform

Breadth(generality)

D

Depth

The MIT pilot program for The MIT pilot program for freshmenfreshmen At MIT there is a unified EECS At MIT there is a unified EECS

departmentdepartment

Two choices for the first year course:Two choices for the first year course: Robotics using probabilistic Robotics using probabilistic

Bayesian approachesBayesian approaches (CS) (CS)

Study of cell phones inside outStudy of cell phones inside out (EE) (EE)

Concrete suggestions IConcrete suggestions I Teaching is inextricably linked to researchTeaching is inextricably linked to research.. TimeTime and and resourcesresources govern curriculum changes. govern curriculum changes. GradualGradual changes are essential.changes are essential. Avoid overlapAvoid overlap of material among different of material among different

required courses.required courses. If possible introduce an elective course onIf possible introduce an elective course on Current trends in computer science.Current trends in computer science. Deal with Deal with massive datamassive data even in intro courses. even in intro courses.

Concrete suggestions IIConcrete suggestions II

When teaching algorithms stress When teaching algorithms stress the potential of: the potential of:

Preprocessing Preprocessing Incrementality Incrementality Parallelization Parallelization ApproximationsApproximations Taking advantage of Taking advantage of sparsenesssparseness

Concrete suggestions IIIConcrete suggestions III Emphasize probability and Emphasize probability and

statistics statistics Bayesian approachesBayesian approaches Hidden Markov ModelsHidden Markov Models Random algorithms Random algorithms Clustering and classificationClustering and classification Machine learning and Data Machine learning and Data

MiningMining

Finally, …Finally, …

Encourage Encourage interdisciplinary work.interdisciplinary work.

It will inspire new directions It will inspire new directions in computer science.in computer science.

Thank you!!Thank you!!

Future of Computer Intensive Future of Computer Intensive Science in the U.S. Science in the U.S. (Daniel Reed 2006)(Daniel Reed 2006)

Ten years – a geological epoch on the computing time scale. Ten years – a geological epoch on the computing time scale. Looking back, a decade brought the web and Looking back, a decade brought the web and consumer email, consumer email, digital cameras and music, broadband networking, multifunction digital cameras and music, broadband networking, multifunction cell phones, WiFi, HDTV, telematics, multiplayer games, cell phones, WiFi, HDTV, telematics, multiplayer games, electronic commerce and computational scienceelectronic commerce and computational science. .

It also brought It also brought spam, phishing, identity theft, software insecurity, spam, phishing, identity theft, software insecurity, outsourcing and globalization, information warfare and blurred outsourcing and globalization, information warfare and blurred work-life boundarieswork-life boundaries. What will a decade of technology advances . What will a decade of technology advances bring in communications and collaboration, sensors and bring in communications and collaboration, sensors and knowledge management, modeling and discovery, electronic knowledge management, modeling and discovery, electronic commerce and digital entertainment, critical infrastructure commerce and digital entertainment, critical infrastructure management and security? management and security?

What will it mean for research and education?What will it mean for research and education? Daniel A. Reed is the director of the Renaissance Computing Institute. He also is Chancellor's Daniel A. Reed is the director of the Renaissance Computing Institute. He also is Chancellor's

Eminent Professor and Vice-Chancellor for Information Technology at the University of North Eminent Professor and Vice-Chancellor for Information Technology at the University of North Carolina at Chapel Hill.Carolina at Chapel Hill.

Cyberinfrastructure and Economic Cyberinfrastructure and Economic Curvature Creating Curvature in a Curvature Creating Curvature in a Flat World Flat World (Singtae Kim, Purdue, 2006)(Singtae Kim, Purdue, 2006)

Cyberinfrastructure is central to Cyberinfrastructure is central to scientificscientific advancement in advancement in the modern, data-intensive research environment. For the modern, data-intensive research environment. For example, the recent revolution in the life sciences, including example, the recent revolution in the life sciences, including the seminal achievement of sequencing the human genome the seminal achievement of sequencing the human genome on an accelerated time frame, was made possible by parallel on an accelerated time frame, was made possible by parallel advances in cyberinfrastructure for research in this data-advances in cyberinfrastructure for research in this data-intensive field. intensive field.

But beyond the enablement of basic research, But beyond the enablement of basic research, cyberinfrastructure is a driver for global economic growth cyberinfrastructure is a driver for global economic growth despite the disruptive 'flattening' effect of IT in the despite the disruptive 'flattening' effect of IT in the developed economies. But even at the regional level, developed economies. But even at the regional level, visionary cyber investments to create smart infrastructures visionary cyber investments to create smart infrastructures will induce 'economic curvature' a gravitational pull to will induce 'economic curvature' a gravitational pull to overcome the dispersive effects of the 'flat' world and the overcome the dispersive effects of the 'flat' world and the consequential acceleration in economic growth.consequential acceleration in economic growth.

Miscellaneous IMiscellaneous I

ClaytronicsClaytronics Game theory (economics - psychology)Game theory (economics - psychology) Other examples in bioinformatics Other examples in bioinformatics Beautiful interaction between sequence Beautiful interaction between sequence

(strings) and structures(strings) and structures Reverse engineeringReverse engineering In biology Geography and Phenotype In biology Geography and Phenotype

(external structural appearance) are of (external structural appearance) are of paramount importanceparamount importance

Systems Biology Systems Biology

Miscellaneous IIMiscellaneous II Cross word puzzle using GoogleCross word puzzle using Google Skiena and statistical NLPSkiena and statistical NLP